research topics in computer science for undergraduate students

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Suggested Undergraduate Research Topics

How to Contact Faculty for IW/Thesis Advising

Send the professor an e-mail. When you write a professor, be clear that you want a meeting regarding a senior thesis or one-on-one IW project, and briefly describe the topic or idea that you want to work on. Check the faculty listing for email addresses.

Parastoo Abtahi, Room 419

Available for single-semester IW and senior thesis advising, 2024-2025

• Research Areas: Human-Computer Interaction (HCI), Augmented Reality (AR), and Spatial Computing
• Input techniques for on-the-go interaction (e.g., eye-gaze, microgestures, voice) with a focus on uncertainty, disambiguation, and privacy.
• Minimal and timely multisensory output (e.g., spatial audio, haptics) that enables users to attend to their physical environment and the people around them, instead of a 2D screen.
• Interaction with intelligent systems (e.g., IoT, robots) situated in physical spaces with a focus on updating users’ mental model despite the complexity and dynamicity of these systems.

Ryan Adams, Room 411

Research areas:

• Machine learning driven design
• Generative models for structured discrete objects
• Approximate inference in probabilistic models
• Accelerating solutions to partial differential equations
• Innovative uses of automatic differentiation
• Modeling and optimizing 3d printing and CNC machining

Andrew Appel, Room 209

Available for Fall 2024 IW advising, only

• Research Areas: Formal methods, programming languages, compilers, computer security.
• Software verification (for which taking COS 326 / COS 510 is helpful preparation)
• Game theory of poker or other games (for which COS 217 / 226 are helpful)
• Computer game-playing programs (for which COS 217 / 226)
•  Risk-limiting audits of elections (for which ORF 245 or other knowledge of probability is useful)

Sanjeev Arora, Room 407

• Theoretical machine learning, deep learning and its analysis, natural language processing. My advisees would typically have taken a course in algorithms (COS423 or COS 521 or equivalent) and a course in machine learning.
• Show that finding approximate solutions to NP-complete problems is also NP-complete (i.e., come up with NP-completeness reductions a la COS 487). 
• Experimental Algorithms: Implementing and Evaluating Algorithms using existing software packages. 
• Studying/designing provable algorithms for machine learning and implementions using packages like scipy and MATLAB, including applications in Natural language processing and deep learning.
• Any topic in theoretical computer science.

David August, Room 221

Not available for IW or thesis advising, 2024-2025

• Research Areas: Computer Architecture, Compilers, Parallelism
• Containment-based approaches to security:  We have designed and tested a simple hardware+software containment mechanism that stops incorrect communication resulting from faults, bugs, or exploits from leaving the system.   Let's explore ways to use containment to solve real problems.  Expect to work with corporate security and technology decision-makers.
• Parallelism: Studies show much more parallelism than is currently realized in compilers and architectures.  Let's find ways to realize this parallelism.
• Any other interesting topic in computer architecture or compilers. 

Mark Braverman, 194 Nassau St., Room 231

• Research Areas: computational complexity, algorithms, applied probability, computability over the real numbers, game theory and mechanism design, information theory.
• Topics in computational and communication complexity.
• Applications of information theory in complexity theory.
• Algorithms for problems under real-life assumptions.
• Game theory, network effects
• Mechanism design (could be on a problem proposed by the student)

Sebastian Caldas, 221 Nassau Street, Room 105

• Research Areas: collaborative learning, machine learning for healthcare. Typically, I will work with students that have taken COS324.
• Methods for collaborative and continual learning.
• Machine learning for healthcare applications.

Bernard Chazelle, 194 Nassau St., Room 301

• Research Areas: Natural Algorithms, Computational Geometry, Sublinear Algorithms. 
• Natural algorithms (flocking, swarming, social networks, etc).
• Sublinear algorithms
• Self-improving algorithms
• Markov data structures

Danqi Chen, Room 412

• My advisees would be expected to have taken a course in machine learning and ideally have taken COS484 or an NLP graduate seminar.
• Representation learning for text and knowledge bases
• Pre-training and transfer learning
• Question answering and reading comprehension
• Information extraction
• Text summarization
• Any other interesting topics related to natural language understanding/generation

Marcel Dall'Agnol, Corwin 034

• Research Areas: Theoretical computer science. (Specifically, quantum computation, sublinear algorithms, complexity theory, interactive proofs and cryptography)
• Research Areas: Machine learning

Jia Deng, Room 423

•  Research Areas: Computer Vision, Machine Learning.
• Object recognition and action recognition
• Deep Learning, autoML, meta-learning
• Geometric reasoning, logical reasoning

Adji Bousso Dieng, Room 406

• Research areas: Vertaix is a research lab at Princeton University led by Professor Adji Bousso Dieng. We work at the intersection of artificial intelligence (AI) and the natural sciences. The models and algorithms we develop are motivated by problems in those domains and contribute to advancing methodological research in AI. We leverage tools in statistical machine learning and deep learning in developing methods for learning with the data, of various modalities, arising from the natural sciences.

Robert Dondero, Corwin Hall, Room 038

• Research Areas:  Software engineering; software engineering education.
• Develop or evaluate tools to facilitate student learning in undergraduate computer science courses at Princeton, and beyond.
• In particular, can code critiquing tools help students learn about software quality?

Zeev Dvir, 194 Nassau St., Room 250

• Research Areas: computational complexity, pseudo-randomness, coding theory and discrete mathematics.
• Independent Research: I have various research problems related to Pseudorandomness, Coding theory, Complexity and Discrete mathematics - all of which require strong mathematical background. A project could also be based on writing a survey paper describing results from a few theory papers revolving around some particular subject.

Benjamin Eysenbach, Room 416

• Research areas: reinforcement learning, machine learning. My advisees would typically have taken COS324.
• Using RL algorithms to applications in science and engineering.
• Emergent behavior of RL algorithms on high-fidelity robotic simulators.
• Studying how architectures and representations can facilitate generalization.

Christiane Fellbaum, 1-S-14 Green

• Research Areas: theoretical and computational linguistics, word sense disambiguation, lexical resource construction, English and multilingual WordNet(s), ontology
• Anything having to do with natural language--come and see me with/for ideas suitable to your background and interests. Some topics students have worked on in the past:
• Developing parsers, part-of-speech taggers, morphological analyzers for underrepresented languages (you don't have to know the language to develop such tools!)
• Quantitative approaches to theoretical linguistics questions
• Extensions and interfaces for WordNet (English and WN in other languages),
• Applications of WordNet(s), including:
• Foreign language tutoring systems,
• Spelling correction software,
• Word-finding/suggestion software for ordinary users and people with memory problems,
• Machine Translation 
• Sentiment and Opinion detection
• Automatic reasoning and inferencing
• Collaboration with professors in the social sciences and humanities ("Digital Humanities")

Adam Finkelstein, Room 424 

• Research Areas: computer graphics, audio.

Robert S. Fish, Corwin Hall, Room 037

• Networking and telecommunications
• Learning, perception, and intelligence, artificial and otherwise;
• Human-computer interaction and computer-supported cooperative work
• Online education, especially in Computer Science Education
• Topics in research and development innovation methodologies including standards, open-source, and entrepreneurship
• Distributed autonomous organizations and related blockchain technologies

Michael Freedman, Room 308 

• Research Areas: Distributed systems, security, networking
• Projects related to streaming data analysis, datacenter systems and networks, untrusted cloud storage and applications. Please see my group website at http://sns.cs.princeton.edu/ for current research projects.

Ruth Fong, Room 032

• Research Areas: computer vision, machine learning, deep learning, interpretability, explainable AI, fairness and bias in AI
• Develop a technique for understanding AI models
• Design a AI model that is interpretable by design
• Build a paradigm for detecting and/or correcting failure points in an AI model
• Analyze an existing AI model and/or dataset to better understand its failure points
• Build a computer vision system for another domain (e.g., medical imaging, satellite data, etc.)
• Develop a software package for explainable AI
• Adapt explainable AI research to a consumer-facing problem

Note: I am happy to advise any project if there's a sufficient overlap in interest and/or expertise; please reach out via email to chat about project ideas.

Tom Griffiths, Room 405

Available for Fall 2024 single-semester IW advising, only

Research areas: computational cognitive science, computational social science, machine learning and artificial intelligence

Note: I am open to projects that apply ideas from computer science to understanding aspects of human cognition in a wide range of areas, from decision-making to cultural evolution and everything in between. For example, we have current projects analyzing chess game data and magic tricks, both of which give us clues about how human minds work. Students who have expertise or access to data related to games, magic, strategic sports like fencing, or other quantifiable domains of human behavior feel free to get in touch.

Aarti Gupta, Room 220

• Research Areas: Formal methods, program analysis, logic decision procedures
• Finding bugs in open source software using automatic verification tools
• Software verification (program analysis, model checking, test generation)
• Decision procedures for logical reasoning (SAT solvers, SMT solvers)

Elad Hazan, Room 409  

• Research interests: machine learning methods and algorithms, efficient methods for mathematical optimization, regret minimization in games, reinforcement learning, control theory and practice
• Machine learning, efficient methods for mathematical optimization, statistical and computational learning theory, regret minimization in games.
• Implementation and algorithm engineering for control, reinforcement learning and robotics
• Implementation and algorithm engineering for time series prediction

Felix Heide, Room 410

• Research Areas: Computational Imaging, Computer Vision, Machine Learning (focus on Optimization and Approximate Inference).
• Optical Neural Networks
• Hardware-in-the-loop Holography
• Zero-shot and Simulation-only Learning
• Object recognition in extreme conditions
• 3D Scene Representations for View Generation and Inverse Problems
• Long-range Imaging in Scattering Media
• Hardware-in-the-loop Illumination and Sensor Optimization
• Inverse Lidar Design
• Phase Retrieval Algorithms
• Proximal Algorithms for Learning and Inference
• Domain-Specific Language for Optics Design

Peter Henderson , 302 Sherrerd Hall

• Research Areas: Machine learning, law, and policy

Kyle Jamieson, Room 306

• Research areas: Wireless and mobile networking; indoor radar and indoor localization; Internet of Things
• See other topics on my independent work  ideas page  (campus IP and CS dept. login req'd)

Alan Kaplan, 221 Nassau Street, Room 105

Research Areas:

• Random apps of kindness - mobile application/technology frameworks used to help individuals or communities; topic areas include, but are not limited to: first response, accessibility, environment, sustainability, social activism, civic computing, tele-health, remote learning, crowdsourcing, etc.
• Tools automating programming language interoperability - Java/C++, React Native/Java, etc.
• Software visualization tools for education
• Connected consumer devices, applications and protocols

Brian Kernighan, Room 311

• Research Areas: application-specific languages, document preparation, user interfaces, software tools, programming methodology
• Application-oriented languages, scripting languages.
• Tools; user interfaces
• Digital humanities

Zachary Kincaid, Room 219

• Research areas: programming languages, program analysis, program verification, automated reasoning
• Independent Research Topics:
• Develop a practical algorithm for an intractable problem (e.g., by developing practical search heuristics, or by reducing to, or by identifying a tractable sub-problem, ...).
• Design a domain-specific programming language, or prototype a new feature for an existing language.
• Any interesting project related to programming languages or logic.

Gillat Kol, Room 316

Aleksandra korolova, 309 sherrerd hall.

• Research areas: Societal impacts of algorithms and AI; privacy; fair and privacy-preserving machine learning; algorithm auditing.

Advisees typically have taken one or more of COS 226, COS 324, COS 423, COS 424 or COS 445.

Pravesh Kothari, Room 320

• Research areas: Theory

Amit Levy, Room 307

• Research Areas: Operating Systems, Distributed Systems, Embedded Systems, Internet of Things
• Distributed hardware testing infrastructure
• Second factor security tokens
• Low-power wireless network protocol implementation
• USB device driver implementation

Kai Li, Room 321

• Research Areas: Distributed systems; storage systems; content-based search and data analysis of large datasets.
• Fast communication mechanisms for heterogeneous clusters.
• Approximate nearest-neighbor search for high dimensional data.
• Data analysis and prediction of in-patient medical data.
• Optimized implementation of classification algorithms on manycore processors.

Xiaoyan Li, 221 Nassau Street, Room 104

• Research areas: Information retrieval, novelty detection, question answering, AI, machine learning and data analysis.
• Explore new statistical retrieval models for document retrieval and question answering.
• Apply AI in various fields.
• Apply supervised or unsupervised learning in health, education, finance, and social networks, etc.
• Any interesting project related to AI, machine learning, and data analysis.

Lydia Liu, Room 414

• Research Areas: Economics and computation, marchine learning, and public law & policy.

Wyatt Lloyd, Room 323

• Research areas: Distributed Systems
• Caching algorithms and implementations
• Storage systems
• Distributed transaction algorithms and implementations

Alex Lombardi , Room 312

• Research Areas: Theory

Margaret Martonosi, Room 208

• Quantum Computing research, particularly related to architecture and compiler issues for QC.
• Computer architectures specialized for modern workloads (e.g., graph analytics, machine learning algorithms, mobile applications
• Investigating security and privacy vulnerabilities in computer systems, particularly IoT devices.
• Other topics in computer architecture or mobile / IoT systems also possible.

Jonathan Mayer, Sherrerd Hall, Room 307 

Available for Spring 2025 single-semester IW, only

• Research areas: Technology law and policy, with emphasis on national security, criminal procedure, consumer privacy, network management, and online speech.
• Assessing the effects of government policies, both in the public and private sectors.
• Collecting new data that relates to government decision making, including surveying current business practices and studying user behavior.
• Developing new tools to improve government processes and offer policy alternatives.

Mae Milano, Room 307

• Local-first / peer-to-peer systems
• Wide-ares storage systems
• Consistency and protocol design
• Type-safe concurrency
• Language design
• Gradual typing
• Domain-specific languages
• Languages for distributed systems

Andrés Monroy-Hernández, Room 405

• Research Areas: Human-Computer Interaction, Social Computing, Public-Interest Technology, Augmented Reality, Urban Computing
• Research interests:developing public-interest socio-technical systems.  We are currently creating alternatives to gig work platforms that are more equitable for all stakeholders. For instance, we are investigating the socio-technical affordances necessary to support a co-op food delivery network owned and managed by workers and restaurants. We are exploring novel system designs that support self-governance, decentralized/federated models, community-centered data ownership, and portable reputation systems.  We have opportunities for students interested in human-centered computing, UI/UX design, full-stack software development, and qualitative/quantitative user research.
• Beyond our core projects, we are open to working on research projects that explore the use of emerging technologies, such as AR, wearables, NFTs, and DAOs, for creative and out-of-the-box applications.

Christopher Moretti, Corwin Hall, Room 036

• Research areas: Distributed systems, high-throughput computing, computer science/engineering education
• Expansion, improvement, and evaluation of open-source distributed computing software.
• Applications of distributed computing for "big science" (e.g. biometrics, data mining, bioinformatics)
• Software and best practices for computer science education and study, especially Princeton's 126/217/226 sequence or MOOCs development
• Sports analytics and/or crowd-sourced computing

Radhika Nagpal, F316 Engineering Quadrangle

• Research areas: control, robotics and dynamical systems

Karthik Narasimhan, Room 422

• Research areas: Natural Language Processing, Reinforcement Learning
• Autonomous agents for text-based games ( https://www.microsoft.com/en-us/research/project/textworld/ )
• Transfer learning/generalization in NLP
• Techniques for generating natural language
• Model-based reinforcement learning

Arvind Narayanan, 308 Sherrerd Hall 

Research Areas: fair machine learning (and AI ethics more broadly), the social impact of algorithmic systems, tech policy

Pedro Paredes, Corwin Hall, Room 041

My primary research work is in Theoretical Computer Science.

 * Research Interest: Spectral Graph theory, Pseudorandomness, Complexity theory, Coding Theory, Quantum Information Theory, Combinatorics.

The IW projects I am interested in advising can be divided into three categories:

 1. Theoretical research

I am open to advise work on research projects in any topic in one of my research areas of interest. A project could also be based on writing a survey given results from a few papers. Students should have a solid background in math (e.g., elementary combinatorics, graph theory, discrete probability, basic algebra/calculus) and theoretical computer science (226 and 240 material, like big-O/Omega/Theta, basic complexity theory, basic fundamental algorithms). Mathematical maturity is a must.

A (non exhaustive) list of topics of projects I'm interested in:   * Explicit constructions of better vertex expanders and/or unique neighbor expanders.   * Construction deterministic or random high dimensional expanders.   * Pseudorandom generators for different problems.   * Topics around the quantum PCP conjecture.   * Topics around quantum error correcting codes and locally testable codes, including constructions, encoding and decoding algorithms.

 2. Theory informed practical implementations of algorithms   Very often the great advances in theoretical research are either not tested in practice or not even feasible to be implemented in practice. Thus, I am interested in any project that consists in trying to make theoretical ideas applicable in practice. This includes coming up with new algorithms that trade some theoretical guarantees for feasible implementation yet trying to retain the soul of the original idea; implementing new algorithms in a suitable programming language; and empirically testing practical implementations and comparing them with benchmarks / theoretical expectations. A project in this area doesn't have to be in my main areas of research, any theoretical result could be suitable for such a project.

Some examples of areas of interest:   * Streaming algorithms.   * Numeric linear algebra.   * Property testing.   * Parallel / Distributed algorithms.   * Online algorithms.    3. Machine learning with a theoretical foundation

I am interested in projects in machine learning that have some mathematical/theoretical, even if most of the project is applied. This includes topics like mathematical optimization, statistical learning, fairness and privacy.

One particular area I have been recently interested in is in the area of rating systems (e.g., Chess elo) and applications of this to experts problems.

Final Note: I am also willing to advise any project with any mathematical/theoretical component, even if it's not the main one; please reach out via email to chat about project ideas.

Iasonas Petras, Corwin Hall, Room 033

• Research Areas: Information Based Complexity, Numerical Analysis, Quantum Computation.
• Prerequisites: Reasonable mathematical maturity. In case of a project related to Quantum Computation a certain familiarity with quantum mechanics is required (related courses: ELE 396/PHY 208).
• Possible research topics include:

1.   Quantum algorithms and circuits:

• i. Design or simulation quantum circuits implementing quantum algorithms.
• ii. Design of quantum algorithms solving/approximating continuous problems (such as Eigenvalue problems for Partial Differential Equations).

2.   Information Based Complexity:

• i. Necessary and sufficient conditions for tractability of Linear and Linear Tensor Product Problems in various settings (for example worst case or average case). 
• ii. Necessary and sufficient conditions for tractability of Linear and Linear Tensor Product Problems under new tractability and error criteria.
• iii. Necessary and sufficient conditions for tractability of Weighted problems.
• iv. Necessary and sufficient conditions for tractability of Weighted Problems under new tractability and error criteria.

3. Topics in Scientific Computation:

• i. Randomness, Pseudorandomness, MC and QMC methods and their applications (Finance, etc)

Yuri Pritykin, 245 Carl Icahn Lab

• Research interests: Computational biology; Cancer immunology; Regulation of gene expression; Functional genomics; Single-cell technologies.
• Potential research projects: Development, implementation, assessment and/or application of algorithms for analysis, integration, interpretation and visualization of multi-dimensional data in molecular biology, particularly single-cell and spatial genomics data.

Benjamin Raphael, Room 309  

• Research interests: Computational biology and bioinformatics; Cancer genomics; Algorithms and machine learning approaches for analysis of large-scale datasets
• Implementation and application of algorithms to infer evolutionary processes in cancer
• Identifying correlations between combinations of genomic mutations in human and cancer genomes
• Design and implementation of algorithms for genome sequencing from new DNA sequencing technologies
• Graph clustering and network anomaly detection, particularly using diffusion processes and methods from spectral graph theory

Vikram Ramaswamy, 035 Corwin Hall

• Research areas: Interpretability of AI systems, Fairness in AI systems, Computer vision.
• Constructing a new method to explain a model / create an interpretable by design model
• Analyzing a current model / dataset to understand bias within the model/dataset
• Proposing new fairness evaluations
• Proposing new methods to train to improve fairness
• Developing synthetic datasets for fairness / interpretability benchmarks
• Understanding robustness of models

Ran Raz, Room 240

• Research Area: Computational Complexity
• Independent Research Topics: Computational Complexity, Information Theory, Quantum Computation, Theoretical Computer Science

Szymon Rusinkiewicz, Room 406

• Research Areas: computer graphics; computer vision; 3D scanning; 3D printing; robotics; documentation and visualization of cultural heritage artifacts
• Research ways of incorporating rotation invariance into computer visiontasks such as feature matching and classification
• Investigate approaches to robust 3D scan matching
• Model and compensate for imperfections in 3D printing
• Given a collection of small mobile robots, apply control policies learned in simulation to the real robots.

Olga Russakovsky, Room 408

• Research Areas: computer vision, machine learning, deep learning, crowdsourcing, fairness&bias in AI
• Design a semantic segmentation deep learning model that can operate in a zero-shot setting (i.e., recognize and segment objects not seen during training)
• Develop a deep learning classifier that is impervious to protected attributes (such as gender or race) that may be erroneously correlated with target classes
• Build a computer vision system for the novel task of inferring what object (or part of an object) a human is referring to when pointing to a single pixel in the image. This includes both collecting an appropriate dataset using crowdsourcing on Amazon Mechanical Turk, creating a new deep learning formulation for this task, and running extensive analysis of both the data and the model

Sebastian Seung, Princeton Neuroscience Institute, Room 153

• Research Areas: computational neuroscience, connectomics, "deep learning" neural networks, social computing, crowdsourcing, citizen science
• Gamification of neuroscience (EyeWire  2.0)
• Semantic segmentation and object detection in brain images from microscopy
• Computational analysis of brain structure and function
• Neural network theories of brain function

Jaswinder Pal Singh, Room 324

• Research Areas: Boundary of technology and business/applications; building and scaling technology companies with special focus at that boundary; parallel computing systems and applications: parallel and distributed applications and their implications for software and architectural design; system software and programming environments for multiprocessors.
• Develop a startup company idea, and build a plan/prototype for it.
• Explore tradeoffs at the boundary of technology/product and business/applications in a chosen area.
• Study and develop methods to infer insights from data in different application areas, from science to search to finance to others. 
• Design and implement a parallel application. Possible areas include graphics, compression, biology, among many others. Analyze performance bottlenecks using existing tools, and compare programming models/languages.
• Design and implement a scalable distributed algorithm.

Mona Singh, Room 420

• Research Areas: computational molecular biology, as well as its interface with machine learning and algorithms.
• Whole and cross-genome methods for predicting protein function and protein-protein interactions.
• Analysis and prediction of biological networks.
• Computational methods for inferring specific aspects of protein structure from protein sequence data.
• Any other interesting project in computational molecular biology.

Robert Tarjan, 194 Nassau St., Room 308

• Research Areas: Data structures; graph algorithms; combinatorial optimization; computational complexity; computational geometry; parallel algorithms.
• Implement one or more data structures or combinatorial algorithms to provide insight into their empirical behavior.
• Design and/or analyze various data structures and combinatorial algorithms.

Olga Troyanskaya, Room 320

• Research Areas: Bioinformatics; analysis of large-scale biological data sets (genomics, gene expression, proteomics, biological networks); algorithms for integration of data from multiple data sources; visualization of biological data; machine learning methods in bioinformatics.
• Implement and evaluate one or more gene expression analysis algorithm.
• Develop algorithms for assessment of performance of genomic analysis methods.
• Develop, implement, and evaluate visualization tools for heterogeneous biological data.

David Walker, Room 211

• Research Areas: Programming languages, type systems, compilers, domain-specific languages, software-defined networking and security
• Independent Research Topics:  Any other interesting project that involves humanitarian hacking, functional programming, domain-specific programming languages, type systems, compilers, software-defined networking, fault tolerance, language-based security, theorem proving, logic or logical frameworks.

Shengyi Wang, Postdoctoral Research Associate, Room 216

Available for Fall 2024 single-semester IW, only

• Independent Research topics: Explore Escher-style tilings using (introductory) group theory and automata theory to produce beautiful pictures.

Kevin Wayne, Corwin Hall, Room 040

• Research Areas: design, analysis, and implementation of algorithms; data structures; combinatorial optimization; graphs and networks.
• Design and implement computer visualizations of algorithms or data structures.
• Develop pedagogical tools or programming assignments for the computer science curriculum at Princeton and beyond.
• Develop assessment infrastructure and assessments for MOOCs.

Matt Weinberg, 194 Nassau St., Room 222

• Research Areas: algorithms, algorithmic game theory, mechanism design, game theoretical problems in {Bitcoin, networking, healthcare}.
• Theoretical questions related to COS 445 topics such as matching theory, voting theory, auction design, etc. 
• Theoretical questions related to incentives in applications like Bitcoin, the Internet, health care, etc. In a little bit more detail: protocols for these systems are often designed assuming that users will follow them. But often, users will actually be strictly happier to deviate from the intended protocol. How should we reason about user behavior in these protocols? How should we design protocols in these settings?

Huacheng Yu, Room 310

• data structures
• streaming algorithms
• design and analyze data structures / streaming algorithms
• prove impossibility results (lower bounds)
• implement and evaluate data structures / streaming algorithms

Ellen Zhong, Room 314

Opportunities outside the department.

We encourage students to look in to doing interdisciplinary computer science research and to work with professors in departments other than computer science.  However, every CS independent work project must have a strong computer science element (even if it has other scientific or artistic elements as well.)  To do a project with an adviser outside of computer science you must have permission of the department.  This can be accomplished by having a second co-adviser within the computer science department or by contacting the independent work supervisor about the project and having he or she sign the independent work proposal form.

Here is a list of professors outside the computer science department who are eager to work with computer science undergraduates.

Maria Apostolaki, Engineering Quadrangle, C330

• Research areas: Computing & Networking, Data & Information Science, Security & Privacy

Branko Glisic, Engineering Quadrangle, Room E330

• Documentation of historic structures
• Cyber physical systems for structural health monitoring
• Developing virtual and augmented reality applications for documenting structures
• Applying machine learning techniques to generate 3D models from 2D plans of buildings
•  Contact : Rebecca Napolitano, rkn2 (@princeton.edu)

Mihir Kshirsagar, Sherrerd Hall, Room 315

Center for Information Technology Policy.

• Consumer protection
• Content regulation
• Competition law
• Economic development
• Surveillance and discrimination

Sharad Malik, Engineering Quadrangle, Room B224

Select a Senior Thesis Adviser for the 2020-21 Academic Year.

• Design of reliable hardware systems
• Verifying complex software and hardware systems

Prateek Mittal, Engineering Quadrangle, Room B236

• Internet security and privacy 
• Social Networks
• Privacy technologies, anonymous communication
• Network Science
• Internet security and privacy: The insecurity of Internet protocols and services threatens the safety of our critical network infrastructure and billions of end users. How can we defend end users as well as our critical network infrastructure from attacks?
• Trustworthy social systems: Online social networks (OSNs) such as Facebook, Google+, and Twitter have revolutionized the way our society communicates. How can we leverage social connections between users to design the next generation of communication systems?
• Privacy Technologies: Privacy on the Internet is eroding rapidly, with businesses and governments mining sensitive user information. How can we protect the privacy of our online communications? The Tor project (https://www.torproject.org/) is a potential application of interest.

Ken Norman,  Psychology Dept, PNI 137

• Research Areas: Memory, the brain and computation 
• Lab:  Princeton Computational Memory Lab

Potential research topics

• Methods for decoding cognitive state information from neuroimaging data (fMRI and EEG) 
• Neural network simulations of learning and memory

Caroline Savage

Office of Sustainability, Phone:(609)258-7513, Email: cs35 (@princeton.edu)

The  Campus as Lab  program supports students using the Princeton campus as a living laboratory to solve sustainability challenges. The Office of Sustainability has created a list of campus as lab research questions, filterable by discipline and topic, on its  website .

An example from Computer Science could include using  TigerEnergy , a platform which provides real-time data on campus energy generation and consumption, to study one of the many energy systems or buildings on campus. Three CS students used TigerEnergy to create a  live energy heatmap of campus .

Other potential projects include:

• Apply game theory to sustainability challenges
• Develop a tool to help visualize interactions between complex campus systems, e.g. energy and water use, transportation and storm water runoff, purchasing and waste, etc.
• How can we learn (in aggregate) about individuals’ waste, energy, transportation, and other behaviors without impinging on privacy?

Janet Vertesi, Sociology Dept, Wallace Hall, Room 122

• Research areas: Sociology of technology; Human-computer interaction; Ubiquitous computing.
• Possible projects: At the intersection of computer science and social science, my students have built mixed reality games, produced artistic and interactive installations, and studied mixed human-robot teams, among other projects.

David Wentzlaff, Engineering Quadrangle, Room 228

Computing, Operating Systems, Sustainable Computing.

• Instrument Princeton's Green (HPCRC) data center
• Investigate power utilization on an processor core implemented in an FPGA
• Dismantle and document all of the components in modern electronics. Invent new ways to build computers that can be recycled easier.
• Other topics in parallel computer architecture or operating systems

Research Topics & Ideas: CompSci & IT

50+ Computer Science Research Topic Ideas To Fast-Track Your Project

Finding and choosing a strong research topic is the critical first step when it comes to crafting a high-quality dissertation, thesis or research project. If you’ve landed on this post, chances are you’re looking for a computer science-related research topic , but aren’t sure where to start. Here, we’ll explore a variety of CompSci & IT-related research ideas and topic thought-starters, including algorithms, AI, networking, database systems, UX, information security and software engineering.

NB – This is just the start…

The topic ideation and evaluation process has multiple steps . In this post, we’ll kickstart the process by sharing some research topic ideas within the CompSci domain. This is the starting point, but to develop a well-defined research topic, you’ll need to identify a clear and convincing research gap , along with a well-justified plan of action to fill that gap.

If you’re new to the oftentimes perplexing world of research, or if this is your first time undertaking a formal academic research project, be sure to check out our free dissertation mini-course. In it, we cover the process of writing a dissertation or thesis from start to end. Be sure to also sign up for our free webinar that explores how to find a high-quality research topic. 

Overview: CompSci Research Topics

• Algorithms & data structures
• Artificial intelligence ( AI )
• Computer networking
• Database systems
• Human-computer interaction
• Information security (IS)
• Software engineering
• Examples of CompSci dissertation & theses

Topics/Ideas: Algorithms & Data Structures

• An analysis of neural network algorithms’ accuracy for processing consumer purchase patterns
• A systematic review of the impact of graph algorithms on data analysis and discovery in social media network analysis
• An evaluation of machine learning algorithms used for recommender systems in streaming services
• A review of approximation algorithm approaches for solving NP-hard problems
• An analysis of parallel algorithms for high-performance computing of genomic data
• The influence of data structures on optimal algorithm design and performance in Fintech
• A Survey of algorithms applied in internet of things (IoT) systems in supply-chain management
• A comparison of streaming algorithm performance for the detection of elephant flows
• A systematic review and evaluation of machine learning algorithms used in facial pattern recognition
• Exploring the performance of a decision tree-based approach for optimizing stock purchase decisions
• Assessing the importance of complete and representative training datasets in Agricultural machine learning based decision making.
• A Comparison of Deep learning algorithms performance for structured and unstructured datasets with “rare cases”
• A systematic review of noise reduction best practices for machine learning algorithms in geoinformatics.
• Exploring the feasibility of applying information theory to feature extraction in retail datasets.
• Assessing the use case of neural network algorithms for image analysis in biodiversity assessment

Topics & Ideas: Artificial Intelligence (AI)

• Applying deep learning algorithms for speech recognition in speech-impaired children
• A review of the impact of artificial intelligence on decision-making processes in stock valuation
• An evaluation of reinforcement learning algorithms used in the production of video games
• An exploration of key developments in natural language processing and how they impacted the evolution of Chabots.
• An analysis of the ethical and social implications of artificial intelligence-based automated marking
• The influence of large-scale GIS datasets on artificial intelligence and machine learning developments
• An examination of the use of artificial intelligence in orthopaedic surgery
• The impact of explainable artificial intelligence (XAI) on transparency and trust in supply chain management
• An evaluation of the role of artificial intelligence in financial forecasting and risk management in cryptocurrency
• A meta-analysis of deep learning algorithm performance in predicting and cyber attacks in schools

Topics & Ideas: Networking

• An analysis of the impact of 5G technology on internet penetration in rural Tanzania
• Assessing the role of software-defined networking (SDN) in modern cloud-based computing
• A critical analysis of network security and privacy concerns associated with Industry 4.0 investment in healthcare.
• Exploring the influence of cloud computing on security risks in fintech.
• An examination of the use of network function virtualization (NFV) in telecom networks in Southern America
• Assessing the impact of edge computing on network architecture and design in IoT-based manufacturing
• An evaluation of the challenges and opportunities in 6G wireless network adoption
• The role of network congestion control algorithms in improving network performance on streaming platforms
• An analysis of network coding-based approaches for data security
• Assessing the impact of network topology on network performance and reliability in IoT-based workspaces

Topics & Ideas: Database Systems

• An analysis of big data management systems and technologies used in B2B marketing
• The impact of NoSQL databases on data management and analysis in smart cities
• An evaluation of the security and privacy concerns of cloud-based databases in financial organisations
• Exploring the role of data warehousing and business intelligence in global consultancies
• An analysis of the use of graph databases for data modelling and analysis in recommendation systems
• The influence of the Internet of Things (IoT) on database design and management in the retail grocery industry
• An examination of the challenges and opportunities of distributed databases in supply chain management
• Assessing the impact of data compression algorithms on database performance and scalability in cloud computing
• An evaluation of the use of in-memory databases for real-time data processing in patient monitoring
• Comparing the effects of database tuning and optimization approaches in improving database performance and efficiency in omnichannel retailing

Topics & Ideas: Human-Computer Interaction

• An analysis of the impact of mobile technology on human-computer interaction prevalence in adolescent men
• An exploration of how artificial intelligence is changing human-computer interaction patterns in children
• An evaluation of the usability and accessibility of web-based systems for CRM in the fast fashion retail sector
• Assessing the influence of virtual and augmented reality on consumer purchasing patterns
• An examination of the use of gesture-based interfaces in architecture
• Exploring the impact of ease of use in wearable technology on geriatric user
• Evaluating the ramifications of gamification in the Metaverse
• A systematic review of user experience (UX) design advances associated with Augmented Reality
• A comparison of natural language processing algorithms automation of customer response Comparing end-user perceptions of natural language processing algorithms for automated customer response
• Analysing the impact of voice-based interfaces on purchase practices in the fast food industry

Topics & Ideas: Information Security

• A bibliometric review of current trends in cryptography for secure communication
• An analysis of secure multi-party computation protocols and their applications in cloud-based computing
• An investigation of the security of blockchain technology in patient health record tracking
• A comparative study of symmetric and asymmetric encryption algorithms for instant text messaging
• A systematic review of secure data storage solutions used for cloud computing in the fintech industry
• An analysis of intrusion detection and prevention systems used in the healthcare sector
• Assessing security best practices for IoT devices in political offices
• An investigation into the role social media played in shifting regulations related to privacy and the protection of personal data
• A comparative study of digital signature schemes adoption in property transfers
• An assessment of the security of secure wireless communication systems used in tertiary institutions

Topics & Ideas: Software Engineering

• A study of agile software development methodologies and their impact on project success in pharmacology
• Investigating the impacts of software refactoring techniques and tools in blockchain-based developments
• A study of the impact of DevOps practices on software development and delivery in the healthcare sector
• An analysis of software architecture patterns and their impact on the maintainability and scalability of cloud-based offerings
• A study of the impact of artificial intelligence and machine learning on software engineering practices in the education sector
• An investigation of software testing techniques and methodologies for subscription-based offerings
• A review of software security practices and techniques for protecting against phishing attacks from social media
• An analysis of the impact of cloud computing on the rate of software development and deployment in the manufacturing sector
• Exploring the impact of software development outsourcing on project success in multinational contexts
• An investigation into the effect of poor software documentation on app success in the retail sector

CompSci & IT Dissertations/Theses

While the ideas we’ve presented above are a decent starting point for finding a CompSci-related research topic, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses to see how this all comes together.

Below, we’ve included a selection of research projects from various CompSci-related degree programs to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

• An array-based optimization framework for query processing and data analytics (Chen, 2021)
• Dynamic Object Partitioning and replication for cooperative cache (Asad, 2021)
• Embedding constructural documentation in unit tests (Nassif, 2019)
• PLASA | Programming Language for Synchronous Agents (Kilaru, 2019)
• Healthcare Data Authentication using Deep Neural Network (Sekar, 2020)
• Virtual Reality System for Planetary Surface Visualization and Analysis (Quach, 2019)
• Artificial neural networks to predict share prices on the Johannesburg stock exchange (Pyon, 2021)
• Predicting household poverty with machine learning methods: the case of Malawi (Chinyama, 2022)
• Investigating user experience and bias mitigation of the multi-modal retrieval of historical data (Singh, 2021)
• Detection of HTTPS malware traffic without decryption (Nyathi, 2022)
• Redefining privacy: case study of smart health applications (Al-Zyoud, 2019)
• A state-based approach to context modeling and computing (Yue, 2019)
• A Novel Cooperative Intrusion Detection System for Mobile Ad Hoc Networks (Solomon, 2019)
• HRSB-Tree for Spatio-Temporal Aggregates over Moving Regions (Paduri, 2019)

Looking at these titles, you can probably pick up that the research topics here are quite specific and narrowly-focused , compared to the generic ones presented earlier. This is an important thing to keep in mind as you develop your own research topic. That is to say, to create a top-notch research topic, you must be precise and target a specific context with specific variables of interest . In other words, you need to identify a clear, well-justified research gap.

Fast-Track Your Research Topic

If you’re still feeling a bit unsure about how to find a research topic for your Computer Science dissertation or research project, check out our Topic Kickstarter service.

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100 Great Computer Science Research Topics Ideas for 2023

Being a computer student in 2023 is not easy. Besides studying a constantly evolving subject, you have to come up with great computer science research topics at some point in your academic life. If you’re reading this article, you’re among many other students that have also come to this realization.

Interesting Computer Science Topics

Awesome research topics in computer science, hot topics in computer science, topics to publish a journal on computer science.

• Controversial Topics in Computer Science

Fun AP Computer Science Topics

Exciting computer science ph.d. topics, remarkable computer science research topics for undergraduates, incredible final year computer science project topics, advanced computer science topics, unique seminars topics for computer science, exceptional computer science masters thesis topics, outstanding computer science presentation topics.

• Key Computer Science Essay Topics

Main Project Topics for Computer Science

• We Can Help You with Computer Science Topics

Whether you’re earnestly searching for a topic or stumbled onto this article by accident, there is no doubt that every student needs excellent computer science-related topics for their paper. A good topic will not only give your essay or research a good direction but will also make it easy to come up with supporting points. Your topic should show all your strengths as well.

Fortunately, this article is for every student that finds it hard to generate a suitable computer science topic. The following 100+ topics will help give you some inspiration when creating your topics. Let’s get into it.

One of the best ways of making your research paper interesting is by coming up with relevant topics in computer science . Here are some topics that will make your paper immersive:

• Evolution of virtual reality
• What is green cloud computing
• Ways of creating a Hopefield neural network in C++
• Developments in graphic systems in computers
• The five principal fields in robotics
• Developments and applications of nanotechnology
• Differences between computer science and applied computing

Your next research topic in computer science shouldn’t be tough to find once you’ve read this section. If you’re looking for simple final year project topics in computer science, you can find some below.

• Applications of the blockchain technology in the banking industry
• Computational thinking and how it influences science
• Ways of terminating phishing
• Uses of artificial intelligence in cyber security
• Define the concepts of a smart city
• Applications of the Internet of Things
• Discuss the applications of the face detection application

Whenever a topic is described as “hot,” it means that it is a trendy topic in computer science. If computer science project topics for your final years are what you’re looking for, have a look at some below:

• Applications of the Metaverse in the world today
• Discuss the challenges of machine learning
• Advantages of artificial intelligence
• Applications of nanotechnology in the paints industry
• What is quantum computing?
• Discuss the languages of parallel computing
• What are the applications of computer-assisted studies?

Perhaps you’d like to write a paper that will get published in a journal. If you’re searching for the best project topics for computer science students that will stand out in a journal, check below:

• Developments in human-computer interaction
• Applications of computer science in medicine
• Developments in artificial intelligence in image processing
• Discuss cryptography and its applications
• Discuss methods of ransomware prevention
• Applications of Big Data in the banking industry
• Challenges of cloud storage services in 2023

 Controversial Topics in Computer Science

Some of the best computer science final year project topics are those that elicit debates or require you to take a stand. You can find such topics listed below for your inspiration:

• Can robots be too intelligent?
• Should the dark web be shut down?
• Should your data be sold to corporations?
• Will robots completely replace the human workforce one day?
• How safe is the Metaverse for children?
• Will artificial intelligence replace actors in Hollywood?
• Are social media platforms safe anymore?

Are you a computer science student looking for AP topics? You’re in luck because the following final year project topics for computer science are suitable for you.

• Standard browser core with CSS support
• Applications of the Gaussian method in C++ development in integrating functions
• Vital conditions of reducing risk through the Newton method
• How to reinforce machine learning algorithms.
• How do artificial neural networks function?
• Discuss the advancements in computer languages in machine learning
• Use of artificial intelligence in automated cars

When studying to get your doctorate in computer science, you need clear and relevant topics that generate the reader’s interest. Here are some Ph.D. topics in computer science you might consider:

• Developments in information technology
• Is machine learning detrimental to the human workforce?
• How to write an algorithm for deep learning
• What is the future of 5G in wireless networks
• Statistical data in Maths modules in Python
• Data retention automation from a website using API
• Application of modern programming languages

Looking for computer science topics for research is not easy for an undergraduate. Fortunately, these computer science project topics should make your research paper easy:

• Ways of using artificial intelligence in real estate
• Discuss reinforcement learning and its applications
• Uses of Big Data in science and medicine
• How to sort algorithms using Haskell
• How to create 3D configurations for a website
• Using inverse interpolation to solve non-linear equations
• Explain the similarities between the Internet of Things and artificial intelligence

Your dissertation paper is one of the most crucial papers you’ll ever do in your final year. That’s why selecting the best ethics in computer science topics is a crucial part of your paper. Here are some project topics for the computer science final year.

• How to incorporate numerical methods in programming
• Applications of blockchain technology in cloud storage
• How to come up with an automated attendance system
• Using dynamic libraries for site development
• How to create cubic splines
• Applications of artificial intelligence in the stock market
• Uses of quantum computing in financial modeling

Your instructor may want you to challenge yourself with an advanced science project. Thus, you may require computer science topics to learn and research. Here are some that may inspire you:

• Discuss the best cryptographic protocols
• Advancement of artificial intelligence used in smartphones
• Briefly discuss the types of security software available
• Application of liquid robots in 2023
• How to use quantum computers to solve decoherence problem
• macOS vs. Windows; discuss their similarities and differences
• Explain the steps taken in a cyber security audit

When searching for computer science topics for a seminar, make sure they are based on current research or events. Below are some of the latest research topics in computer science:

• How to reduce cyber-attacks in 2023
• Steps followed in creating a network
• Discuss the uses of data science
• Discuss ways in which social robots improve human interactions
• Differentiate between supervised and unsupervised machine learning
• Applications of robotics in space exploration
• The contrast between cyber-physical and sensor network systems

Are you looking for computer science thesis topics for your upcoming projects? The topics below are meant to help you write your best paper yet:

• Applications of computer science in sports
• Uses of computer technology in the electoral process
• Using Fibonacci to solve the functions maximum and their implementations
• Discuss the advantages of using open-source software
• Expound on the advancement of computer graphics
• Briefly discuss the uses of mesh generation in computational domains
• How much data is generated from the internet of things?

A computer science presentation requires a topic relevant to current events. Whether your paper is an assignment or a dissertation, you can find your final year computer science project topics below:

• Uses of adaptive learning in the financial industry
• Applications of transitive closure on graph
• Using RAD technology in developing software
• Discuss how to create maximum flow in the network
• How to design and implement functional mapping
• Using artificial intelligence in courier tracking and deliveries
• How to make an e-authentication system

 Key Computer Science Essay Topics

You may be pressed for time and require computer science master thesis topics that are easy. Below are some topics that fit this description:

• What are the uses of cloud computing in 2023
• Discuss the server-side web technologies
• Compare and contrast android and iOS
• How to come up with a face detection algorithm
• What is the future of NFTs
• How to create an artificial intelligence shopping system
• How to make a software piracy prevention algorithm

One major mistake students make when writing their papers is selecting topics unrelated to the study at hand. This, however, will not be an issue if you get topics related to computer science, such as the ones below:

• Using blockchain to create a supply chain management system
• How to protect a web app from malicious attacks
• Uses of distributed information processing systems
• Advancement of crowd communication software since COVID-19
• Uses of artificial intelligence in online casinos
• Discuss the pillars of math computations
• Discuss the ethical concerns arising from data mining

We Can Help You with Computer Science Topics, Essays, Thesis, and Research Papers

We hope that this list of computer science topics helps you out of your sticky situation. We do offer other topics in different subjects. Additionally, we also offer professional writing services tailor-made for you.

We understand what students go through when searching the internet for computer science research paper topics, and we know that many students don’t know how to write a research paper to perfection. However, you shouldn’t have to go through all this when we’re here to help.

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BS | Research Opportunities

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The Computer Science Department at Stanford have faculty and students that are globally recognized for their innovative and cutting-edge research. We offer scholars various opportunities at their disposal to participate in undergraduate research. If you are interested in research, we welcome you to explore the opportunities at your disposal.

CURIS Research

The program for CS undergrad Summer research. Participating students will work on their projects full-time and are paid a stipend for living expenses. 

Independent Study

Undergraduate research is often done through CURIS, for academic credit, or through an informal arrangement with a professor.

Getting Started

• Undergraduate CS research website . The most reliable way to learn about projects you can get involved in is through the  undergraduate CS research  website. Throughout the year, professors have openings for undergrads to do work in their labs. They post descriptions of these projects on the site for your perusal. This site lists CS research projects during the academic year for course credit, CS research projects for the Summer quarter under CURIS (paid internship), and research projects in other departments that include CS applications.
• Go to office hours . Find a professor whose research interests you want to learn more about. Discuss what possibilities are available or find out more about a particular group. Often the professor will be able to direct you to some research papers that might be valuable to read or other groups that you might find interesting. It's always a good idea to email a professor and let them know that you will be coming in. That way if their office hours are particularly busy, they can suggest another time.
• Connect with a graduate student . Graduate students work on projects every day and deal with most of the details, they are probably one of the best sources of information. They will have a good idea of what role you could initially play in the project and will also be able to give an honest assessment of what it is like to work with the professor and what are the expectations of the group. Finally, if you decide to work with the group, the graduate students will probably be the ones who will be mentoring you in the day-to-day aspects of your work. Before you choose a project, try to meet with at least one graduate student in the group, preferably one that would be mentoring you. If you are still deciding between projects, ask the graduate students for their opinion.
• Read your email . The bscs list is constantly getting announcements about presentations that are being given by faculty, advanced graduate students, and visiting faculty. Take the time to read through some of the abstracts and pick a few that interest you. These announcements are not usually forwarded to the considering_cs list. If you are interested in getting these announcements, visit the  course advisor  and declare CS !
• CURIS poster sessions . At the end of the Summer quarter and the beginning of the Fall quarter, the CURIS program organizes poster sessions for undergraduates to present their Summer research projects. This is a great opportunity for you to get first-hand information about your peers' research experience as well as potential project ideas and research groups of interest. In addition, the display in the Gates lobby shows a collection of both undergraduate and graduate research projects year-round.
• 500 level seminars . All of the CS 500 level courses are topic seminars. For instance,  CS 547 Seminar  focuses on Human-Computer Interaction topics. Each week, a different speaker comes in and presents their research. Sometimes the speakers are Stanford professors, graduate students, or they're outside visitors. The presentations are technical, check the schedules on the class web pages to find talks that may be interesting.
• CS300 ( speaker schedule ) . At the beginning of each academic year, all new PhD students are required to take CS 300. In each seminar, two professors come in and describe their research work. The idea is to give PhD students an overview of the ongoing research so they can decide which groups they would like to join. Although the class is technically for PhD students, undergraduate and Master's students can enroll. The presentations are likely to be somewhat technical, but since they are geared toward PhD students with a broad variety of interests, they should be fairly accessible.

Computing Research News

This article is published in the October 2022 issue.

On Undergraduate Research in Computer Science: Tips for shaping successful undergraduate research projects

Note: Khuller was the recipient of the 2020 CRA-E Undergraduate Research Faculty Mentoring Award , which recognizes individual faculty members who have provided exceptional mentorship, undergraduate research experiences and, in parallel, guidance on admission and matriculation of these students to research-focused graduate programs in computing. CRA-E is currently accepting nominations for the 2023 award program .

One of the goals I hope to accomplish with this article is to open the eyes of faculty to the ways in which bright and motivated undergraduates can contribute meaningfully to their research projects and groups. This piece intends to  help educate folks who  have limited experience with undergraduate research or are unsure how to come up with research projects. I hope it helps others learn quickly from the knowledge I have gained over the years.

Exposing undergraduates to research may encourage them to pursue PhDs At the CRA Conference at Snowbird this summer, data was presented that showed that the overall number of PhDs granted in Computer Science (CS) in the US has not changed substantially in the last decade even though undergraduate programs have grown significantly. Meanwhile, the percentage of US students getting PhDs in CS showed a pretty substantial decline from 48%  to 31%. While there are many factors at play–notably a strong job market for undergraduates– I do know from prior discussions with undergraduate students (UGs), that many CS departments also do not make a substantial effort in exposing UGs to research opportunities. Moreover, when I started as a faculty member I too struggled in defining good research projects for undergraduates (they were either too easy or too similar to PhD research topics, and so were likely not appropriate for undergraduates). I think getting UGs excited about research is perhaps the first step to getting them excited to think about getting a PhD as a career option.

Is research by undergraduate students an oxymoron? I will admit that initially I too was skeptical about the possibility and success of true undergraduate research. My own research experiences as an undergraduate were pathetic. As a student often I would hear people say “I am going to the library to do research”. So I too went to the library to do research. Research to me meant finding something in the library that was not in a textbook, understanding it, and telling people about the work.  At that point I thought I had done some research! I never gave much thought to how new material got into journals to begin with.

Talking to a colleague recently – he said “maybe what all UGs do in a chemistry lab is wash test tubes….”.  The truth is that I do not really know what UG research in chemistry looks like.  But the point I wanted to make with this article is that high level UG research in CS is entirely doable. Indeed, in theoretical computer science (TCS) we have witnessed brilliant papers in top conferences by undergraduate students, and I would argue that UG research can be done quite effectively in other areas of computing research as well.

So what should UG research in CS look like? I have advised over 30 undergraduate researchers and based on my experiences, I have a few observations. Most successful research projects involving undergraduates require a lead time of about 18 months before graduation. It usually takes a few months for the student to read the relevant papers, and for us to identify a topic that aligns with the student’s interests and background. I usually expect that students would have taken both an undergraduate level class in algorithm design as well as discrete mathematics. If they can take a graduate level class, that would also be incredibly valuable.

Tips for shaping successful undergraduate research projects Below is my process for defining a successful UG research project. UGs typically have 12-18 months for a research project, not 3-4 years like most Ph.D. students.

• At my first meeting, I ask the students about the different topics they learned about in their Algorithms class and what appealed to them the most.
• Using their answer from bullet #1, I usually spend some time thinking about the right topic for them to work on. The key here is that any paper that the student has to read should not have a long chain of preceding papers that will take them months to get to. Luckily many graph problems as well as combinatorial optimization and scheduling problems lend themselves to easy descriptions. So in a few minutes you can describe the problem.
• The research should be on a topic of significant interest and related to things I have worked on, and one in which I have some intuition about the direction of research and conjectures that might be true and provable with elementary methods.
• I usually treat undergraduates the same way as PhD students, while being aware that they have limited time (a year) as opposed to PhD students who might begin a vaguely defined research project.
• Have them work jointly with a PhD student, if the research is close enough to the PhD students interests and expertise. It’s also a valuable mentoring experience for the PhD student. Simply having a couple of undergrads work on a project jointly can be motivating for both.
• One benefit of tackling hard problems at this stage is that there is no downside. If a student does not make progress, in the worst case they read a few papers and learn some new things. This allows us to work on problems with less pressure than second and third year graduate students are under.

Over the last 25 years, I have had the opportunity to work with a very large number of talented undergraduates –from University of Maryland (UMD) and Northwestern  University, but also many via the NSF funded REU site program (REU CAAR) that  Bill Gasarch (UMD) and I co-ran from 2012-2018. Many of the students I advised, have published the work they did and subsequently received fellowships and admission to top Ph.D programs. Recent graduates are Elissa Redmiles (Ph.D. UMD), Frederic Koehler (Ph.D. MIT) and Riley Murray (Ph.D. Caltech).  I specifically wanted to mention An Zhu (Ph.D. Stanford University) who first opened my eyes to the amazing work that is possible by undergraduates.

About the Author Samir Khuller received his M.S and Ph.D from Cornell University in 1989 and 1990, respectively, under the supervision of Vijay Vazirani. He was the first Elizabeth Stevinson Iribe Chair for CS at the University of Maryland. As chair he led the development of the Brendan Iribe Center for Computer Science and Innovation, a project completed in March 2019. In March 2019, Khuller joined Northwestern University as the Peter and Adrienne Barris Chair for CS.

His research interests are in graph algorithms, discrete optimization, and computational geometry. He has published about 200 journal and conference papers, and several book chapters on these topics. He served on the ESA Steering Committee from 2012-2016 and chaired the 2019 MAPSP Scheduling Workshop, and served on the program committee’s of many top conferences.  From 2018-2021 he was Chair of SIGACT. In 2020, he received the CRA-E Undergraduate Research Mentoring Award and in 2021 he was selected as a Fellow of EATCS.

He received the National Science Foundation’s Career Development Award, several Department Teaching Awards, the Dean’s Teaching Excellence Award and also a CTE-Lilly Teaching Fellowship. In 2003, he and his students were awarded the “Best newcomer paper” award for the ACM PODS Conference. He received the University of Maryland’s Distinguished Scholar Teacher Award in 2007, as well as a Google Research Award and an Amazon Research Award. In 2016, he received the European Symposium on Algorithms inaugural Test of Time Award for his work with Sudipto Guha on Connected Dominating Sets. He graduated at the top of the Computer Science Class from IIT-Kanpur.

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The Top 10 Most Interesting Computer Science Research Topics

Computer science touches nearly every area of our lives. With new advancements in technology, the computer science field is constantly evolving, giving rise to new computer science research topics. These topics attempt to answer various computer science research questions and how they affect the tech industry and the larger world.

Computer science research topics can be divided into several categories, such as artificial intelligence, big data and data science, human-computer interaction, security and privacy, and software engineering. If you are a student or researcher looking for computer research paper topics. In that case, this article provides some suggestions on examples of computer science research topics and questions.

Find your bootcamp match

What makes a strong computer science research topic.

A strong computer science topic is clear, well-defined, and easy to understand. It should also reflect the research’s purpose, scope, or aim. In addition, a strong computer science research topic is devoid of abbreviations that are not generally known, though, it can include industry terms that are currently and generally accepted.

Tips for Choosing a Computer Science Research Topic

• Brainstorm . Brainstorming helps you develop a few different ideas and find the best topic for you. Some core questions you should ask are, What are some open questions in computer science? What do you want to learn more about? What are some current trends in computer science?
• Choose a sub-field . There are many subfields and career paths in computer science . Before choosing a research topic, ensure that you point out which aspect of computer science the research will focus on. That could be theoretical computer science, contemporary computing culture, or even distributed computing research topics.
• Aim to answer a question . When you’re choosing a research topic in computer science, you should always have a question in mind that you’d like to answer. That helps you narrow down your research aim to meet specified clear goals.
• Do a comprehensive literature review . When starting a research project, it is essential to have a clear idea of the topic you plan to study. That involves doing a comprehensive literature review to better understand what has been learned about your topic in the past.
• Keep the topic simple and clear. The topic should reflect the scope and aim of the research it addresses. It should also be concise and free of ambiguous words. Hence, some researchers recommended that the topic be limited to five to 15 substantive words. It can take the form of a question or a declarative statement.

What’s the Difference Between a Research Topic and a Research Question?

A research topic is the subject matter that a researcher chooses to investigate. You may also refer to it as the title of a research paper. It summarizes the scope of the research and captures the researcher’s approach to the research question. Hence, it may be broad or more specific. For example, a broad topic may read, Data Protection and Blockchain, while a more specific variant can read, Potential Strategies to Privacy Issues on the Blockchain.

On the other hand, a research question is the fundamental starting point for any research project. It typically reflects various real-world problems and, sometimes, theoretical computer science challenges. As such, it must be clear, concise, and answerable.

How to Create Strong Computer Science Research Questions

To create substantial computer science research questions, one must first understand the topic at hand. Furthermore, the research question should generate new knowledge and contribute to the advancement of the field. It could be something that has not been answered before or is only partially answered. It is also essential to consider the feasibility of answering the question.

Top 10 Computer Science Research Paper Topics

1. battery life and energy storage for 5g equipment.

The 5G network is an upcoming cellular network with much higher data rates and capacity than the current 4G network. According to research published in the European Scientific Institute Journal, one of the main concerns with the 5G network is the high energy consumption of the 5G-enabled devices . Hence, this research on this topic can highlight the challenges and proffer unique solutions to make more energy-efficient designs.

2. The Influence of Extraction Methods on Big Data Mining

Data mining has drawn the scientific community’s attention, especially with the explosive rise of big data. Many research results prove that the extraction methods used have a significant effect on the outcome of the data mining process. However, a topic like this analyzes algorithms. It suggests strategies and efficient algorithms that may help understand the challenge or lead the way to find a solution.

3. Integration of 5G with Analytics and Artificial Intelligence

According to the International Finance Corporation, 5G and AI technologies are defining emerging markets and our world. Through different technologies, this research aims to find novel ways to integrate these powerful tools to produce excellent results. Subjects like this often spark great discoveries that pioneer new levels of research and innovation. A breakthrough can influence advanced educational technology, virtual reality, metaverse, and medical imaging.

4. Leveraging Asynchronous FPGAs for Crypto Acceleration

To support the growing cryptocurrency industry, there is a need to create new ways to accelerate transaction processing. This project aims to use asynchronous Field-Programmable Gate Arrays (FPGAs) to accelerate cryptocurrency transaction processing. It explores how various distributed computing technologies can influence mining cryptocurrencies faster with FPGAs and generally enjoy faster transactions.

5. Cyber Security Future Technologies

Cyber security is a trending topic among businesses and individuals, especially as many work teams are going remote. Research like this can stretch the length and breadth of the cyber security and cloud security industries and project innovations depending on the researcher’s preferences. Another angle is to analyze existing or emerging solutions and present discoveries that can aid future research.

6. Exploring the Boundaries Between Art, Media, and Information Technology

The field of computers and media is a vast and complex one that intersects in many ways. They create images or animations using design technology like algorithmic mechanism design, design thinking, design theory, digital fabrication systems, and electronic design automation. This paper aims to define how both fields exist independently and symbiotically.

7. Evolution of Future Wireless Networks Using Cognitive Radio Networks

This research project aims to study how cognitive radio technology can drive evolution in future wireless networks. It will analyze the performance of cognitive radio-based wireless networks in different scenarios and measure its impact on spectral efficiency and network capacity. The research project will involve the development of a simulation model for studying the performance of cognitive radios in different scenarios.

8. The Role of Quantum Computing and Machine Learning in Advancing Medical Predictive Systems

In a paper titled Exploring Quantum Computing Use Cases for Healthcare , experts at IBM highlighted precision medicine and diagnostics to benefit from quantum computing. Using biomedical imaging, machine learning, computational biology, and data-intensive computing systems, researchers can create more accurate disease progression prediction, disease severity classification systems, and 3D Image reconstruction systems vital for treating chronic diseases.

9. Implementing Privacy and Security in Wireless Networks

Wireless networks are prone to attacks, and that has been a big concern for both individual users and organizations. According to the Cyber Security and Infrastructure Security Agency CISA, cyber security specialists are working to find reliable methods of securing wireless networks . This research aims to develop a secure and privacy-preserving communication framework for wireless communication and social networks.

10. Exploring the Challenges and Potentials of Biometric Systems Using Computational Techniques

Much discussion surrounds biometric systems and the potential for misuse and privacy concerns. When exploring how biometric systems can be effectively used, issues such as verification time and cost, hygiene, data bias, and cultural acceptance must be weighed. The paper may take a critical study into the various challenges using computational tools and predict possible solutions.

Other Examples of Computer Science Research Topics & Questions

Computer research topics.

• The confluence of theoretical computer science, deep learning, computational algorithms, and performance computing
• Exploring human-computer interactions and the importance of usability in operating systems
• Predicting the limits of networking and distributed systems
• Controlling data mining on public systems through third-party applications
• The impact of green computing on the environment and computational science

Computer Research Questions

• Why are there so many programming languages?
• Is there a better way to enhance human-computer interactions in computer-aided learning?
• How safe is cloud computing, and what are some ways to enhance security?
• Can computers effectively assist in the sequencing of human genes?
• How valuable is SCRUM methodology in Agile software development?

Choosing the Right Computer Science Research Topic

Computer science research is a vast field, and it can be challenging to choose the right topic. There are a few things to keep in mind when making this decision. Choose a topic that you are interested in. This will make it easier to stay motivated and produce high-quality research for your computer science degree .

Select a topic that is relevant to your field of study. This will help you to develop specialized knowledge in the area. Choose a topic that has potential for future research. This will ensure that your research is relevant and up-to-date. Typically, coding bootcamps provide a framework that streamlines students’ projects to a specific field, doing their search for a creative solution more effortless.

Computer Science Research Topics FAQ

To start a computer science research project, you should look at what other content is out there. Complete a literature review to know the available findings surrounding your idea. Design your research and ensure that you have the necessary skills and resources to complete the project.

The first step to conducting computer science research is to conceptualize the idea and review existing knowledge about that subject. You will design your research and collect data through surveys or experiments. Analyze your data and build a prototype or graphical model. You will also write a report and present it to a recognized body for review and publication.

You can find computer science research jobs on the job boards of many universities. Many universities have job boards on their websites that list open positions in research and academia. Also, many Slack and GitHub channels for computer scientists provide regular updates on available projects.

There are several hot topics and questions in AI that you can build your research on. Below are some AI research questions you may consider for your research paper.

• Will it be possible to build artificial emotional intelligence?
• Will robots replace humans in all difficult cumbersome jobs as part of the progress of civilization?
• Can artificial intelligence systems self-improve with knowledge from the Internet?

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Current Undergraduate Research Opportunities

The Department of Computer Science, as well as Purdue University as a whole, has multiple research faculty engaging in research for a variety of areas both within the field of computer science and beyond.  For an undergraduate student looking to join in research the process may seem daunting, so here are some FAQ's and resources to assist in getting started.

When do I get involved in research? 

Undergraduate students can engage in research opportunities as early as their freshman year. This will depend on the research project as well as the professor's requirements and skillsets needed. Some professors will want you to have taken a specific course before you start research, while others say it's never too early to engage in a project, especially since you'll do a lot of your learning on the job.

How do I get involved in research?

The first step is finding the type of research you would like to be involved in (see next question for a list of websites). You should talk with faculty who were or are your instructors for ideas and insights. If you are approaching faculty that you have not had for a course, be sure you write a clear and detailed email about your request to be part of their research and see if you can meet them in person to discuss further.

Your academic advisor is also a great resource. They can discuss how to develop the skills you'll need for research, help manage your expectations, assist with the paperwork you need to register once you are on a research project as well as provide other insight and resources.

Excelling in coursework leads to research opportunities

What opportunities are there to do research?

Research is available to students not only through the academic year, but can be an alternative to internships during the summer. Besides research on Purdue's campus (either through the Department of Computer Science or other departments on campus) there are resources and opportunities to do research on other campuses across the country or with other organizations.

Volunteering for research leads to first paper

Undergraduate research resources at Purdue:

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Research Opportunities off-campus:

• National Science Foundation's Research Experience for Undergraduates (REU's)
• Computing Research Association's Computer Science Undergraduate Research (CONQUER)

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Home » 500+ Computer Science Research Topics

500+ Computer Science Research Topics

Computer Science is a constantly evolving field that has transformed the world we live in today. With new technologies emerging every day, there are countless research opportunities in this field. Whether you are interested in artificial intelligence, machine learning, cybersecurity, data analytics, or computer networks, there are endless possibilities to explore. In this post, we will delve into some of the most interesting and important research topics in Computer Science. From the latest advancements in programming languages to the development of cutting-edge algorithms, we will explore the latest trends and innovations that are shaping the future of Computer Science. So, whether you are a student or a professional, read on to discover some of the most exciting research topics in this dynamic and rapidly expanding field.

Computer Science Research Topics

Computer Science Research Topics are as follows:

• Using machine learning to detect and prevent cyber attacks
• Developing algorithms for optimized resource allocation in cloud computing
• Investigating the use of blockchain technology for secure and decentralized data storage
• Developing intelligent chatbots for customer service
• Investigating the effectiveness of deep learning for natural language processing
• Developing algorithms for detecting and removing fake news from social media
• Investigating the impact of social media on mental health
• Developing algorithms for efficient image and video compression
• Investigating the use of big data analytics for predictive maintenance in manufacturing
• Developing algorithms for identifying and mitigating bias in machine learning models
• Investigating the ethical implications of autonomous vehicles
• Developing algorithms for detecting and preventing cyberbullying
• Investigating the use of machine learning for personalized medicine
• Developing algorithms for efficient and accurate speech recognition
• Investigating the impact of social media on political polarization
• Developing algorithms for sentiment analysis in social media data
• Investigating the use of virtual reality in education
• Developing algorithms for efficient data encryption and decryption
• Investigating the impact of technology on workplace productivity
• Developing algorithms for detecting and mitigating deepfakes
• Investigating the use of artificial intelligence in financial trading
• Developing algorithms for efficient database management
• Investigating the effectiveness of online learning platforms
• Developing algorithms for efficient and accurate facial recognition
• Investigating the use of machine learning for predicting weather patterns
• Developing algorithms for efficient and secure data transfer
• Investigating the impact of technology on social skills and communication
• Developing algorithms for efficient and accurate object recognition
• Investigating the use of machine learning for fraud detection in finance
• Developing algorithms for efficient and secure authentication systems
• Investigating the impact of technology on privacy and surveillance
• Developing algorithms for efficient and accurate handwriting recognition
• Investigating the use of machine learning for predicting stock prices
• Developing algorithms for efficient and secure biometric identification
• Investigating the impact of technology on mental health and well-being
• Developing algorithms for efficient and accurate language translation
• Investigating the use of machine learning for personalized advertising
• Developing algorithms for efficient and secure payment systems
• Investigating the impact of technology on the job market and automation
• Developing algorithms for efficient and accurate object tracking
• Investigating the use of machine learning for predicting disease outbreaks
• Developing algorithms for efficient and secure access control
• Investigating the impact of technology on human behavior and decision making
• Developing algorithms for efficient and accurate sound recognition
• Investigating the use of machine learning for predicting customer behavior
• Developing algorithms for efficient and secure data backup and recovery
• Investigating the impact of technology on education and learning outcomes
• Developing algorithms for efficient and accurate emotion recognition
• Investigating the use of machine learning for improving healthcare outcomes
• Developing algorithms for efficient and secure supply chain management
• Investigating the impact of technology on cultural and societal norms
• Developing algorithms for efficient and accurate gesture recognition
• Investigating the use of machine learning for predicting consumer demand
• Developing algorithms for efficient and secure cloud storage
• Investigating the impact of technology on environmental sustainability
• Developing algorithms for efficient and accurate voice recognition
• Investigating the use of machine learning for improving transportation systems
• Developing algorithms for efficient and secure mobile device management
• Investigating the impact of technology on social inequality and access to resources
• Machine learning for healthcare diagnosis and treatment
• Machine Learning for Cybersecurity
• Machine learning for personalized medicine
• Cybersecurity threats and defense strategies
• Big data analytics for business intelligence
• Blockchain technology and its applications
• Human-computer interaction in virtual reality environments
• Artificial intelligence for autonomous vehicles
• Natural language processing for chatbots
• Cloud computing and its impact on the IT industry
• Internet of Things (IoT) and smart homes
• Robotics and automation in manufacturing
• Augmented reality and its potential in education
• Data mining techniques for customer relationship management
• Computer vision for object recognition and tracking
• Quantum computing and its applications in cryptography
• Social media analytics and sentiment analysis
• Recommender systems for personalized content delivery
• Mobile computing and its impact on society
• Bioinformatics and genomic data analysis
• Deep learning for image and speech recognition
• Digital signal processing and audio processing algorithms
• Cloud storage and data security in the cloud
• Wearable technology and its impact on healthcare
• Computational linguistics for natural language understanding
• Cognitive computing for decision support systems
• Cyber-physical systems and their applications
• Edge computing and its impact on IoT
• Machine learning for fraud detection
• Cryptography and its role in secure communication
• Cybersecurity risks in the era of the Internet of Things
• Natural language generation for automated report writing
• 3D printing and its impact on manufacturing
• Virtual assistants and their applications in daily life
• Cloud-based gaming and its impact on the gaming industry
• Computer networks and their security issues
• Cyber forensics and its role in criminal investigations
• Machine learning for predictive maintenance in industrial settings
• Augmented reality for cultural heritage preservation
• Human-robot interaction and its applications
• Data visualization and its impact on decision-making
• Cybersecurity in financial systems and blockchain
• Computer graphics and animation techniques
• Biometrics and its role in secure authentication
• Cloud-based e-learning platforms and their impact on education
• Natural language processing for machine translation
• Machine learning for predictive maintenance in healthcare
• Cybersecurity and privacy issues in social media
• Computer vision for medical image analysis
• Natural language generation for content creation
• Cybersecurity challenges in cloud computing
• Human-robot collaboration in manufacturing
• Data mining for predicting customer churn
• Artificial intelligence for autonomous drones
• Cybersecurity risks in the healthcare industry
• Machine learning for speech synthesis
• Edge computing for low-latency applications
• Virtual reality for mental health therapy
• Quantum computing and its applications in finance
• Biomedical engineering and its applications
• Cybersecurity in autonomous systems
• Machine learning for predictive maintenance in transportation
• Computer vision for object detection in autonomous driving
• Augmented reality for industrial training and simulations
• Cloud-based cybersecurity solutions for small businesses
• Natural language processing for knowledge management
• Machine learning for personalized advertising
• Cybersecurity in the supply chain management
• Cybersecurity risks in the energy sector
• Computer vision for facial recognition
• Natural language processing for social media analysis
• Machine learning for sentiment analysis in customer reviews
• Explainable Artificial Intelligence
• Quantum Computing
• Blockchain Technology
• Human-Computer Interaction
• Natural Language Processing
• Cloud Computing
• Robotics and Automation
• Augmented Reality and Virtual Reality
• Cyber-Physical Systems
• Computational Neuroscience
• Big Data Analytics
• Computer Vision
• Cryptography and Network Security
• Internet of Things
• Computer Graphics and Visualization
• Artificial Intelligence for Game Design
• Computational Biology
• Social Network Analysis
• Bioinformatics
• Distributed Systems and Middleware
• Information Retrieval and Data Mining
• Computer Networks
• Mobile Computing and Wireless Networks
• Software Engineering
• Database Systems
• Parallel and Distributed Computing
• Human-Robot Interaction
• Intelligent Transportation Systems
• High-Performance Computing
• Cyber-Physical Security
• Deep Learning
• Sensor Networks
• Multi-Agent Systems
• Human-Centered Computing
• Wearable Computing
• Knowledge Representation and Reasoning
• Adaptive Systems
• Brain-Computer Interface
• Health Informatics
• Cognitive Computing
• Cybersecurity and Privacy
• Internet Security
• Cybercrime and Digital Forensics
• Cloud Security
• Cryptocurrencies and Digital Payments
• Machine Learning for Natural Language Generation
• Cognitive Robotics
• Neural Networks
• Semantic Web
• Image Processing
• Cyber Threat Intelligence
• Secure Mobile Computing
• Cybersecurity Education and Training
• Privacy Preserving Techniques
• Cyber-Physical Systems Security
• Virtualization and Containerization
• Machine Learning for Computer Vision
• Network Function Virtualization
• Cybersecurity Risk Management
• Information Security Governance
• Intrusion Detection and Prevention
• Biometric Authentication
• Machine Learning for Predictive Maintenance
• Security in Cloud-based Environments
• Cybersecurity for Industrial Control Systems
• Smart Grid Security
• Software Defined Networking
• Quantum Cryptography
• Security in the Internet of Things
• Natural language processing for sentiment analysis
• Blockchain technology for secure data sharing
• Developing efficient algorithms for big data analysis
• Cybersecurity for internet of things (IoT) devices
• Human-robot interaction for industrial automation
• Image recognition for autonomous vehicles
• Social media analytics for marketing strategy
• Quantum computing for solving complex problems
• Biometric authentication for secure access control
• Augmented reality for education and training
• Intelligent transportation systems for traffic management
• Predictive modeling for financial markets
• Cloud computing for scalable data storage and processing
• Virtual reality for therapy and mental health treatment
• Data visualization for business intelligence
• Recommender systems for personalized product recommendations
• Speech recognition for voice-controlled devices
• Mobile computing for real-time location-based services
• Neural networks for predicting user behavior
• Genetic algorithms for optimization problems
• Distributed computing for parallel processing
• Internet of things (IoT) for smart cities
• Wireless sensor networks for environmental monitoring
• Cloud-based gaming for high-performance gaming
• Social network analysis for identifying influencers
• Autonomous systems for agriculture
• Robotics for disaster response
• Data mining for customer segmentation
• Computer graphics for visual effects in movies and video games
• Virtual assistants for personalized customer service
• Natural language understanding for chatbots
• 3D printing for manufacturing prototypes
• Artificial intelligence for stock trading
• Machine learning for weather forecasting
• Biomedical engineering for prosthetics and implants
• Cybersecurity for financial institutions
• Machine learning for energy consumption optimization
• Computer vision for object tracking
• Natural language processing for document summarization
• Wearable technology for health and fitness monitoring
• Internet of things (IoT) for home automation
• Reinforcement learning for robotics control
• Big data analytics for customer insights
• Machine learning for supply chain optimization
• Natural language processing for legal document analysis
• Artificial intelligence for drug discovery
• Computer vision for object recognition in robotics
• Data mining for customer churn prediction
• Autonomous systems for space exploration
• Robotics for agriculture automation
• Machine learning for predicting earthquakes
• Natural language processing for sentiment analysis in customer reviews
• Big data analytics for predicting natural disasters
• Internet of things (IoT) for remote patient monitoring
• Blockchain technology for digital identity management
• Machine learning for predicting wildfire spread
• Computer vision for gesture recognition
• Natural language processing for automated translation
• Big data analytics for fraud detection in banking
• Internet of things (IoT) for smart homes
• Robotics for warehouse automation
• Machine learning for predicting air pollution
• Natural language processing for medical record analysis
• Augmented reality for architectural design
• Big data analytics for predicting traffic congestion
• Machine learning for predicting customer lifetime value
• Developing algorithms for efficient and accurate text recognition
• Natural Language Processing for Virtual Assistants
• Natural Language Processing for Sentiment Analysis in Social Media
• Explainable Artificial Intelligence (XAI) for Trust and Transparency
• Deep Learning for Image and Video Retrieval
• Edge Computing for Internet of Things (IoT) Applications
• Data Science for Social Media Analytics
• Cybersecurity for Critical Infrastructure Protection
• Natural Language Processing for Text Classification
• Quantum Computing for Optimization Problems
• Machine Learning for Personalized Health Monitoring
• Computer Vision for Autonomous Driving
• Blockchain Technology for Supply Chain Management
• Augmented Reality for Education and Training
• Natural Language Processing for Sentiment Analysis
• Machine Learning for Personalized Marketing
• Big Data Analytics for Financial Fraud Detection
• Cybersecurity for Cloud Security Assessment
• Artificial Intelligence for Natural Language Understanding
• Blockchain Technology for Decentralized Applications
• Virtual Reality for Cultural Heritage Preservation
• Natural Language Processing for Named Entity Recognition
• Machine Learning for Customer Churn Prediction
• Big Data Analytics for Social Network Analysis
• Cybersecurity for Intrusion Detection and Prevention
• Artificial Intelligence for Robotics and Automation
• Blockchain Technology for Digital Identity Management
• Virtual Reality for Rehabilitation and Therapy
• Natural Language Processing for Text Summarization
• Machine Learning for Credit Risk Assessment
• Big Data Analytics for Fraud Detection in Healthcare
• Cybersecurity for Internet Privacy Protection
• Artificial Intelligence for Game Design and Development
• Blockchain Technology for Decentralized Social Networks
• Virtual Reality for Marketing and Advertising
• Natural Language Processing for Opinion Mining
• Machine Learning for Anomaly Detection
• Big Data Analytics for Predictive Maintenance in Transportation
• Cybersecurity for Network Security Management
• Artificial Intelligence for Personalized News and Content Delivery
• Blockchain Technology for Cryptocurrency Mining
• Virtual Reality for Architectural Design and Visualization
• Natural Language Processing for Machine Translation
• Machine Learning for Automated Image Captioning
• Big Data Analytics for Stock Market Prediction
• Cybersecurity for Biometric Authentication Systems
• Artificial Intelligence for Human-Robot Interaction
• Blockchain Technology for Smart Grids
• Virtual Reality for Sports Training and Simulation
• Natural Language Processing for Question Answering Systems
• Machine Learning for Sentiment Analysis in Customer Feedback
• Big Data Analytics for Predictive Maintenance in Manufacturing
• Cybersecurity for Cloud-Based Systems
• Artificial Intelligence for Automated Journalism
• Blockchain Technology for Intellectual Property Management
• Virtual Reality for Therapy and Rehabilitation
• Natural Language Processing for Language Generation
• Machine Learning for Customer Lifetime Value Prediction
• Big Data Analytics for Predictive Maintenance in Energy Systems
• Cybersecurity for Secure Mobile Communication
• Artificial Intelligence for Emotion Recognition
• Blockchain Technology for Digital Asset Trading
• Virtual Reality for Automotive Design and Visualization
• Natural Language Processing for Semantic Web
• Machine Learning for Fraud Detection in Financial Transactions
• Big Data Analytics for Social Media Monitoring
• Cybersecurity for Cloud Storage and Sharing
• Artificial Intelligence for Personalized Education
• Blockchain Technology for Secure Online Voting Systems
• Virtual Reality for Cultural Tourism
• Natural Language Processing for Chatbot Communication
• Machine Learning for Medical Diagnosis and Treatment
• Big Data Analytics for Environmental Monitoring and Management.
• Cybersecurity for Cloud Computing Environments
• Virtual Reality for Training and Simulation
• Big Data Analytics for Sports Performance Analysis
• Cybersecurity for Internet of Things (IoT) Devices
• Artificial Intelligence for Traffic Management and Control
• Blockchain Technology for Smart Contracts
• Natural Language Processing for Document Summarization
• Machine Learning for Image and Video Recognition
• Blockchain Technology for Digital Asset Management
• Virtual Reality for Entertainment and Gaming
• Natural Language Processing for Opinion Mining in Online Reviews
• Machine Learning for Customer Relationship Management
• Big Data Analytics for Environmental Monitoring and Management
• Cybersecurity for Network Traffic Analysis and Monitoring
• Artificial Intelligence for Natural Language Generation
• Blockchain Technology for Supply Chain Transparency and Traceability
• Virtual Reality for Design and Visualization
• Natural Language Processing for Speech Recognition
• Machine Learning for Recommendation Systems
• Big Data Analytics for Customer Segmentation and Targeting
• Cybersecurity for Biometric Authentication
• Artificial Intelligence for Human-Computer Interaction
• Blockchain Technology for Decentralized Finance (DeFi)
• Virtual Reality for Tourism and Cultural Heritage
• Machine Learning for Cybersecurity Threat Detection and Prevention
• Big Data Analytics for Healthcare Cost Reduction
• Cybersecurity for Data Privacy and Protection
• Artificial Intelligence for Autonomous Vehicles
• Blockchain Technology for Cryptocurrency and Blockchain Security
• Virtual Reality for Real Estate Visualization
• Natural Language Processing for Question Answering
• Big Data Analytics for Financial Markets Prediction
• Cybersecurity for Cloud-Based Machine Learning Systems
• Artificial Intelligence for Personalized Advertising
• Blockchain Technology for Digital Identity Verification
• Virtual Reality for Cultural and Language Learning
• Natural Language Processing for Semantic Analysis
• Machine Learning for Business Forecasting
• Big Data Analytics for Social Media Marketing
• Artificial Intelligence for Content Generation
• Blockchain Technology for Smart Cities
• Virtual Reality for Historical Reconstruction
• Natural Language Processing for Knowledge Graph Construction
• Machine Learning for Speech Synthesis
• Big Data Analytics for Traffic Optimization
• Artificial Intelligence for Social Robotics
• Blockchain Technology for Healthcare Data Management
• Virtual Reality for Disaster Preparedness and Response
• Natural Language Processing for Multilingual Communication
• Machine Learning for Emotion Recognition
• Big Data Analytics for Human Resources Management
• Cybersecurity for Mobile App Security
• Artificial Intelligence for Financial Planning and Investment
• Blockchain Technology for Energy Management
• Virtual Reality for Cultural Preservation and Heritage.
• Big Data Analytics for Healthcare Management
• Cybersecurity in the Internet of Things (IoT)
• Artificial Intelligence for Predictive Maintenance
• Computational Biology for Drug Discovery
• Virtual Reality for Mental Health Treatment
• Machine Learning for Sentiment Analysis in Social Media
• Human-Computer Interaction for User Experience Design
• Cloud Computing for Disaster Recovery
• Quantum Computing for Cryptography
• Intelligent Transportation Systems for Smart Cities
• Cybersecurity for Autonomous Vehicles
• Artificial Intelligence for Fraud Detection in Financial Systems
• Social Network Analysis for Marketing Campaigns
• Cloud Computing for Video Game Streaming
• Machine Learning for Speech Recognition
• Augmented Reality for Architecture and Design
• Natural Language Processing for Customer Service Chatbots
• Machine Learning for Climate Change Prediction
• Big Data Analytics for Social Sciences
• Artificial Intelligence for Energy Management
• Virtual Reality for Tourism and Travel
• Cybersecurity for Smart Grids
• Machine Learning for Image Recognition
• Augmented Reality for Sports Training
• Natural Language Processing for Content Creation
• Cloud Computing for High-Performance Computing
• Artificial Intelligence for Personalized Medicine
• Virtual Reality for Architecture and Design
• Augmented Reality for Product Visualization
• Natural Language Processing for Language Translation
• Cybersecurity for Cloud Computing
• Artificial Intelligence for Supply Chain Optimization
• Blockchain Technology for Digital Voting Systems
• Virtual Reality for Job Training
• Augmented Reality for Retail Shopping
• Natural Language Processing for Sentiment Analysis in Customer Feedback
• Cloud Computing for Mobile Application Development
• Artificial Intelligence for Cybersecurity Threat Detection
• Blockchain Technology for Intellectual Property Protection
• Virtual Reality for Music Education
• Machine Learning for Financial Forecasting
• Augmented Reality for Medical Education
• Natural Language Processing for News Summarization
• Cybersecurity for Healthcare Data Protection
• Artificial Intelligence for Autonomous Robots
• Virtual Reality for Fitness and Health
• Machine Learning for Natural Language Understanding
• Augmented Reality for Museum Exhibits
• Natural Language Processing for Chatbot Personality Development
• Cloud Computing for Website Performance Optimization
• Artificial Intelligence for E-commerce Recommendation Systems
• Blockchain Technology for Supply Chain Traceability
• Virtual Reality for Military Training
• Augmented Reality for Advertising
• Natural Language Processing for Chatbot Conversation Management
• Cybersecurity for Cloud-Based Services
• Artificial Intelligence for Agricultural Management
• Blockchain Technology for Food Safety Assurance
• Virtual Reality for Historical Reenactments
• Machine Learning for Cybersecurity Incident Response.
• Secure Multiparty Computation
• Federated Learning
• Internet of Things Security
• Blockchain Scalability
• Quantum Computing Algorithms
• Explainable AI
• Data Privacy in the Age of Big Data
• Adversarial Machine Learning
• Deep Reinforcement Learning
• Online Learning and Streaming Algorithms
• Graph Neural Networks
• Automated Debugging and Fault Localization
• Mobile Application Development
• Software Engineering for Cloud Computing
• Cryptocurrency Security
• Edge Computing for Real-Time Applications
• Natural Language Generation
• Virtual and Augmented Reality
• Computational Biology and Bioinformatics
• Internet of Things Applications
• Robotics and Autonomous Systems
• Explainable Robotics
• 3D Printing and Additive Manufacturing
• Distributed Systems
• Parallel Computing
• Data Center Networking
• Data Mining and Knowledge Discovery
• Information Retrieval and Search Engines
• Network Security and Privacy
• Cloud Computing Security
• Data Analytics for Business Intelligence
• Neural Networks and Deep Learning
• Reinforcement Learning for Robotics
• Automated Planning and Scheduling
• Evolutionary Computation and Genetic Algorithms
• Formal Methods for Software Engineering
• Computational Complexity Theory
• Bio-inspired Computing
• Computer Vision for Object Recognition
• Automated Reasoning and Theorem Proving
• Natural Language Understanding
• Machine Learning for Healthcare
• Scalable Distributed Systems
• Sensor Networks and Internet of Things
• Smart Grids and Energy Systems
• Software Testing and Verification
• Web Application Security
• Wireless and Mobile Networks
• Computer Architecture and Hardware Design
• Digital Signal Processing
• Game Theory and Mechanism Design
• Multi-agent Systems
• Evolutionary Robotics
• Quantum Machine Learning
• Computational Social Science
• Explainable Recommender Systems.
• Artificial Intelligence and its applications
• Cloud computing and its benefits
• Cybersecurity threats and solutions
• Internet of Things and its impact on society
• Virtual and Augmented Reality and its uses
• Blockchain Technology and its potential in various industries
• Web Development and Design
• Digital Marketing and its effectiveness
• Big Data and Analytics
• Software Development Life Cycle
• Gaming Development and its growth
• Network Administration and Maintenance
• Machine Learning and its uses
• Data Warehousing and Mining
• Computer Architecture and Design
• Computer Graphics and Animation
• Quantum Computing and its potential
• Data Structures and Algorithms
• Computer Vision and Image Processing
• Robotics and its applications
• Operating Systems and its functions
• Information Theory and Coding
• Compiler Design and Optimization
• Computer Forensics and Cyber Crime Investigation
• Distributed Computing and its significance
• Artificial Neural Networks and Deep Learning
• Cloud Storage and Backup
• Programming Languages and their significance
• Computer Simulation and Modeling
• Computer Networks and its types
• Information Security and its types
• Computer-based Training and eLearning
• Medical Imaging and its uses
• Social Media Analysis and its applications
• Human Resource Information Systems
• Computer-Aided Design and Manufacturing
• Multimedia Systems and Applications
• Geographic Information Systems and its uses
• Computer-Assisted Language Learning
• Mobile Device Management and Security
• Data Compression and its types
• Knowledge Management Systems
• Text Mining and its uses
• Cyber Warfare and its consequences
• Wireless Networks and its advantages
• Computer Ethics and its importance
• Computational Linguistics and its applications
• Autonomous Systems and Robotics
• Information Visualization and its importance
• Geographic Information Retrieval and Mapping
• Business Intelligence and its benefits
• Digital Libraries and their significance
• Artificial Life and Evolutionary Computation
• Computer Music and its types
• Virtual Teams and Collaboration
• Computer Games and Learning
• Semantic Web and its applications
• Electronic Commerce and its advantages
• Multimedia Databases and their significance
• Computer Science Education and its importance
• Computer-Assisted Translation and Interpretation
• Ambient Intelligence and Smart Homes
• Autonomous Agents and Multi-Agent Systems.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Research Opportunities

The Allen School is committed to offering research opportunities to its undergraduate majors. Research is an exciting, and sometimes challenging, process of discovering something completely new and communicating the discovery to others. For a research result to be meaningful, it must be shared for others to apply or build upon.

Research involves many aspects: investigating prior work, experimenting, inventing, reasoning (proofs), collaboration, organization, writing, and speaking. If there is no chance of failure, it is not research. Projects can vary. Always choose one that you think you would enjoy.

Finding a Research Project

Types of research credit.

• Registration

Research Funding

Departmental honors and senior thesis, cross-departmental research.

What is ugrad research?  |  Why should I get involved in research?  |  What are the prerequisites for research?  | I don't have the prereqs! |  How can I apply?

• What is ugrad research?
• Research is a fancy way of saying 'creating new knowledge.' Researchers tackle problems that have unclear solutions and produce new ways of solving these problems.
• Ugrad research is an opportunity to learn the research mindset and build a relationship with a mentor. This mindset looks different in different subfields (theory, ml/robotics, HCI) and mentors will also have different personal styles.
• Why should I get involved in research?
• The main reason is if you want to see what research looks like as a career / think you may want a PhD. Undergraduate research is (unsurprisingly) one of the best ways to experiment with research as a career path.
• Ugrad research is an experience that is also sometimes transferrable to industry - some subfields, especially in machine learning, HCI, and ubicomp will be programming-heavy and can demonstrate experience for SWE roles.
• What are the prerequisites for research?
• This will depend a lot on the subfield you are interested in. Here are a few sample research subfields and the type of work you might encounter:
• Human-Computer Interaction : HCI researchers ask, how do humans use computers? How can we make those interactions more seamless? Better for people with disabilities? HCI research often will involve coding, user studies, and data analysis.
• Machine learning/robotics : ML/robotics researchers ask, how can we teach computers to learn? What techniques does the literature use, and how can we improve on that? ML/robotics research will often be coding heavy and may involve matrix calculus/linear algebra. Taking CSE446 (ML) and math coursework is often recommended.
• Computational/synthetic biology : comp/synth bio researchers ask, how can computational techniques advance our understanding of biology? This field is broad and may require prior knowledge in biology or an aptitude to read papers from both computer science and biology. Research may look like work in the wetlab, data analysis / visualization, or coding.
• Theory : theory researchers ask, what can we prove using math? Theory often stands alone from other research areas in that coding is infrequently needed - most of the work is reviewing literature and proving theorems. Strong performance in CSE311/421, high level math coursework, or taking graduate level theory courses is recommended.
• I don't have the prereqs! What should I do?
• Colloquia  (CSE590) are amazing ways to explore a new field, meet grad students, and see cutting edge research! Plus, you can elect to get 1 credit.
• Take the relevant classes to your subfield and/or do personal projects
• Consider summer research internships like the Research Experience for Undergrads (REUs) or internships at a national laboratory
• What subfield am I interested in? Do I want to work on something specific (e.g. improving mobile communication access for rural communities) or something broad (e.g. exploring HCI as a subfield)?
• Why am I interested in doing research? Maybe you're interested in research to a) try it out, b) explore a new subfield, or c) deepen knowledge in a subfield you're interested in.
• How has my prior experience clarified my interests and passions? Did you take a class and really liked the style of thinking? How do you approach problems?
• (*) For non-theory students : Start at cs.uw.edu/findingresearch - some faculty and labs already have an established pipeline for applicants. If you do not see a faculty/subfield of interest, go to Faculty by Expertise  to see faculty by their subfield.
• (*) For theory students : your best bet is reaching out directly to theory faculty with some topics of interest. In general, there are fewer students interested in theory research, meaning it is easier to match with grad students or faculty.

The best way to do this is to explore, and the CSE department has a number of ways to do this.

• Check out the  research project home pages  to find out what research faculty members are doing. Here is an additional page specifically made for CSE undergrads with specific information about research labs and faculty and how to get involved with them. Building connections with graduate students and asking them about projects they are working on can also be a good way to learn more about research opportunities.
• Attend Faculty Colloquia in the Fall of each year (previous colloquia are archived in the  Colloquia On-Demand  webpage).
• Talk to the faculty teaching your classes about their work, and other related work going on in the department.

Step 2: Discuss your research interests with a potential faculty sponsor.

Occasionally, faculty members and graduate students will advertise research projects for undergraduates. It is not wise simply to wait for these announcements. It is better to approach a faculty member with the knowledge of their projects and how your experience and background can benefit them. Contact them during office hours or via e-mail to set up a time to discuss  their work. If it seems like a fit, it is worthwhile: (1) to discuss the planned duration of your research (either in terms of number of credits or number of quarters) and expected outcomes (for example, if you are expected to write papers or do a presentation at the end), (2) to make a plan for when you will start, and (3) to determine if you will work for academic credit (either C/NC or graded) or for pay (not all faculty offer paid research opportunities). There are ways to work on the same project for both pay and credit, but it must be clearly articulated which hours are paid and which hours are for credit. Students may not receive both pay and credit for the same hours of research work. If you have questions, please see an academic advisor to clarify your plans.

Step 3: Register for research credits during the quarterly class registration process.

Each research credit hour carries the expectation of three hours of work per week (1 credit = 3 hours per week, 2 credits = 6 hours per week, etc.). Use the CSE research registration tool  to get the add-code you need to enter when you register for classes.

Step 4 (for students pursuing CSE or College honors): Sign up for honors.

Make sure you are familiar with the CSE honors enrollment process and expectations .

Step 5: Complete research.

Be proactive in communicating with your research advisor and in making sure project goals/requirements are clear. One of the skills developed through engagement in research is the ability to work independently; therefore, you will be expected to be somewhat self-directed. Your faculty sponsor is the one to determine if you have met the requirements and expectations of the research project, so checking in periodically to make sure you are on track is a good idea. You should turn in any results, assignments or written work to them, and they will submit your grades at the end of the quarter. Research credits are subject to the UW's numerical and letter grading system . Honors students are required to do research and write a senior thesis.

Each year a Best Senior Thesis Award is given.

NOTE: Students who wish to participate in research outside of CSE can only use it toward CSE senior electives if they get a CSE faculty sponsor and register for CSE 498/496 credit. Please discuss this with an advisor if you have questions about conducting research in another department and applying it toward CSE requirements.

CSE 498, CSE 496, and CSE 499 are used to provide you with academic credit towards your degree requirements for research activities and/or independent projects conducted under the supervision of a faculty member (see detailed descriptions below).The department strongly encourages research and independent project participation by undergraduates both as a way to sample and prepare for graduate school and to work on the leading edge of the field.

Both CSE 498  (maximum of 9 credits) and CSE 496  (maximum of 9 credits) may be used to fulfill Computer Science & Engineering electives and are graded courses. The difference between the two is that CSE 496 is for students enrolled in the University or Departmental Honors programs. CSE 499 may be used only as free elective credit and is graded credit/no-credit. You may register for CSE 499 for a quarter or two prior to fully engaging in a research project under CSE 498/496.

The number of496/498/499credits you take per quarter may vary. However, the average is 3-4 quarterly credits. Expect the workload to be approximately 3-4 hours per week per credit.

A faculty member must officially supervise all projects. A CSE graduate student or industry supervisor may, under the direction of a faculty member, also supervise your work. A faculty member is always responsible for the grading of every research project. Honors projects include an additional requirement that is laid out in detail on the honors webpage. (The content of the honors paper is determined by the student and supervising faculty. The paper is submitted as part of the final grade for the project. Since honors projects span multiple quarters, a student should receive an "X" until a final grade is submitted the last quarter of the project.)

You may not be paid an hourly salary and receive credit for the same research hours. However, if resources allow, it is possible to split research by having some hours paid and some counting towards credit.

CSE 498, 496 Research Projects

To receive graded research, you should describe a development, survey literature, or conduct a small research project in an area of specialization. Objectives are: (1) applying and integrating classroom material from several courses, (2) becoming familiar with professional literature, (3) gaining experience in writing a technical document, and (4) enhancing employability through the evidence of independent work. Your project may cover an area in computer science and engineering or an application to another field. The work normally extends over more than one quarter. Prerequisite: Permission of instructor. Students pursuing 496, honors, must complete all 9 credits, their senior thesis, and oral presentation on the same project.

CSE 499 Reading and Research (1-24)

Available for CSE majors to do reading and research in the field. Usable as a free elective, but it cannot be taken in place of a core course or Computer Science & Engineering senior elective. 499 can be a good way to experiment with a research project before committing to 9 credits of honors work or further graded research. Prerequisite: Permission of instructor. Credit/No credit.

CSE 498, 496, or 499 Registration

The type of research credits a student can enroll in is dependent on the student’s faculty mentor. The flowcharts below describe the research credits you are eligible to enroll in.

If you are a CSE major requesting research registration with an Allen School full-time faculty member, follow the instructions below:

• Log in to your MyCSE webpage.
• Scroll down the front page until you see the "Apply for Research" box.
• Check to make sure the default quarter is accurate; this is especially important when signing up for fall quarter as summer may still be listed.
• Fill in the online form requesting research. If you plan to work with a CSE grad student, you should list their faculty advisor as your research advisor on the form.
• An email will be sent to your faculty advisor, who will then go online to approve the request.
• Once the request has been approved, you will be sent an email with an add code to use to register.
• Important last step: actually REGISTER for the approved credits.

You are responsible for making sure that you do not over-enroll for more than 9 credits of graded, 498 research (9 credits allowed/required for honors).

Faculty members who have NSF research grants can apply for NSF Research Experience for Undergraduates (REU) as supplements to their existing grants. You should remind your faculty sponsor about this opportunity. This site also gives information about REU programs at other universities for which you may be eligible. The Mary Gates Endowment and the Washington NASA Space Grant Program  have research grants for undergraduates.

For full requirements on how to graduate with departmental honors, please see the departmental honors web page .

Students typically complete their thesis during their last quarter of research. Once a decision is made to pursue departmental honors, you should notify your faculty advisor and determine a topic for your senior thesis. The honors research and project should be completed with one faculty member, or, in the rare instance where you need to switch advisors, faculty within the same area of research as the original advisor.

Once the thesis is completed, one copy should be submitted to the faculty supervisor and one to the CSE undergraduate advisors. If you do not meet the honors thesis requirements, you will not graduate with honors even if  you have successfully completed nine credits of research. In many cases, faculty will not issue grades for honors research until the entire project is finished and approved.

Undergraduate Thesis Archive

All CSE honors theses, including the past winners of the Best Senior Thesis Award, are published online as part of the UW CSE Undergraduate Thesis Archive .

Students can pursue research in any department. However, if they are doing CSE-related work and wish to earn CSE research credits they must find a CSE faculty member to sponsor the research. Credit types, amounts, and grading would then be worked out between the facutly sponsor, the student, and the research advisor in the other department. This should be arranged prior to beginning a project.

Undergraduate research

The Department of Computer Science is passionate about involving students at every level in its research. We are proud to say that we have many undergraduates who do research with our faculty members.

If you are new to the idea of doing research, but not sure how to get started, Associate Teaching Professor Mark Sheldon has advice to share . Professor Sheldon’s recommendations include the Summer Scholars Program at Tufts and, outside of Tufts, Research Experiences for Undergrads (REUs). Be sure to read his recommendations in full to experience the best start to your research experience. You could also look into the Tufts Laidlaw Scholars or DIAMONDS programs.

For inspiration, read the profiles of Tufts students who have done research as undergraduates.

Book series

Undergraduate Topics in Computer Science

About this book series.

'Undergraduate Topics in Computer Science' (UTiCS) delivers high-quality instructional content for undergraduates studying in all areas of computing and information science. From core foundational and theoretical material to final-year topics and applications, UTiCS books take a fresh, concise, and modern approach and are ideal for self-study or for a one- or two-semester course. The texts are authored by established experts in their fields, reviewed by an international advisory board, and contain numerous examples and problems, many of which include fully worked solutions.

The UTiCS concept centers on high-quality, ideally and generally quite concise books in softback format. For advanced undergraduate textbooks that are likely to be longer and more expository, Springer continues to offer the highly regarded Texts in Computer Science series, to which we refer potential authors.

Book titles in this series

Concise guide to the internet of things.

A Hands-On Introduction to Technologies, Procedures, and Architectures

• Michael McCarthy
• Ian Pollock
• Copyright: 2024

Available Renditions

• Soft cover ( Book w. online files / update )

Understanding Computer Organization

A Guide to Principles Across RISC-V, ARM Cortex, and Intel Architectures

• Patricio Bulić

Computational Thinking

First Algorithms, Then Code

• Paolo Ferragina
• Fabrizio Luccio

Introduction to Artificial Intelligence

• Wolfgang Ertel

Computability and Complexity

Foundations and Tools for Pursuing Scientific Applications

Publish with us

Abstracted and indexed in.

Undergraduate Research Opportunities

Research may be part of your coursework or as as part of individual research opportunities working with professors.

Learn about Harvard CS Faculty’s research by looking at the following Google spreadsheet on Faculty Research Interests and Office Hours . In addition to information about their research, it lists their office hours. Be sure to look at the info paragraph column to get a sense of what is the background needed to get involved with each particular research group.

Also considering taking a graduate course or advanced undergraduate course as a way to gain deeper knowledge in an area you are interested in. Many undergraduates take graduate courses, and many of these graduate courses involve reading research papers and engaging in a research project. This provides a great way to get involved in research within the context of a course, often in a small class setting.

We also recommend you check out the Computer Science colloquium to get a sense for what’s going on in the world of Computer Science Research.

Another way to get involved with research is to do a CS91r or senior thesis .

Other useful resources

Harvard College Office of Undergraduate Research and Fellowships Many opportunities for funding student research, including PRISE, Herchel Smith, and the Harvard College Research Program (HCRP).

SEAS-wide info on undergraduate research and FAQ

SEAS Undergrad Research Canvas Page (events and information)

Active Learning Labs

Student Employment Office: Research Opportunities

Harvard Innovation Labs

Remote Research Resources

How to get a research-based summer internship/job

REU Programs (Research Experience for Undergraduates funded by NSF):

• http://www.nsf.gov/crssprgm/reu/reu_search.jsp

Non-REU Programs:

• Lincoln Labs/MIT
• DAAD RISE (Germany)
• AT&T Research Internships
• DOE Science Undergraduate Laboratory Internships
• DOE Scholars Program
• Caltech Summer Undergraduate Research Fellowships
• Summer Undergraduate Research Fellowships, funded by NIST
• NCAR Computational Science
• National Security Agency
• Lawrence Livermore National Laboratory
• Privacy Tools for Sharing Research Data Project
• The Mind Project
• Radcliffe Research Partnernships

Harvard College offers a variety of research funding opportunities which are administered by the Office of Undergraduate Research and Fellowships . In particular, we’d like to point out PRISE via the Summer Residential Research Programs and the Harvard College Research Program (HCRP) via Independent Research Funding .

The Kempner Institute for the Study of Natural and Artificial Intelligence offers two undergraduate research programs for Harvard College undergraduates: a term-time program (KURE) and a 10-week summer program (KRANIUM). Please see their website for more information.

Though uncommon, sometimes faculty members may be able to pay for students to work during the semester. Please be aware, though, that Harvard does not allow students to receive academic credit for work for which they were compensated .

Harvard offers a Research Experience for Undergraduates (REU) Program for students to spend their summer performing research. Other universities also participate in REU programs for those who would like to do research elsewhere, as discussed above.

Travel Funding for Workshops, conferences, coding bootcamps, and other courses.

Always apply for grants from the hosting organization and check with your research advisor regarding any available funding for research-related presentations. Failing those options, the CS Area does have a small budget to support undergraduate student conference travel to present their research, please check with the DUS team.

The CS Diversity Committee allows students to apply for conference funding in support of women and underrepresented minorities in Computer Science.

The Office of Undergraduate Research and Fellowships offers funding for conferences . The URAF conference funding program supports Harvard College undergraduate students in presenting their original, independent research (poster or paper) at an academic conference. Awards are available year-round with a rolling deadline to apply for funding. Undergraduate students from all concentrations are encouraged to apply.

If your research also falls under Life and/or Physical Sciences and your lab is difficult to get to, then you might be eligible for transportation funding to get to your lab .

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101 Best Computer Science Topics for 2023

Any student will know the difficulty that comes with developing and choosing a great topic in computer science. Generally speaking, a good topic should be original, interesting, and challenging. It should push the limits of the field of study while still adequately answering the main questions brought on by the study.

We understand the stress that this may cause students, which is why we’ve dedicated our time to search the web and print resources to find the latest computer science topics that create the biggest waves in the field. Here’s the list of the top computer science research topics for 2023 you can use for an essay or senior thesis :

AP Computer Science Topics for Students Entering College

• How has big data impacted the way small businesses conduct market research?
• Does machine learning negatively impact the way neurons in the brain work?
• Did biotech change how medicine is administered to patients?
• How is human perception affected by virtual reality technologies?
• How can education benefit from using virtual reality in learning?
• Are quantum computers the way of the future or are they just a fad?
• Has the Covid-19 pandemic delayed advancements in computer science?

Computer Science Research Paper Topics for High School

• How successful has distance learning computer tech been in the time of Covid-19?
• Will computer assistance in businesses get rid of customer service needs?
• How has encryption and decryption technology changed in the last 20 years?
• Can AI impact computer management and make it automated?
• Why do programmers avoid making a universal programming language?
• How important are human interactions with computer development?
• How will computers change in the next five to ten years?

Controversial Topics in Computer Science for Grad Students

• What is the difference between math modeling and art?
• How are big-budget Hollywood films being affected by CGI technologies?
• Should students be allowed to use technology in classrooms other than comp science?
• How important is it to limit the amount of time we spend using social media?
• Are quantum computers for personal or home use realistic?
• How are embedded systems changing the business world?
• In what ways can human-computer interactions be improved?

Computer Science Capstone Project Ideas for College Courses

• What are the physical limitations of communication and computation?
• Is SCRUM methodology still viable for software development?
• Are ATMs still secure machines to access money or are they a threat?
• What are the best reasons for using open source software?
• The future of distributed systems and its use in networks?
• Has the increased use of social media positively or negatively affected our relationships?
• How is machine learning impacted by artificial intelligence?

Interesting Computer Science Topics for College Students

• How has Blockchain impacted large businesses?
• Should people utilize internal chips to track their pets?
• How much attention should we pay to the content we read on the web?
• How can computers help with human genes sequencing?
• What can be done to enhance IT security in financial institutions?
• What does the digitization of medical fields mean for patients’ privacy?
• How efficient are data back-up methods in business?

Hot Topics in Computer Science for High School Students

• Is distance learning the new norm for earning postgraduate degrees?
• In reaction to the Covid-19 pandemic should more students take online classes?
• How can game theory aid in the analysis of algorithms?
• How can technology impact future government elections?
• Why are there fewer females in the computer science field?
• Should the world’s biggest operating systems share information?
• Is it safe to make financial transactions online?

Ph.D. Research Topics in Computer Science for Grad Students

• How can computer technology help professional athletes improve performance?
• How have Next Gen Stats changed the way coaches game plan?
• How has computer technology impacted medical technology?
• What impact has MatLab software had in the medical engineering field?
• How does self-adaptable application impact online learning?
• What does the future hold for information technology?
• Should we be worried about addiction to computer technology?

Computer Science Research Topics for Undergraduates

• How has online sports gambling changed IT needs in households?
• In what ways have computers changed learning environments?
• How has learning improved with interactive multimedia and similar technologies?
• What are the psychological perspectives on IT advancements?
• What is the balance between high engagement and addiction to video games?
• How has the video gaming industry changed over the decades?
• Has social media helped or damaged our communication habits?

Research Paper Topics in Computer Science

• What is the most important methodology in project planning?
• How has technology improved people’s chances of winning in sports betting?
• How has artificial technology impacted the U.S. economy?
• What are the most effective project management processes in IT?
• How can IT security systems help the practice of fraud score generation?
• Has technology had an impact on religion?
• How important is it to keep your social networking profiles up to date?

More Computer Science Research Papers Topics

• There is no area of human society that is not impacted by AI?
• How adaptive learning helps today’s professional world?
• Does a computer program code from a decade ago still work?
• How has medical image analysis changed because of IT?
• What are the ethical concerns that come with data mining?
• Should colleges and universities have the right to block certain websites?
• What are the major components of math computing?

Computer Science Thesis Topics for College Students

• How can logic and sets be used in computing?
• How has online gambling impacted in-person gambling?
• How did the 5-G network generation change communication?
• What are the biggest challenges to IT due to Covid-19?
• Do you agree that assembly language is a new way to determine data-mine health?
• How can computer technology help track down criminals?
• Is facial recognition software a violation of privacy rights?

Quick and Easy Computer Science Project Topics

• Why do boys and girls learn the technology so differently?
• How effective are computer training classes that target young girls?
• How does technology affect how medicines are administered?
• Will further advancements in technology put people out of work?
• How has computer science changed the way teachers educate?
• Which are the most effective ways of fighting identify theft?

Excellent Computer Science Thesis Topic Ideas

• What are the foreseeable business needs computers will fix?
• What are the pros and cons of having smart home technology?
• How does computer modernization at the office affect productivity?
• How has computer technology led to more job outsourcing?
• Do self-service customer centers sufficiently provide solutions?
• How can a small business compete without updated computer products?

Computer Science Presentation Topics

• What does the future hold for virtual reality?
• What are the latest innovations in computer science?
• What are the pros and cons of automating everyday life?
• Are hackers a real threat to our privacy or just to businesses?
• What are the five most effective ways of storing personal data?
• What are the most important fundamentals of software engineering?

Even More Topics in Computer Science

• In what ways do computers function differently from human brains?
• Can world problems be solved through advancements in video game technology?
• How has computing helped with the mapping of the human genome?
• What are the pros and cons of developing self-operating vehicles?
• How has computer science helped developed genetically modified foods?
• How are computers used in the field of reproductive technologies?

Our team of academic experts works around the clock to bring you the best project topics for computer science student. We search hundreds of online articles, check discussion boards, and read through a countless number of reports to ensure our computer science topics are up-to-date and represent the latest issues in the field. If you need assistance developing research topics in computer science or need help editing or writing your assignment, we are available to lend a hand all year. Just send us a message “ help me write my thesis ” and we’ll put you in contact with an academic writer in the field.

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Undergraduate Research

There are a variety of research opportunities for undergraduate students at the University of Michigan. In fact, about 150 undergraduate students conduct research on EECS faculty projects in a typical year; many of these are paid positions. Below you will find some of the research opportunities open to undergraduate students. See the bottom of the page for tips on how to get involved.

Independent research projects

Students are encouraged to contact individual faculty about doing independent research in an area of mutual interest . EECS 399 and EECS 499, Directed Study, can be taken for 1-4 credits. It provides an opportunity for undergraduate students to work on substantial research problems in EECS or areas of special interest such as design problems. For each hour of credit, it is expected that the student will work an average of three or four hours per week and that the challenges will be comparable with other 400 level EECS classes. An oral presentation and/or written report will be due at the end of the term.

Please note:

• If a student gets approved for an EECS research project after the drop/add deadline, they can submit a late add request in Wolverine Access to get added to the appropriate section of EECS 399 or 499.
• Students can only enroll in one section of EECS 399 or EECS 499 per term.
• CS-LSA Honors students cannot enroll in EECS 443 and EECS 499 in the same term.

Steps to take to sign up for independent research

• Students are responsible for connecting to EECS faculty members to find upcoming research opportunities (for tips on identifying research areas or connecting with faculty see the tips section at the bottom of the page).
• Brief description of your project
• How will you be evaluated?
• Will materials from other classes you have taken be used in the project?
• How often will you meet with your Faculty Director?
• How will the completion of your project be determined?
• Fill out and submit the EECS independent research form .
• Your Faculty Director must approve your submission before you can enroll.
• Faculty independent study section numbers

Multidisciplinary Design Program (MDP)

The Multidisciplinary Design Program provides team-based, “learn by doing” opportunities through participation on ongoing faculty research teams. With MDP, you can: apply what you learn in class to engineering research; gain the technical and professional skills necessary to thrive in engineering research or professional settings; and experience how people from multiple disciplines collaborate within a team. In addition to skilled technical roles, teams offer Apprentice Researcher positions for first and second year students to develop their skills through mentoring by experienced members of the team. A minimum of two semesters participation (2 credits per term) is required.  Students are encouraged to participate on their team throughout their degree. Experienced MDP students have presented at research and professional conferences, participated in University patents, and co-authored publications. Experienced students have also taken on leadership roles on their teams.

The MDP application opens in September and is due mid-October; projects begin in January and end in December (summer is generally not included). For more information about how to apply to an MDP research team, please visit here or contact [email protected] .

Summer Undergraduate Research in Engineering (SURE) Program

The Summer Undergraduate Research in Engineering (SURE) offers summer research internships to outstanding undergraduate students who have completed their sophomore or junior year (preference will be given to those who have completed three years of study) by the time of their internship. Participants have the opportunity to conduct 10-12 weeks of full-time summer research with an EECS faculty member on a research project defined by the faculty. Applicants for EECS SURE projects should list on the application their top three areas of interest in preference order.

• List of SURE projects in CSE (2023-2024)
• List of SURE projects in CSE (2022-2023)
• List of SURE projects in CSE (2021-2022)
• List of SURE projects in CSE (2020-2021)
• List of SURE projects in CSE (2019-2020)
• List of SURE projects in CSE (2018-2019)

Undergraduate Research Opportunity Program (UROP)

The Undergraduate Research Opportunity Program (UROP) creates research partnerships between first and second year UM students and faculty. All schools and colleges at the University of Michigan are active participants in UROP, which provides a wealth of interesting research topics for program participants. There are two different ways to engage in UROP research: either throughout the course of an academic year or through a 10-week summer research project. For more information about these research opportunities, contact [email protected] .

Summer Research Opportunity Program (SROP)

The Summer Research Opportunity Program (SROP) is designed for outstanding non-UM students entering into their 3rd or 4th year of undergraduate study and who are underrepresented within their field. The goal of this program is to provide students with the opportunity to conduct an intensive graduate level research project with faculty and graduate students at the University of Michigan. This eight-week program, held on the Ann Arbor campus, culminates in a research symposium where each participant presents their research project. Throughout the program, all students will engage in a series of academic, professional, and personal development seminars. For more information about eligibility requirements, benefits, and the application process, visit here or contact rackham.umich.edu .

Tips for getting involved in research

Research is a cornerstone of academia. The pursuit of new knowledge is one of the main factors that motivates students to attend the University of Michigan. However, stepping into the world of research can feel overwhelming, especially if you’re not sure where to begin. This guide is intended to help CSE students feel empowered to engage in some form of research during their undergraduate studies at the University of Michigan.

• Start with what interests you! Your interests might be centered around questions, or topics, or methods, and they may be specific or broad. There is no right way to start—the identification or formulation of specific scientific research questions or ideas will come later. 
• Spend time learning about faculty research interests from their own personal and lab web sites.  Most department web sites allow for keyword searches, and you can always use Google and include “University of Michigan” and a department name in the search. Remember, there is no one right way to start.. and the results of your initial search will help you formulate new searches.
• Go to professors’ office hours. Ask them about their own research projects and find out what most excites them right now in their science. Ask them how they got started in research. You can do a lot to prepare yourself to get the most out of these meetings. Read the “Contacting Professors and Potential Project Advisors” for more information.
• Attend extracurricular lectures, symposia, and speaker sessions. Going to these types of events are good ways to see what topics academics and professionals are exploring in their fields and may even give you ideas for projects, or even people you would like to work with in the future.
• Check out the library!  Campus libraries have incredible resources beyond books. You can set up an appointment with a librarian to learn how to search for scholarly sources, how to develop a research question, and even how to read empirical research articles. Ever heard of JSTOR, Google Scholar or Interlibrary Loan?
• Take research methods and/or additional statistics classes. Many of these courses will give you tools you will frequently need when working in a laboratory or collecting your own data!
• Contact Professors and potential Project Advisors . Reaching out to faculty members for the first time can be intimidating. You may not know exactly what your own research interests are, how formal your conversation should be, or may have never even spoken to a professor one-on-one outside of class before! You can find suggestions for interacting with faculty members here .

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200+ Computer Science Research Project Ideas for College Students in Kenya

The future depends on computational technologies and there is no better time to be a computer scientist than now. Here are some of the interesting computer science projects and research topics you can consider for your academic (or non-academic) work. Have fun selecting and building the projects.

Cyber Security Research Project Ideas for College Students

• Effective encryption technology and techniques
• The need for data security and cloud computing
• The prevention of data loss
• Tracing breaches to their source by using behavioral analytics
• The use of security assertion make up language to regain corporate traffic
• Necessity of access management
• Techniques and tools of hackers
• Handling messaging threat
• Proven ways to detect emerging threats
• Strategies of risk management
• Mitigating against DDoS attacks
• Improving network service visibility
• Evaluating and managing of IoT security issues
• Curbing serverless security issues
• Use of firewalls to prevent network crimes
• The relationship between files download and computer security
• Justification for building reliable anti-malware devices
• How cookies make computer security vulnerable
• Necessary internet antivirus software for commercial purposes
• History, effect, and remedies of ransomware
• Detection and prevention of attacks by anti-malware software
• How top operating systems implement security systems
• Ensuring privacy of online dating apps users
• Advantages and disadvantages of unified user profiles
• Learning safe internet habits and why it is important
• Reasons for the bring your device (BYOD) policy
• Why the clean desk policy remains indispensable
• The danger of social networking
• The implications of malware on devices
• Cyber security and children
• The need for secure passwords on online platforms
• Effective self-protection strategies against cybercrime
• Getting rid of malware on personal computers
• Data Breaches: How they happen
• Software patches and updates: Why they are important for cyber security
• How to secure one’s digital footprint online
• Effective scam detection methods on the internet
• Security of synchronized devices
• Exploring the reasons for cyber crimes
• The importance of social engineering
• Early detection and prevention of network intrusion
• The essence of coding viruses
• Installation of applications on mobile phones, tablets, and computers
• Security precautions needed for the safe running of Windows, Unix and macOS computers
• Optimizing lost data restoration to prevent loss of vital information
• Evaluating and optimizing the processes involved for user authentication

Interesting Computer Science Design Project Ideas for Finalists

• Application of face detection technologies in crime deterrence
• The role of an online auction system in preventing bribery
• Application of computing technologies to improve academic performance
• Shortcomings of the e-authentication systems
• Effects of basing a system’s object movement on RGB
• Application of data mining algorithms in crime prediction
• Vitality of patent rights when developing computer systems
• Application of computer science knowledge in social sciences
• How can YouTube enhance system design and development?
• Enhancing the web design process
• Application of the android battery saver system

Computer Science Project Ideas for Forward Thinking Students

• Effects of using chatbots on company’s response systems
• How Kenya’s education system is enhancing computer science innovations
• The role of coding skills in system design and development
• Latest inventions in the CCTV sector
• Implications of 5G technology and associated innovations
• The role of biometric databases in busy workplaces
• Enhancing traffic flow through computer assisted systems at the toll stations
• How computers can ease traffic in busy and congested cities
• Trends in mobile phone systems: A case study of Android
• The role of computers in enhancing healthcare systems
• How computer systems can cause harm to a society
• How computer science innovations shape the world
• The role of computer science in vaccine development and administration
• How computer systems have led to the loss of human labor
• The effects of having robots on the streets
• How terrorists are using computer science to identify and attack their targets
• Computer systems in developed versus developing nations
• Implications of having CCTVs in public places
• Why does the government have the right to access personal data on databases?
• The effects of having distributed server systems in different countries
• Working from the cloud: Its effects on distributed work systems
• The impact of computer science symposiums and conferences
• Why universities should enroll more students in computer science fields

Genius Computer Science Project Ideas for High Achievers

• How to develop mobile apps for matching fingerprints
• Using computer science to develop flowcharts
• Evaluate the naming rules and conventions in Computer Science
• Compare and contrast between dynamic and static typing
• Procedural 3D tree creation in computer science and its effects
• Create a basic program structure from scratch
• The syntax rules and pseudo-codes for programs
• How to effectively conduct documentation, comments, and coding styles
• How is scoping essential in the study of Computer Science?
• Order of precedence in computer science
• Identification and use of numeric operators in computer science
• Effectiveness of cloud computing in saving on computer storage
• How to operate complex computer systems
• Procedure of conducting conformance testing
• Necessity of data and informatics in the world today
• The role of computational science in a pandemic
• Effects of breaches in cyber-physical systems
• Application of computer science in cancer treatment
• How often should companies conduct interoperability testing?
• Factors considered in conducting a successful software research
• The role of computer science in video analytics
• How IT has transformed voting systems in Kenya
• Usability and human factors in computer systems
• Effects of virtual/augmented reality
• How computer systems invade privacy without the user’s knowledge
• Should websites request personal information from users?
• Effects of cybersecurity policies in developed countries
• How IoT is changing the world
• The role of computer science in globalization
• How computer science enhances sporting activities
• Preservation of culture through computer science
• Impacts of over-reliance on computer systems in a company

Stellar Computer Science Project for Exemplary Final Year Project

• Visualization of scientific data through IT
• Importance of integrating IT in social and physical sciences
• The role of artificial intelligence in economic growth
• New risks that IT brings to the world today
• The role of innovation hubs in developing inventions
• Effects of Robot Process Automation in industries
• Effectiveness of using CAPTCHA in deterring spam on websites and applications
• How to effectively implement honey pot for non-obtrusive spam deterrence
• How is edge computing affecting the world?
• The role of quantum computing in qualitative analysis
• Discuss the part of blockchain in computing
• How 5G will transform the mobile industry in Africa
• Analyze the various techniques for processing statistical data
• The role of the US as an international data hub and its implications to the global economy
• The human brain versus a computer’s processor
• Are computer robots going to replace human labor?
• The place of compassion and empathy in computing
• Compare various operating systems
• Latest hacking techniques used in espionage and cyberbullying
• How can the government regulate computer usage without infringing on user’s rights of expression?
• How do manufacturers determine the RAM and ROM of a particular mobile phone?
• How developers work with programmers to achieve a computer system
• The effects of free WIFI on hacking and data protection policies in Kenya
• Implications of clearing your caches immediately after use
• Why is Windows operating system more popular than Linux and Ubuntu?
• Troubleshooting recursive transition networks in computing
• Drawbacks of the substitution model of evaluation
• Why should developers care about the history of computing machines?
• How to determine the analyzing procedures: A case of input size
• Interface layers: Hardware, operating system, and applications
• History and pragmatics of the Java platform
• The essence of systematic knowledge in computer science
• What it takes to be a skilled programmer
• Difficulties encountered in networking and distributed computing
• Challenges involved in human-computer interaction
• What are search algorithms and how do they work?
• Explain the evolution of search algorithms
• The hazards of most computer viruses
• Is SCRUM methodology the best computer science invention?
• How useful is networking in the development of future computer systems?
• Evolution of AI over the years
• How unique is software development for mobile gadgets?
• Pros and cons of cloud storage
• Limits of computation and communication
• Practical ways to identify lapses and improve computer data security
• Discuss database management and architecture
• Relationship between computer science and {a subject of interest}
• Privacy, memory, and security in the cloud storage era
• Overview of quantum computing and its future
• How can DDOS attacks be prevented? What are the hazards?
• Why is having several programming languages important?
• Importance of usability in human-computer interactions

Some Interesting Topics in Computer Science You Might Like

• Connection between human perception and virtual reality
• The future of computer-assisted education
• High-dimensional data modeling and computer science
• Use of artificial intelligence and blockchain for algorithmic regulations
• Computer science: Declarative versus imperative languages
• Discuss blockchain technology and the banking industry
• Parallel computing and languages- Discuss
• Use of mesh generation in computational domains
• How can a persistent data structure be optimized?
• Effects of machine architecture on the coding efficiency
• What is phishing and how can it be eliminated?
• Overview of software security
• The most efficient protocols for cryptography
• Effects of computational thinking on science
• Network economics and game theory
• Systems programming languages development
• Computer graphics development
• Cyber-physical system versus sensor networks
• Non-photorealistic rendering case in computer science
• Programming language and floating-point

Interesting Computer Science Research Topics for Undergraduates

• Can computers understand natural and human language?
• How relevant is HTML5 technology today?
• Role of computers in the development of operations research
• What is the Internet of Things? How does it impact life?
• Can AI diagnosis systems be an alternative to doctors?
• Benefits of VOIP phone systems
• How data mining can help in fighting crime
• Advantages and disadvantages of open-source software
• Advanced web design technology and how it benefits visually impaired persons
• Applications and roles of artificial intelligence
• Application of micro-chips in pet security
• Application of the computer science knowledge to explain time travel
• Computer gaming and virtual reality
• Advantages and disadvantages of blockchain technology
• Analyze ATMs and advanced bank security
• Advantages and disadvantages of biometric systems
• How to improve human-computer interactions
• Advancement and evolution of torrents in the data sharing field
• Quality elements in digital forensics
• Relationship between computer games and physics
• Discuss the principles of computer programs and programming
• What is ethical hacking? Discuss its importance.
• Discuss advanced computer programs and programming systems
• Importance of big data analysis for an established business
• Neutral networks and deep learning
• Fate of robotics, computers, and computing in the next x years

Controversial Research/Project Topics in Computer Science

• Long-term effects of sustained computer usage
• Effects of growing up in a computer-driven world?
• Discuss (with a relevant example) a privacy-centric operating system
• Potential threats of the new computer viruses
• How does virtual reality impact human perception? What are the pros and cons?
• Challenges facing data security
• Over-reliance on computers has made people less social
• Online medicine applications cannot substitute real doctors. Discuss
• Discuss the future of the 5G wireless systems
• How computer science facilitates gene editing
• Discuss why log in sites should not request users for personal data
• Do eye biometrics cause cancer?
• Effects of computing on critical thinking
• Are computers causing more harm than good today?
• Should elementary school children use computer systems for study?
• Differences between functional and imperative programming
• Philosophical controversies in computer engineering
• Effects of solid encryption on system security
• Does phishing amount to unlawful/unethical discrimination?
• Effects of the ‘big data’ on people’s privacy

Research Topics in Computer Science for PhD’s

• Ethical issues surrounding the use of big data banks to store human DNA
• Can computer application lead to human worker obsolescence?
• Application of computer science to solve health problems
• The future of quantum computers
• Computer viruses and associated risks/hazards
• Application of robotics and artificial intelligence in enhancing human capabilities
• Application of latest computing technologies in education
• Business process modeling technology
• Big data analytics
• The working principle of machine learning and pattern recognition
• Using machine learning to analyse medical images
• Distributed computing and algorithms
• Audio, language, and speech processing
• Computer security and forensics
• Communication and computation limits
• Environments and programming languages
• Computer systems security and support for the digital democracy

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Undergraduate Research Opportunities

Did you know that there are many opportunities in the CS department for undergraduates to participate in cutting-edge research?

• If a project is offered by a professor in the CS Department or by an affiliated professor , you can take 0-6 credits of CSE 487 (Research in Computer Science) over several semesters, 3 credits of which can serve as a CSE Technical Elective requirement used to satisfy CSE major requirements. Apply here .
• If a project is offered by a professor in the CS Department or by an affiliated professor , you can take 0-6 credits of ISE 487 (Research in Information Systems) over several semesters, 3 credits of which can serve as an upper division requirement or a specialization requirement used to satisfy ISE major requirements. Apply  here .
• Alternatively, you can participate in a research project via the VIP program (Vertically Integrated Projects). If you complete 3 upper-division credits of VIP research that contains a substantial computing component, those 3 credits may substitute as a CS technical elective. Note that CSE 487 and VIP credit may not both be used to satisfy CS major requirements.
• Finally, students in the CS Honors Program are required to complete a senior honors research project under CSE 495/496.

Whatever your situation may be, browse the research opportunities below and consider contacting the professor sponsoring the project if you are interested and meet the minimum qualifications.

NSF Research Experience for Undergraduates (REU) in Big Data

Project supervisor:  Fusheng Wang

We have multiple openings for NSF funded Research Experience for Undergraduates (REU). The program is open to U.S. citizens and permanent residents who are undergraduates majoring in computer science or informatics, or a related field. The REU projects are extensions of the NSF CAREER project "High Performance Spatial Queries and Analytics for Spatial Big Data" and "CIF21 DIBBs: Middleware and High Performance Analytics Libraries for Scalable Data Science". Students will conduct interdisciplinary research crosscutting computer science and biomedical informatics. Example projects include (not limited to): 1) Understanding COVID-19’s Impact on Opioid Misuse Using Social Media; 2) Incorrect Face Mask Wearing Detection and its Deployment Using Amazon DeepLens; 3) Geospatial-temporal Patterns Analysis of 30-day Readmissions of Patients in New York State; 4) Cancer Burdens in Long Island at Census Tract Level; 5) 2020 US Presidential Election and its Correlation Factors (in NYS) at High Spatial Resolution.

You can either start to work immediately (hourly based), or work full-time from June 1 to August 6. You will work in the Lab of Data Management and Biomedical Data Analytics, directed by  Dr. Fusheng Wang . We invite you to join a community of undergraduate researchers, graduate students and faculty to develop innovative solutions for processing, managing, and analyzing large scale data.

Application to the REU Program

The program is open to U.S. citizens and permanent residents who are undergraduates majoring in computer science or informatics, or a related field, with a GPA > 3.0/4.0. Applicants must have completed at least their freshman year. You must still be in a student status at the time of the research. Potential students submit an application form, unofficial transcript, one letter of recommendation, a personal statement on research interest, previous research experiences, and any coursework relevant to their research interests.

Selection will be rolling based.

Please fill the 1)  application form , 2) uploading an essay, CV and unofficial transcript. (You need a GMail account to sign in.) 3) Please have a letter of reference sent to  [email protected]

The REU Experience

The REU program at Stony Brook University “Big Spatial and Image Data Analytics” is an opportunity for qualified, academically talented and motivated undergraduate students interested in eventually pursuing their doctor degree in Computer Science or Biomedical Informatics. The program provides the student an intensive research experience with leading researchers in the field.

The program is expected to run from June 1 to August 6, 2021. For Stony Brook Students, working hourly in Spring is welcome. The REU student will participate in a research project mentored by Dr. Fusheng Wang and his Ph.D. students, and become fully integrated in the research group. The student will attend weekly research meetings, and present the research results. The student will also attend academic development workshops co-located with other Stony Brook University REU site, and have the opportunity to present a poster at the REU symposium.

The participant will receive a stipend of $500 per week, for a total of $5000 summer stipend for full-time research (or hourly if working from Spring, up to $5000).

Systems and Security

Computer Architecture

Project supervisor:  Michael Ferdman

You will help to publish a paper on a custom memory allocator that leverages CPU caches to improve performance of software. On the R&D spectrum, this is all the way on the R side; a research paper will be submitted for publication in this project.

Minimum qualifications: Applicants should have aced CSE 220 and CSE 320 and have a solid understanding of assembly programming.

Linux Administration Guru

You will figure out how to bring up a private cloud based on OpenStack with several hundred servers. On the R&D spectrum, this falls on the D side, not publishable research, but rather advanced development work.

Minimum qualifications: Applicants should have experience with Linux administration and networking.

Computer Systems and Storage

Project supervisor:  Erez Zadok

We seek to recruit qualified undergraduate CS students each semester to join a project involving broadly computer operating systems and storage systems. Projects often touch on topics of operating systems, data storage, networking, security, machine learning, performance, and more. Students will get an opportunity to work on cutting-edge research topics alongside graduate students and faculty, and even publish papers and attend conferences.  Qualifications expected: C/C++ experience, passed CSE-306 (OS) or CSE-320, and CSE-373 (Algorithms), or equivalent. Students who join the project can receive project credit or be paid for their time ($15/hr minimum, available to domestic students under the NSF REU program); salary is commensurate with experience. To apply, use the following Google Form (where you'd be asked to upload your up-to-date resume):  here

Artificial Intelligence, Machine Learning and Related Areas

EyeCanDo: Eye Gaze-based Communication for Patients with Motor Disabilities

EyeCanDo is an eye gaze-based app running on iPhone/iPad that can advance the communication of patients with motor disability and improve their quality of life. It combines augmented reality, human-computer interaction and AI to 1) achieve high accuracy and stability, 2) provide multiple levels of communication – from basic needs to reading, messaging, typing and entertainment, and 3) automatically adapt to each individual for a smooth user experience.

We are looking for students with strong motivation on mobile app development (iOS) to work on the project.

Computer Vision and Machine Learning for Object Detection and Counting

Project supervisor:  Minh Hoai Nguyen

The project's goals are to develop computer vision and machine learning models to detect and count objects in images, and subsequently optimize the models to fit into low-resource devices such as smart watches and phones. Through this project, students will have opportunities to learn cutting edge computer vision and machine learning algorithms, develop their research skills, and strengthen their coding competency.

Minimum qualifications: Must have completed either Fundamentals of Computer Vision (CSE 327) or Machine Learning (CSE 353) with a grade A- or better. Must have a GPA of at least 3.7. Experience with Android programming is desired, but not required.

Natural Language Processing (NLP)

Project supervisor:  Niranjan Balasubramanian

The LUNR lab works on many problems in NLP including question answering, common-sense reasoning, building energy and compute-efficient NLP models, mobile NLP, NLP to assist formal verification of software, language generation, and much more. Below is a listing of projects that the lab is currently pursuing:

Modeling common-sense knowledge: The goal here is to augment NLP models with common-sense knowledge about events. In particular, we are interested in understanding why events happen, what enables events, what are the goals of actors in certain scenarios, and predicting what events are likely to happen in the future. 

Efficiency and Energy Consumption of NLP models: The goal here is to improve the efficiency of NLP models and to understand the energy consumption patterns of these models. These are critical as NLP models are becoming larger taking up exorbitant amounts of compute and energy.

NLP techniques for formal verification of software: The goal here is to translate specifications laid out in natural language into formal statements that can then be used for analysis and verification. This is a challenging problem that requires human-in-the-loop systems. Our team looks at developing deep learning systems trained on synthetic data. 

Machine Learning for Optimization: The goal of this project is to design ML algorithms that can help improve search solutions for optimization problems. The search algorithms (e.g. branch-and-bound) usually have multiple parameters that are typically  predetermined before the search begins. In this project we are developing ways to make these choices dynamically  using predictions from a ML model. 

Other projects: The lab also works on building Question Answering systems, summarizing biomedical pathways that explain how entities affect certain biochemical processes, multimodal projects with vision, and many more areas. 

If you are interested in doing research in Natural Language Processing, Machine Learning or broadly in AI, please contact Prof. Balasubramanian via email.  

Learning from Human Movement using Wearable Devices

Project supervisor: Shubham Jain

The project's goals are to learn human movement using wearable devices, such as smartwatches, phones, and earphones. We use the signals to learn information about the user, such as emotion, fitness levels, and whether or not they are performing exercise correctly.  Through this project, students will have opportunities to learn cutting edge data processing  and machine learning algorithms, develop their research skills, and strengthen their coding competency. The project will also include application development on Android/iOS/Fitbit etc.

Minimum qualifications: Experience with Android or iOS programming is required.

Research Programmer for AI-based Mental Health Research App

Project supervisor: Andrew Schwartz

The Human Language Analysis Lab seeks undergraduates interested in systems plus AI to develop cell phone apps to be used for developing and evaluating state-of-the-art AI-based mental health (depression, anxiety, post-traumatic stress) assessment. The undergraduate research programmer will implement approaches to collect daily video, audio, and language interviews, as well as implement data transfer and preprocessing pipeline to insure high-quality inputs to AI-based predictive models  (Transformer sequence models).  The research programmer will get to learn multiple aspects of modern human-centered natural language processing (previous experience is good but not required). The positions are for Fall 2022 with potential renewal for Spring 2023.

Advanced Programming Methods & Applications

Projects supervisor: Annie Liu

Four main project examples, all involving programming with Python and advanced features:

Learning a systematic method for algorithm design and program optimization, and implementing automatic program analysis and optimizations.

Developing high-level queries and efficient implementations for access control and trust management.

Implementing a distributed algorithm and checking safety and liveness properties.

Automating graphics and visualization, for developing visual stories and teaching algorithms.

Minimum qualification: Being good at algorithms and programming, and enjoying learning and thinking about design and automation.

Algorithms and Theory

Projects supervisor:  Pramod Ganapathi

Mathematical Puzzles

The goal of this project is to deeply understand various counterintuitive mathematical and algorithmic puzzles and all ways of solving them. The work includes reading web articles, reading books, reading papers, analyzing the pros and cons of existing solutions, developing new solutions if possible, coding and experimenting with the solutions to analyze new patterns, generalizing the puzzles, understanding different variants of the puzzles, and documenting. This work will be part of a new book on mathematical puzzles.

Minimum qualifications: Applicants should be strong in mathematics.

Organized Algorithmic Problem-Solving

The goal of this project is to understand the underlying organization/structure/template/pattern among several algorithms or solutions that use the same algorithm design technique or problem-solving strategy. The work includes reading web articles, reading books, analyzing and writing hundreds of algorithms for different classes of problems using common structures or templates, adding these algorithms to a website (using technologies such as github, jupyter, html, latex, etc), and creating beautiful visualizations for the recurrences used by the algorithms. The final product is a website that teaches organized algorithmic problem-solving and might be helpful to thousands of students, professionals, and teachers, to learn/teach algorithms in an organized way.

Minimum qualifications: Applicants should be strong in algorithms and programming.

Interdisciplinary Research

Single Cell Sequencing to Identify Novel Genes in Models of Kidney Disease

Project Supervisor: Dr. Sandeep Mallipattu ( [email protected] )

This research project involves utilizing single-cell RNA sequencing to identify novel genes involved in mediating kidney injury and regeneration. The student will use basic programming (R, etc.) to interrogate large sequencing datasets to study cell to cell interactions in the kidney. They will also work with a team of post-doctoral fellows and research scientists to help validate the signaling pathways in the kidney regulated by these genes. This project is ideal for students interested in interrogating large data sets, with an interest in pursuing a career in medicine and/or biomedical research.

Research funding: Supported by NIH and Veterans Affairs. Students committing to project will be provided with a stipend.

Bioengineering Education, Application and Research (BEAR)

Project supervisor:  Richard McKenna

The project's goals are to develop innovative solutions for Bioengineering education, application and research based on iterative engineering design processes and cutting-edge tools; produce tangible outcomes that can be applied and measured; and promote entrepreneurship activities with the collaboration of science and non-science majors.

See the  project's page  on the Vertically-integrated Project website for more information and to apply.

PoliTech: Automated Redistricting System

Project supervisor:  Robert Kelly

The Stony Brook PoliTech project is a multidisciplinary research project that examines various aspects of Congressional redistricting. The project combines work of interest to Computer Science, Political Science, Applied Math, Psychology, Sociology, and others. At the heart of the research is the Stony Brook University Automated Redistricting System (ARS), which provides for the rapid generation of statewide congressional districts in accordance with constitutional and court-ordered guidelines, as well as user-defined preferences. For Computer Science majors, PoliTech explores efficient large scale graph partitioning algorithms, visualization of political and demographic data in a geographic context, and probabilistic assessment of districting plans.

Minimum qualifications: U2 status, 3.0 cumulative GPA

See the  project's page  on the Vertically-integrated Project website and this flyer for more information and to apply. Students may instead participate through CSE 487 if they wish.

Interactive Visualization Development for Cellular Neurophysiology Textbook

Project supervisor: David McKinnon ( [email protected] )

We have created an open source textbook that uses interactive data presentations. The textbook can be found here: Examples of interactive graphs we have created can be found here , here and here . The graphs are created using javascript and snap.SVG. These graphs need to be lightweight and load quickly to maintain attention. Data visualization on the web is an increasingly important part of web development. Although there are many existing graphing libraries much of the time custom solutions are required, as was the case here. This project is an opportunity to get some experience creating custom interactive data visualizations. Our open source textbook is a mature project that will continue to exist indefinitely since it is hosted by the university library as part of their permanent collection. If you are interested in this project you will be able to point to any contribution that you make to the site as an example of your work in this area.

Research opportunities

The Cheriton School of Computer Science provides several exciting part-time and full-time research opportunities to its undergraduate students. These awards provide students with the opportunity to gain experience in research by working part-time or full-time for a term with a faculty member in their area of interest. Undergraduate students should consider pursuing awards like the NSERC USRA if they are interested in research or help them decide whether they want to continue their studies on a graduate level. 

• Undergraduate Research Assistant (URA)
• Natural Sciences and Engineering Research Council - Undergraduate Student Research Award program (NSERC USRA)
• Undergraduate Research Fellowship (URF)
• Mathematics Undergraduate Research Assistance (MURA) offered by the Faculty of Mathematics

Students in Research

Daekun kim receives 2022 jessie w.h. zou memorial award.

Daekun Kim , a third-year software engineering student, has received the 2022   Jessie W.H. Zou Memorial Award for Excellence in Undergraduate Research . Established in 2012, the $1,000 annual award recognizes excellence in research conducted by an undergraduate student in the Faculty of Mathematics... Read more

Cheriton School of Computer Science students receive 2022 CRA Outstanding Undergraduate Researcher Awards

Four students at the Cheriton School of Computer Science are recipients of the   Computing Research Association’s 2022 Outstanding Undergraduate Researcher Awards . The annual CRA awards program recognizes undergraduate students from universities across North America who have distinguished themselves by conducting exceptional research in an area of computer science.

Nicholas Vadivelu receives 2021 Jessie W.H. Zou Memorial Award for Excellence in Undergraduate Research

Nicholas Vadivelu , an undergraduate student majoring in computer science and statistics, has received the 2021  Jessie W.H. Zou Memorial Award . Established in 2012, this prestigious annual award recognizes excellence in research conducted by an undergraduate student in the Faculty of Mathematics. 

Steven Feng and Shannon Veitch selected for honourable mention in CRA’s 2020 Outstanding Undergraduate Researcher Awards

Undergraduate students Steven Feng and Shannon Veitch have each received a prestigious honorable mention for their research from the Computing Research Association. The annual CRA awards program recognizes undergraduate students from universities across North America who show outstanding research potential in an area of computing science. 

Additional Links:

Ildar Gainullin (2A CS), Jason Yuen (4B CS) and Wesley Leung (4A SE) dominate at the 2021 ICPC North America Division Championship, will advance to 45th ICPC World Finals: Read more

CS student Shivam Sharma, creator of Reflect, wins Concept $5K pitch competition: Read more

Vikram Subramanian awarded first place in the ACM Student Research Competition for his presentation that examined first contributions of developers to open source projects on GitHub: Read more

Team of three CS students — Jason Williamson, Bing Xu Hu and Rebecca Brown — has won the Pasupalak Velocity CS Capstone Award for entry in the 2019 Computer Science and Software Engineering Capstone Design Symposium: Read more

Thinking of Graduate Studies?

Passionate about research and interested in continuing your education by pursuing graduate studies? Consider applying to the combined bachelor's/master's programs that allow students to start graduate studies during the last year of their undergraduate studies.

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• 15 Latest Networking Research Topics for Students

Comparative analysis between snort and suricata IDS software(s)

Description of the topic

The main focus of this research is to conduct a comparative analysis between Snort and Suricata software to determine which IDS software can provide better performance. There are various IDS software(s) available that can be used by organizations but it is difficult to identify which one is best (Aldarwbi et al., 2022). Different organizational structures are often facing problems while setting up an IDS system which results in false positives and intrusions. Through this research, it can be identified which IDS software is better and what secure configuration is required to detect intrusions (Waleed et al., 2022).

Research objectives

• To evaluate Snort and Suricata IDS software(s) to determine the most optimal one.
• To identify the false positive rate of Snort and Suricata on the networked environment.

Research questions

RQ1: Which IDS software can perform better on the production network in terms of performance, security, scalability and reliability?

RQ2: What different ways can be followed to deal with false positive problems in IDS technology?

Research methodology

The given research objectives and research questions can be addressed using quantitative research methodology where an experimental approach can be followed. For the given topic, both Snort and Suricata IDS systems should be configured and tested against different attacks. Depending on the findings, it can be analyzed which IDS software can perform better in terms of performance and security (Shuai & Li, 2021).

• Aldarwbi, M.Y., Lashkari, A.H. and Ghorbani, A.A. (2022) “The sound of intrusion: A novel network intrusion detection system,” Computers and Electrical Engineering , 104, p. 108455.
• Shuai, L. and Li, S. (2021) “Performance optimization of Snort based on DPDK and Hyperscan,” Procedia Computer Science , 183, pp. 837-843.
• Waleed, A., Jamali, A.F. and Masood, A. (2022) “Which open-source ids? Snort, Suricata or Zeek,” Computer Networks , 213, p. 109116.

Role of honeypots and honey nets in network security

Network Security has become essential nowadays and there is a need for setting up robust mechanisms to maintain confidentiality and integrity (Feng et al., 2023). Due to the number of security mechanisms available, organizations found it hard to finalize and implement them on their network. For example, honey pots and honeynet approaches look almost the same and have the same purpose but work differently. Under this research topic, the configuration of honeynets and honeypots can be done to check which one can perform better security in terms of trapping cyber attackers. The entire implementation can be carried out in the cloud-based instance for improved security and it can be identified which type of honey pot technology must be preferred (Maesschalck et al., 2022).

• To set up a honey pot system using Open Canary on the virtual instance to protect against cyber attackers.
• To set up a honeynet system on the virtual instance to assure protection is provided against malicious attackers.
• To test honeypots and honeynets by executing DDoS attacks to check which can provide better security.

RQ1: Why is there a need for using honeypots over honey pots in a production networked environment?

RQ2: What are the differences between cloud-based and local honey pot systems for endpoint protection?

This research can be carried out using the quantitative method of research. At the initial stage, the implementation of honeypots and honeypots can be done on the virtual instance following different security rules. Once the rules are applied, the testing can be performed using a Kali Linux machine to check whether honey pots were effective or honeynets (Gill et al., 2020).

• Feng, H. et al. (2023) “Game theory in network security for Digital Twins in industry,” Digital Communications and Networks [Preprint].
• Gill, K.S., Saxena, S. and Sharma, A. (2020) “GTM-CSEC: A game theoretic model for cloud security based on ids and Honeypot,” Computers & Security , 92, p. 101732
• Maesschalck, S. et al. (2022) “Don’t get stung, cover your ICS in honey: How do honeypots fit within industrial control system security,” Computers & Security , 114, p. 102598.

How do malware variants are progressively improving?

This research can be based on evaluating how malware variants are progressively improving and what should be its state in the coming future. Malware is able to compromise confidential user’s information assets which is why this research can be based on identifying current and future consequences owing to its improvements (Deng et al., 2023). In this field, there is no research work that has been carried out to identify how malware variants are improving their working and what is expected to see in future. Once the evaluation is done, a clear analysis can also be done on some intelligent preventive measures to deal with dangerous malware variants and prevent any kind of technological exploitation (Tang et al., 2023).

• To investigate types of malware variants available to learn more about malware's hidden features.
• To focus on future implications of malware executable programs and how they can be avoided.
• To discuss intelligent solutions to deal with all malware variants.

RQ1: How do improvements in malware variants impact enterprises?

RQ2: What additional solutions are required to deal with malware variants?

In this research, qualitative analysis can be conducted on malware variants and the main reason behind their increasing severity. The entire research can be completed based on qualitative research methodology to answer defined research questions and objectives. Some real-life case studies should also be integrated into the research which can be supported by the selected topic (Saidia Fasci et al., 2023).

• Deng, H. et al. (2023) “MCTVD: A malware classification method based on three-channel visualization and deep learning,” Computers & Security , 126, p. 103084.
• Saidia Fasci, L. et al. (2023) “Disarming visualization-based approaches in malware detection systems,” Computers & Security , 126, p. 103062.
• Tang, Y. et al. (2023) “BHMDC: A byte and hex n-gram based malware detection and classification method,” Computers & Security , p. 103118.

Implementation of IoT - enabled smart office/home using cisco packet tracer

The Internet of Things has gained much more attention over the past few years which is why each enterprise and individual aims at setting up an IoT network to automate their processes (Barriga et al., 2023). This research can be based on designing and implementing an IoT-enabled smart home/office network using Cisco Packet Tracer software. Logical workspace, all network devices, including IoT devices can be used for preparing a logical network star topology (Elias & Ali, 2014). To achieve automation, the use of different IoT rules can be done to allow devices to work based on defined rules.

• To set up an IoT network on a logical workspace using Cisco Packet Tracer simulation software.
• To set up IoT-enabled rules on an IoT registration server to achieve automation (Hou et al., 2023).

RQ: Why is the Cisco packet tracer preferred for network simulation over other network simulators?

At the beginning of this research, a quantitative research methodology can be followed where proper experimental set-up can be done. As a packet tracer is to be used, the star topology can be used to interconnect IoT devices, sensors and other network devices at the home/office. Once a placement is done, the configuration should be done using optimal settings and all IoT devices can be connected to the registration server. This server will have IoT rules which can help in achieving automation by automatically turning off lights and fans when no motion is detected (Baggan et al., 2022).

• Baggan, V. et al. (2022) “A comprehensive analysis and experimental evaluation of Routing Information Protocol: An Elucidation,” Materials Today: Proceedings , 49, pp. 3040–3045.
• Barriga, J.A. et al. (2023) “Design, code generation and simulation of IOT environments with mobility devices by using model-driven development: Simulateiot-Mobile,” Pervasive and Mobile Computing , 89, p. 101751.
• Elias, M.S. and Ali, A.Z. (2014) “Survey on the challenges faced by the lecturers in using packet tracer simulation in computer networking course,” Procedia - Social and Behavioral Sciences , 131, pp. 11–15.
• Hou, L. et al. (2023) “Block-HRG: Block-based differentially private IOT networks release,” Ad Hoc Networks , 140, p. 103059.

Comparative analysis between AODV, DSDV and DSR routing protocols in WSN networks

For wireless sensor networks (WSN), there is a major need for using WSN routing rather than performing normal routines. As WSN networks are self-configured, there is a need for an optimal routing protocol that can improve network performance in terms of latency, jitter, and packet loss (Luo et al., 2023). There are often various problems faced when WSN networks are set up due to a lack of proper routing protocol selection. As a result of this, severe downtime is faced and all links are not able to communicate with each other easily (Hemanand et al., 2023). In this research topic, the three most widely used WSN routing protocols AODV, DSDV and DSR can be compared based on network performance. To perform analysis, three different scenarios can be created in network simulator 2 (Ns2).

• To create three different scenarios on ns2 software to simulate a network for 1 to 100 seconds.
• To analyze which WSN routing is optimal in terms of network performance metrics, including latency, jitter and packet loss.
• To use CBR and NULL agents for all wireless scenarios to start with simulation purposes.

RQ: How do AODV, DSR and DSDV routing protocols differ from each other in terms of network performance?

This research can be carried out using a quantitative research method. The implementation for the provided research topic can be based on Ns2 simulation software where three different scenarios can be created (AODV, DSDV and DSR). For each scenario, NULL, CSR and UDP agents can be done to start with simulation for almost 1 to 100 seconds. For all transmissions made during the given time, network performance can be checked to determine which routing is best (Mohapatra & Kanungo, 2012).

• Human and, D. et al. (2023) “Analysis of power optimization and enhanced routing protocols for Wireless Sensor Networks,” Measurement: Sensors , 25, p. 100610. Available at: https://doi.org/10.1016/j.measen.2022.100610.
• Luo, S., Lai, Y. and Liu, J. (2023) “Selective forwarding attack detection and network recovery mechanism based on cloud-edge cooperation in software-defined wireless sensor network,” Computers & Security , 126, p. 103083. Available at: https://doi.org/10.1016/j.cose.2022.103083.
• Mohapatra, S. and Kanungo, P. (2012) “Performance analysis of AODV, DSR, OLSR and DSDV routing protocols using NS2 Simulator,” Procedia Engineering , 30, pp. 69–76. Available at: https://doi.org/10.1016/j.proeng.2012.01.835.

Securing wireless network using AAA authentication and WLAN controller

Wireless networks often face intrusion attempts due to insecure protocols and sometimes open SSIDs. As a result of this, man-in-the-middle and eavesdropping attacks become easier which results in the loss of confidential information assets (Sivasankari & Kamalakkannan, 2022). When it comes to managing networks in a large area, there are higher chances for attacks that enable cyber attackers in intercepting ongoing communication sessions. However, there is currently no research conducted where the use of AAA authentication has been done with WLAN controllers to make sure a higher level of protection is provided (Nashwan, 2021). The proposed research topic can be based on securing wireless networks with the help of AAA authentication and WLAN controllers. The use of AAA authentication can be done to set up a login portal for users whilst the WLAN controller can be used for managing all wireless access points connected to the network (Nashwan, 2021).

• To set up AAA authentication service on the wireless network simulated on Cisco Packet Tracer for proper access control.
• To set up a WLAN controller on the network to manage all wireless access points effortlessly.
• To use WPA2-PSK protocol on the network to assure guest users are only able to access wireless networks over a secure protocol.

RQ1: What additional benefits are offered by AAA authentication on the WLAN networks?

RQ2: Why are wireless networks more likely to face network intrusions than wired networks?

This research topic is based on the secure implementation of a wireless LAN network using a Cisco packet tracer. Hence, this research can be carried out using a quantitative research method. The implementation can be carried out using AAA authentication which can assure that access control is applied for wireless logins. On the other hand, a WLAN controller can also be configured which can ensure that all WAPs are managed (ZHANG et al., 2012).

• Nashwan, S. (2021) “AAA-WSN: Anonymous Access Authentication Scheme for wireless sensor networks in Big Data Environment,” Egyptian Informatics Journal , 22(1), pp. 15–26.
• Sivasankari, N. and Kamalakkannan, S. (2022) “Detection and prevention of man-in-the-middle attack in IOT network using regression modeling,” Advances in Engineering Software , 169, p. 103126.
• ZHANG, J. et al. (2012) “AAA authentication for Network mobility,” The Journal of China Universities of Posts and Telecommunications , 19(2), pp. 81-86.

OWASP's approach to secure web applications from web application exploits

The research can revolve around the development of web applications by considering OWASP's top 10 rules. Usually, web applications are deployed by organizations depending on their requirements and these applications are vulnerable to various exploits, including injection, broken authentication and other forgery attacks (Poston, 2020). Identifying every single vulnerability is difficult when reference is not taken and often organizations end up hosting a vulnerable server that leads to privacy issues and compromises confidential information easily. In this research, OWASP's top 10 approaches can be followed to develop a secure web application that can be able to protect against top web application exploits. This approach is based on emphasizing severe and minor vulnerabilities which must be patched for protecting against web application attacks (Deepa & Thilagam, 2016).

• The first objective can be setting up an insecure web application on the cloud environment which can be exploited with different techniques.
• The second objective can be to consider all techniques and procedures provided by OWASP's top 10 methodologies.
• The last objective can be applying all fixes to insecure web applications to make them resistant to OWASP top 10 attacks (Sonmez, 2019).

RQ1: What are the benefits of using OWASP's top 10 approaches to harden web applications in comparison to other security approaches?

The research methodology considered for this research project can be quantitative using an experimental approach. The practical work can be done for the selected topic using AWS or the Azure cloud platform. Simply, a virtual web server can be configured and set up with a secure and insecure web application. Following OWASP's top 10 techniques and procedures, the web application can be secured from possible attacks. In addition, insecure applications can also be exploited and results can be evaluated (Applebaum et al., 2021).

• Applebaum, S., Gaber, T. and Ahmed, A. (2021) “Signature-based and machine-learning-based web application firewalls: A short survey,” Procedia Computer Science , 189, pp. 359–367. Available at: https://doi.org/10.1016/j.procs.2021.05.105.
• Deepa, G. and Thilagam, P.S. (2016) “Securing web applications from injection and logic vulnerabilities: Approaches and challenges,” Information and Software Technology , 74, pp. 160–180. Available at: https://doi.org/10.1016/j.infsof.2016.02.005.
• Poston, H. (2020) “Mapping the owasp top Ten to the blockchain,” Procedia Computer Science , 177, pp. 613-617. Available at: https://doi.org/10.1016/j.procs.2020.10.087.
• Sonmez, F.Ö. (2019) “Security qualitative metrics for Open Web Application Security Project Compliance,” Procedia Computer Science , 151, pp. 998-1003. Available at: https://doi.org/10.1016/j.procs.2019.04.140.

Importance of configuring RADIUS (AAA) server on the network

User authentication has become significant nowadays as it guarantees that a legitimate user is accessing the network. But a problem is faced when a particular security control is to be identified for authentication and authorization. These controls can be categorized based on mandatory access controls, role-based access control, setting up captive portals and many more. Despite several other security controls, one of the most efficient ones is the RADIUS server (SONG et al., 2008). This server can authenticate users on the network to make sure network resources are accessible to only legal users. This research topic can be based on understanding the importance of RADIUS servers on the network which can also be demonstrated with the help of the Cisco Packet Tracer. A network can be designed and equipped with a RADIUS server to ensure only legal users can access network resources (WANG et al., 2009).

• To configure RADIUS (AAA) server on the network which can be able to authenticate users who try to access network resources.
• To simulate a network on a packet tracer simulation software and verify network connectivity.

RQ1: What are other alternatives to RADIUS (AAA) authentication servers for network security?

RQ2: What are the common and similarities between RADIUS and TACACS+ servers?

As a logical network is to be designed and configured, a quantitative research methodology can be followed. In this research coursework, a secure network design can be done using a packet tracer network simulator, including a RADIUS server along with the DMZ area. The configuration for the RADIUS server can be done to allow users to only access network resources by authenticating and authorizing (Nugroho et al., 2022).

• Nugroho, Y.S. et al. (2022) “Dataset of network simulator related-question posts in stack overflow,” Data in Brief , 41, p. 107942.
• SONG, M., WANG, L. and SONG, J.-de (2008) “A secure fast handover scheme based on AAA protocol in Mobile IPv6 Networks,” The Journal of China Universities of Posts and Telecommunications , 15, pp. 14-18.
• WANG, L. et al. (2009) “A novel congestion control model for interworking AAA in heterogeneous networks,” The Journal of China Universities of Posts and Telecommunications , 16, pp. 97-101.

Comparing mod security and pF sense firewall to block illegitimate traffic

Firewalls are primarily used for endpoint security due to their advanced features ranging from blocking to IDS capabilities and many more. It is sometimes challenging to identify which type of firewall is best and due to this reason, agencies end up setting up misconfigured firewalls (Tiwari et al., 2022). This further results in a cyber breach, destroying all business operations. The research can be emphasizing conducting a comparison between the two most widely used firewalls i.e. Mod Security and pF sense. Using a virtualized environment, both firewalls can be configured and tested concerning possible cyber-attacks (Lu & Yang, 2020).

• To use the local environment to set up Mod security and pF sense firewall with appropriate access control rules.
• To test both firewalls by executing distributed denial of service attacks from a remote location.
• To compare which type of firewall can provide improved performance and robust security.

RQ: How do Mod security and pF sense differ from each other in terms of features and performance?

The practical experimentation for both firewalls can be done using a virtualized environment where two different machines can be created. Hence, this research can be carried out using a quantitative research method . The first machine can have Mod security and the second machine can have pF sense configured. A new subnet can be created which can have these two machines. The third machine can be an attacking machine which can be used for testing firewalls. The results obtained can be then evaluated to identify which firewall is best for providing security (Uçtu et al., 2021).

• Lu, N. and Yang, Y. (2020) “Application of evolutionary algorithm in performance optimization of Embedded Network Firewall,” Microprocessors and Microsystems , 76, p. 103087.
• Tiwari, A., Papini, S. and Hemamalini, V. (2022) “An enhanced optimization of parallel firewalls filtering rules for scalable high-speed networks,” Materials Today: Proceedings , 62, pp. 4800-4805.
• Uçtu, G. et al. (2021) “A suggested testbed to evaluate multicast network and threat prevention performance of Next Generation Firewalls,” Future Generation Computer Systems , 124, pp. 56-67.

Conducting a comprehensive investigation on the PETYA malware

The main purpose of this research is to conduct a comprehensive investigation of the PETYA malware variant (McIntosh et al., 2021). PETYA often falls under the category of ransomware attacks which not only corrupt and encrypt files but can compromise confidential information easily. Along with PETYA, there are other variants also which lead to a security outage and organizations are not able to detect these variants due to a lack of proper detection capabilities (Singh & Singh, 2021). In this research, a comprehensive analysis has been done on PETYA malware to identify its working and severity level. Depending upon possible causes of infection of PETYA malware, some proactive techniques can also be discussed (Singh & Singh, 2021). A separation discussion can also be made on other malware variants, their features, and many more.

• The main objective of this research is to scrutinize the working of PETYA malware because a ransomware attack can impact the micro and macro environment of the organizations severely.
• The working of PETYA malware along with its source code can be reviewed to identify its structure and encryption type.
• To list all possible CVE IDs which are exploited by the PETYA malware.

RQ1: How dangerous is PETYA malware in comparison to other ransomware malware?

This research can be based on qualitative research methodology to evaluate the working of PETYA malware from various aspects, the methodology followed and what are its implications. The research can be initiated by evaluating the working of PETYA malware, how it is triggered, what encryption is applied and other factors. A sample source code can also be analyzed to learn more about how cryptography is used with ransomware (Abijah Roseline & Geetha, 2021).

• Abijah Roseline, S. and Geetha, S. (2021) “A comprehensive survey of tools and techniques mitigating computer and mobile malware attacks,” Computers & Electrical Engineering , 92, p. 107143.
• McIntosh, T. et al. (2021) “Enforcing situation-aware access control to build malware-resilient file systems,” Future Generation Computer Systems , 115, pp. 568-582.
• Singh, J. and Singh, J. (2021) “A survey on machine learning-based malware detection in executable files,” Journal of Systems Architecture , 112, p. 101861.

Setting up a Live streaming server on the cloud platform

Nowadays, various organizations require a live streaming server to stream content depending upon their business. However, due to a lack of proper hardware, organizations are likely to face high network congestion, slowness and other problems (Ji et al., 2023). Referring to the recent cases, it has been observed that setting up a streaming server on the local environment is not expected to perform better than a cloud-based streaming server configuration (Martins et al., 2019). This particular research topic can be based on setting up a live streaming server on the AWS or Azure cloud platform to make sure high network bandwidth is provided with decreased latency. The research gap analysis would be conducted to analyze the performance of live streaming servers on local and cloud environments in terms of network performance metrics (Bilal et al., 2018).

• To set up a live streaming server on the AWS or Azure cloud platform to provide live streaming services.
• To use load balancers alongside streaming servers to ensure the load is balanced and scalability is achieved.
• To use Wireshark software to test network performance during live streaming.

RQ1: Why are in-house streaming servers not able to provide improved performance in comparison to cloud-based servers?

RQ2: What additional services are provided by cloud service providers which help in maintaining network performance?

The implementation is expected to carry out on the AWS cloud platform with other AWS services i.e. load balancer, private subnet and many more (Efthymiopoulou et al., 2017). Hence, this research can be carried out using a quantitative research method. The configuration of ec2 instances can be done which can act as a streaming server for streaming media and games. For testing this project, the use of OBS studio can be done which can help in checking whether streaming is enabled or not. For network performance, Wireshark can be used for testing network performance (George et al., 2020).

• Bilal, KErbad, A. and Hefeeda, M. (2018) “QoE-aware distributed cloud-based live streaming of multi-sourced Multiview Videos,” Journal of Network and Computer Applications , 120, pp. 130-144.
• Efthymiopoulou, M. et al. (2017) “Robust control in cloud-assisted peer-to-peer live streaming systems,” Pervasive and Mobile Computing , 42, pp. 426-443.
• George, L.C. et al. (2020) “Usage visualization for the AWS services,” Procedia Computer Science , 176, pp. 3710–3717.
• Ji, X. et al. (2023) “Adaptive QoS-aware multipath congestion control for live streaming,” Computer Networks , 220, p. 109470.
• Martins, R. et al. (2019) “Iris: Secure reliable live-streaming with Opportunistic Mobile Edge Cloud offloading,” Future Generation Computer Systems , 101, pp. 272-292.

Significance of using OSINT framework for Network reconnaissance

Network reconnaissance is becoming important day by day when it comes to penetration testing. Almost all white hat hackers are dependent on the OSINT framework to start with network reconnaissance and footprinting when it comes to evaluating organizational infrastructure. On the other hand, cyber attackers are also using this technique to start fetching information about their target. Currently, there is no investigation carried out to identify how effective the OSINT framework is over traditional reconnaissance activities (Liu et al., 2022). This research is focused on using OSINT techniques to analyze victims using different sets of tools like Maltego, email analysis and many other techniques. The analysis can be based on fetching sensitive information about the target which can be used for conducting illegal activities (Abdullah, 2019).

• To use Maltego software to conduct network reconnaissance on the target by fetching sensitive information.
• To compare the OSINT framework with other techniques to analyze why it performs well.

RQ1: What is the significance of using the OSINT framework in conducting network reconnaissance?

RQ2: How can the OSINT framework be used by cyber hackers for conducting illegitimate activities?

The OSINT framework is easily accessible on its official website where different search options are given. Hence, this research can be carried out using a quantitative research method. Depending upon the selected target, each option can be selected and tools can be shortlisted for final implementation. Once the tools are shortlisted, they can be used to conduct network reconnaissance (González-Granadillo et al., 2021). For example, Maltego can be used as it is a powerful software to fetch information about the target.

• Abdullah, S.A. (2019) “Seui-64, bits an IPv6 addressing strategy to mitigate reconnaissance attacks,” Engineering Science and Technology , an International Journal, 22(2), pp. 667–672.
• Gonzalez-Granadillo, G. et al. (2021) “ETIP: An enriched threat intelligence platform for improving OSINT correlation, analysis, visualization and sharing capabilities,” Journal of Information Security and Applications , 58, p. 102715.
• Liu, W. et al. (2022) “A hybrid optimization framework for UAV Reconnaissance Mission Planning,” Computers & Industrial Engineering , 173, p. 108653.

Wired and wireless network hardening in cisco packet tracer

At present, network security has become essential and if enterprises are not paying attention to the security infrastructure, there are several chances for cyber breaches. To overcome all these issues, there is a need for setting up secure wired and wireless networks following different techniques such as filtered ports, firewalls, VLANs and other security mechanisms. For the practical part, the use of packet tracer software can be done to design and implement a highly secure network (Sun, 2022).

• To use packet tracer simulation software to set up secure wired and wireless networks.
• Use different hardening techniques, including access control rules, port filtering, enabling passwords and many more to assure only authorized users can access the network (Zhang et al., 2012).

RQ: Why is there a need for emphasizing wired and wireless network security?

Following the quantitative approach, the proposed research topic implementation can be performed in Cisco Packet Tracer simulation software. Several devices such as routers, switches, firewalls, wireless access points, hosts and workstations can be configured and interconnected using Cat 6 e cabling. For security, every device can be checked and secure design principles can be followed like access control rules, disabled open ports, passwords, encryption and many more (Smith & Hasan, 2020).

• Smith, J.D. and Hasan, M. (2020) “Quantitative approaches for the evaluation of Implementation Research Studies,” Psychiatry Research , 283, p. 112521.
• Sun, J. (2022) “Computer Network Security Technology and prevention strategy analysis,” Procedia Computer Science , 208, pp. 570–576.
• Zhang, YLiang, R. and Ma, H. (2012) “Teaching innovation in computer network course for undergraduate students with a packet tracer,” IERI Procedia , 2, pp. 504–510.

Different Preemptive ways to resist spear phishing attacks

When it comes to social engineering, phishing attacks are rising and are becoming one of the most common ethical issues as it is one of the easiest ways to trick victims into stealing information. This research topic is based on following different proactive techniques which would help in resisting spear phishing attacks (Xu et al., 2023). This can be achieved by using the Go-Phish filter on the machine which can automatically detect and alert users as soon as the phished URL is detected. It can be performed on the cloud platform where the apache2 server can be configured along with an anti-phishing filter to protect against phishing attacks (Yoo & Cho, 2022).

• To set up a virtual instance on the cloud platform with an apache2 server and anti-phishing software to detect possible phishing attacks.
• To research spear phishing and other types of phishing attacks that can be faced by victims (Al-Hamar et al., 2021).

RQ1: Are phishing attacks growing just like other cyber-attacks?

RQ2: How effective are anti-phishing filters in comparison to cyber awareness sessions?

The entire research can be conducted by adhering to quantitative research methodology which helps in justifying all research objectives and questions. The implementation of the anti-phishing filter can be done by creating a virtual instance on the cloud platform which can be configured with an anti-phishing filter. Along with this, some phishing attempts can also be performed to check whether the filter works or not (Siddiqui et al., 2022).

• Al-Hamar, Y. et al. (2021) “Enterprise credential spear-phishing attack detection,” Computers & Electrical Engineering , 94, p. 107363.
• Siddiqui, N. et al. (2022) “A comparative analysis of US and Indian laws against phishing attacks,” Materials Today: Proceedings , 49, pp. 3646–3649.
• Xu, T., Singh, K. and Rajivan, P. (2023) “Personalized persuasion: Quantifying susceptibility to information exploitation in spear-phishing attacks,” Applied Ergonomics , 108, p. 103908.
• Yoo, J. and Cho, Y. (2022) “ICSA: Intelligent chatbot security assistant using text-CNN and multi-phase real-time defense against SNS phishing attacks,” Expert Systems with Applications , 207, p. 117893.

Evaluating the effectiveness of distributed denial of service attacks

The given research topic is based on evaluating the effectiveness of distributed denial of service attacks on cloud and local environments. Hence, this research can be carried out using a quantitative research method. Cyber attackers find DDoS as one of the most dangerous technological exploitation when it comes to impacting network availability (Krishna Kishore et al., 2023). This research can revolve around scrutinizing the impact of DDoS attacks on the local environment and cloud environment. This can be done by executing DDoS attacks on a simulated environment using hoping or other software(s) to check where it has a higher magnitude (de Neira et al., 2023).

• To set up a server on the local and cloud environment to target using DDoS attacks for checking which had experienced slowness.
• To determine types of DDoS attack types, their magnitude and possible mitigation techniques.

RQ: Why do DDoS attacks have dynamic nature and how is it likely to sternly impact victims?

The experimentation for this research can be executed by creating a server on the local and cloud environment. Hence, this research can be carried out using a quantitative research method. These servers can be set up as web servers using apache 2 service. On the other hand, a Kali Linux machine can be configured with DDoS execution software. Each server can be targeted with DDoS attacks to check its effectiveness (Benlloch-Caballero et al., 2023).

• Benlloch-Caballero, P., Wang, Q. and Alcaraz Calero, J.M. (2023) “Distributed dual-layer autonomous closed loops for self-protection of 5G/6G IOT networks from distributed denial of service attacks,” Computer Networks , 222, p. 109526.
• de Neira, A.B., Kantarci, B. and Nogueira, M. (2023) “Distributed denial of service attack prediction: Challenges, open issues and opportunities,” Computer Networks , 222, p. 109553.
• Krishna Kishore, P., Ramamoorthy, S. and Rajavarman, V.N. (2023) “ARTP: Anomaly-based real time prevention of distributed denial of service attacks on the web using machine learning approach,” International Journal of Intelligent Networks , 4, pp. 38–45.

Recommended Readings

Latest Web Development Research Topics

Top Management Research Topics

Newest AI Research Topics

Research in every field is becoming more and more essential because of constant developments around the world. Similar is the case in the field of networking. This is the reason; students who are preparing to master the field of networking need to keep their knowledge of the current state of the art in the field up to date.

However, choosing the right research topic often becomes a tough task for students to carry out their research effectively. That being the case, this list contains 15 latest research topics in the field of networking. Whether you are a seasoned researcher or just starting, this list can provide you with ample inspiration and guidance to drive your research forward in the dynamic and evolving field of Networking.

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Undergraduate Research Opportunities

Undergraduates are an essential part of our leading-edge research. There are many ways to contribute to impactful research early in your career, from summer programs to paid research positions with faculty.

Year Long Research

• Clare Boothe Luce Research Scholars an ISUR-affiliated program supporting undergraduate women in research and teaching in science, mathematics, and engineering. Eight scholars are selected and funded each year.
• C3SR-Undergraduate Research in Artificial Intelligence is an IBM-Illinois and ISUR partnership funding undergraduate AI and cognitive computing research, from theory to practical application while working with a C3SR faculty mentor.
• The National Center for Supercomputing Applications (NCSA) SPIN is an academic internship program for undergraduate students to participate in supercomputing, visualization, data analytics, and similar fields with five weekly paid hours.

Semester Long Research

• CS Job Portal is our department's employment opportunities with course assistant and undergraduate research positions.
• PURE (Promoting Undergraduate Research in Engineering) is a student-run research program connecting first-year and second-year students with graduate student mentors to jump-start their research careers. 

Summer Research

• The National Center for Supercomputing Applications (NCSA) INCLUSION program is a 10-week program for students from underrepresented communities to work in pairs with mentors on research aimed toward social impact based around open-source software development.
• Summer Research Program for Undergraduates (SRP)  students work on state-of-the-art research with university faculty while attending professional development programs aimed at making students strong researchers and graduate school candidates
• Mind in Vitro Undergraduate Summer Research Program undergraduate researchers work with faculty mentors and graduate students on projects related to Mind in Vitro while participating in the Illinois summer research program networking, socials, lunches, and seminars.

Mentorship Opportunities

Showcase Opportunities

• Engineering Research Fair is hosted by Grainger Engineering every semester for researchers to share their work and labs and for companies recruiting researchers.
• Undergraduate Research Symposium is a yearly campus-wide research symposium for undergraduate researchers to present the results of their research and gain experience presenting work to a wider audience.

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What to Know About Undergraduate Research

Many students don’t consider undergraduate research when searching for a co-op or internship, but it can have the same benefits – if not better – than those traditional options. This blog will detail the benefits of undergraduate research, including skills you can gain, types of research, and how to find opportunities.

Benefits of Undergraduate Research

The field of research is the heart of innovation and is responsible for the continuous advancement of medical care, artificial intelligence, space exploration, and so much more. Similar to an industry internship or co-op, participating in a research lab gives students the opportunity to gain experience, build a relevant skillset, and learn about their field. Unique to research, though, is the development of critical thinking skills. Because they’re constantly looking for new answers, methods, or ideas, researchers build very strong critical thinking skills, which are highly desired in the world of engineering. Other skills like independence and collaboration are also specially attained in research because you are responsible for designing/completing your own experiments and analyzing the results with multidisciplinary teams. Beyond technical and interpersonal skills, research also provides the opportunity to present and publish your work. Whether you present your research at a conference or publish it in a research journal, being an author on a formal technical document is an amazing qualification to have when applying for a position – in both industry and academia!

How to Find Opportunities

Research opportunities may be posted on job boards, like Handshake or LinkedIn, but you’re more likely to find them elsewhere. There are two main types of research opportunities you will find: on-campus at Ohio State and at other institutions. Ohio State professors love to take on undergraduate students and frequently have positions available. The best way to find professors and information about their research is on the department website (i.e., biomedical engineering ). Many departments have professors separated by research interest, so it is very easy to search within a field you are interested in. From here, you can reach out by email to professors to ask them about open positions (I like to format it similar to a cover letter, but ECS can help draft an email with you!). On-campus research can be completed during the school year while still taking classes or during the summer, where you can take on more hours. On the contrary, off-campus research is more similar to an internship, where you can work full-time over the summer or take a co-op during an academic term. Specifically during the summer, many universities offer Research Experience for Undergraduates (REUs) where you typically live on that universities campus. 

Ultimately, there are a plethora of research opportunities here at Ohio State and across the nation, which are frequently available for younger students without prior experience. Getting involved in research can help you build a unique and relevant skillset that’ll make you stand out on job applications while also getting hands-on experience in advanced topics. Research experience is just as impactful as internship or co-op experience, so start your search today!

“Don’t wait for opportunity, create it.” - George Bernard Shaw

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FSU Panama City

FSU Panama City will close Wednesday, April 10,  at 4 p.m. in anticipation of the extreme weather moving through the Panhandle.  Campus will reopen Thursday, April 11, for normal operations. Updates will be posted as necessary both here and on FSU PC social media.

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Computer Science (Undergraduate Student Research)

Asynchronous Online Research Presentations Videos

Title of presentation : Pattern Analysis of Flight Delays   Presenter :  William Robert Bartels Advisor :  Dr. Karen Works   Abstract :

Are there any regular patterns to flight delays? In this exhibition study, I utilized a data set of reported flight delays and created a graphical database of such events using NEO4j. The Flight Delays dataset was obtained from Kaggle (https://www.kaggle.com/datasets/flight-delay). It is comprised of flight details, delay information, and weather data. As preliminary analysis I examined distributions and trends using visualizations in Python. I then created a graphical database modeling airports as nodes and flights as relationships to analyze potential delay propagation patterns . I am exploring how to structure the graphical database to search for common patterns in scheduling sequences that lead to flight delays.

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Title of presentation : 'NextGenProperties' : A Data-Driven Approach to Automated Real Estate Valuation and Categorization   Presenter :  Renzo Broggi Advisor :  Dr. Karen Works   Abstract :

Current real estate valuation tools stand to benefit from additional insights beyond those based solely on analyses of sales data. This project, NextGenProperties, seeks to address the need by introducing an automated tool for analyzing real estate sales data and categorizing properties based on various factors such as location, size, market trends, and investment potential. By leveraging machine learning algorithms, our system processes large datasets to extract meaningful insights, allowing users to make informed decisions regarding property valuation and investment opportunities. By implementing a combination of data preprocessing techniques and feature extraction methods, 'NextGenProperties' seeks to streamline the analysis process and provide accurate estimations, while minimizing manual effort. As an exploratory submission, my presentation will showcase the potential 'NextGenProperties' has to help revolutionize real estate estimation through data-driven automation. 

Title of presentation : Effects of Graph Augmentation of Graph Neural Networks (GNN) Twitter Bot Detection Models   Presenter :  Juan Sanchez Moreno Advisor :  Dr. Karen Works   Abstract :

There is a growing concern about the presence and real proportion of bots in social media, especially on Twitter (now X). These bots can spread misinformation, impose narratives, and distort the reality of the platforms users. Graph Neural Networks (GNN) Twitter bot detection models have been shown to be highly effective. We seek to improve the detection accuracy of social media bots and the efficiency of the GNN by adapting the relationships between nodes and randomly dropping nodes and edges on the graph.  Our experiments bound the levels at which each of the aforementioned adjustments stop improving the GNN.

Title of presentation :  Advisor :     Abstract :

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Office of the Vice President for Research

Four clas faculty researchers secure prestigious early career awards.

Continuing  an upward trend of University of Iowa faculty securing prestigious early-career grants, four investigators from the Departments of Physics and Astronomy and Computer Science have been awarded notable grant awards to advance their careers.

DeRoo, Hoadley advance space instrumentation with Nancy Grace Roman Technology Fellowships in Astrophysics for Early Career Researchers

Casey DeRoo and Keri Hoadley , both assistant professors in the Department of Physics and Astronomy, each received a Nancy Grace Roman Technology Fellowship in Astrophysics for Early Career Researchers. The NASA fellowship provides each researcher with $500,000 over two years to support their research in space-based instrumentation. 

Hoadley’s research is two-pronged. She will design and ultimately prototype a mirror-based vacuum ultraviolet polarizer, which will allow researchers to access polarized light from space below 120-nanometer wavelength. Polarizing light at such a low wavelength is crucial to building optics for NASA’s future Habitable World Observatory (HWO), the agency’s next flagship astrophysics mission after the Nancy Grace Roman Space Telescope. 

“Our vacuum ultraviolet polarizer project is meant to help set up our lab to propose to NASA for one or more follow-up technology programs, including adapting this polarizer for use in vacuum systems, duplicating it and measuring its efficiency to measure additional flavors of polarized UV light, quantifying the polarization effects introduced by UV optical components that may be used on HWO, and building an astronomical instrument to measure the polarization of UV from around massive stars and throughout star-forming regions,” said Hoadley.

In addition, Hoadley and her team will build a facility to align, calibrate, and integrate small space telescopes before flight, using a vacuum chamber and wavelengths of light typically only accessible in space, which could help the university win future small satellite and suborbital missions from NASA. 

DeRoo will work to advance diffraction gratings made with electron beams that pattern structures on a nanometer scale.   Like a prism, diffraction gratings spread out and direct light coming from stars and galaxies, allowing researchers to deduce things like the temperature, density, or composition of an astronomical object.

The fellowship will allow DeRoo to upgrade the university’s Raith

 Voyager tool, a specialized fabrication tool hosted by OVPR’s Materials Analysis, Testing and Fabrication (MATFab) facility.

“These upgrades will let us perform algorithmic patterning, which uses computer code to quickly generate the patterns to be manufactured,” DeRoo said. “This is a major innovation that should enable us to make more complex grating shapes as well as make gratings more quickly.” DeRoo added that the enhancements mean his team may be able to make diffraction gratings that allow space instrument designs that are distinctly different from those launched to date.

“For faculty who develop space-based instruments, the Nancy Grace Roman Technology Fellowship is on par with the prestige of an NSF CAREER or Department of Energy Early Career award,” said Mary Hall Reno, professor and department chair. “Our track record with the program elevates our status as a destination university for astrophysics and space physics missions.”

Uppu pursues building blocks quantum computing with NSF CAREER Award

Ravitej Uppu, assistant professor in the Department of Physics and Astronomy, received a 5-year NSF CAREER award of $550,000 to conduct research aimed at amplifying the power of quantum computing and making its application more practical. 

Uppu and his team will explore the properties of light-matter interactions at the level of a single photon interacting with a single molecule, enabling them to generate efficient and high-quality multiphoton entangled states of light. Multiphoton entangled states, in which photons become inextricably linked, are necessary for photons to serve as practical quantum interconnects, transmitting information between quantum computing units, akin to classical cluster computers. 

“ In our pursuit of secure communication, exploiting quantum properties of light is the final frontier,” said Uppu. “However, unavoidable losses that occur in optical fiber links between users can easily nullify the secure link. Our research on multiphoton entangled states is a key building block for implementing ‘quantum repeaters’ that can overcome this challenge.”

Jiang tackles real-world data issues with NSF CAREER Award

Peng Jiang, assistant professor in the Department of Computer Science, received an NSF CAREER Award that will provide $548,944 over five years to develop tools to support the use of sampling-based algorithms. 

Sampling-based algorithms reduce computing costs by processing only a random selection of a dataset, which has made them increasingly popular, but the method still faces limited efficiency. Jiang will develop a suite of tools that simplify the implementation of sampling-based algorithms and improve their efficacy across wide range of computing and big data applications.

“ A simple example of a real-world application is subgraph matching,” Jiang said. “For example, one might be interested in finding a group of people with certain connections in a social network. The use of sampling-based algorithms can significantly accelerate this process.”

In addition to providing undergraduate students the opportunity to engage with this research, Jiang also plans for the project to enhance projects in computer science courses.

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School of Computer Science Demo Day

The School of Computer Science is looking forward to seeing you on Wednesday, April 10 th , for our   9 th CS Demo Day at The School of Computer Science Advanced Computing Hub, located at 300 Ouellette Avenue, from 10:00 am – 12:30 pm

Our event will feature 20 presentations, including research and real-time projects from our current undergraduate and graduate students. CS Demo Day will create fantastic networking opportunities for students, faculty, and industry partners!

Our Computer Science students at the University of Windsor are excited to showcase their talent and hard work to all of you.

Please note: no registration is required for students to attend this event

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4 duke cs students receive 2024 nsf graduate research fellowships, april 10, 2024.

Four Duke CS students received NSF Graduate Research Fellowships :

• Jonathan Donnelly , who worked with Cynthia Rudin and will pursue a PhD in Machine Learning at Duke.
• Jabari Kwesi worked with Pardis Emami-Naeini and will pursue a PhD in Human Computer Interaction at Duke.
• Megan Richards is a recent Duke ECE-CS grad who plans to pursue a PhD in ML. She worked with Mark Sendak at DIHI and Ricardo Henao of Duke ECE.
• Ruoyu (Roy) Xie worked with Bhuwan Dhingra and will pursue a PhD in Natural Language Processing at Duke.

Congratulations to all!

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UNITE Fall 2024 Course Offerings

UNITE Distributed Learning provides access to live streaming video of class sessions plus same-day access to streaming video archives and downloadable video and audio files of course meetings to the students who enroll through UNITE, "piggybacking" on an on-campus section of the course in a UNITE-enhanced classroom.

Semester Schedule

The UNITE sections of a course follow the same semester schedule as the on-campus section of the course. This includes exams (which may be required synchronous events - see below) and homework deadlines as well as University deadlines for adding courses, cancelling courses, refunds, etc.

Exams, Presentations and Homework

Assessments (exams, presentations, homework, etc.) vary class-to-class, instructor-to-instructor.  Note that some courses require that exams be taken at the same time/same day as the on-campus section of the course upon which UNITE is "piggybacking" for UNITE-enrolled students as well as live student presentations to the class.

Courses Exams Requiring Synchronous, Live Proctoring

For courses in which the instructor is holding in-class, proctored exams for those enrolled in the on-campus sections, students enrolled through UNITE are REQUIRED  to take exams on the same day/same time as the students enrolled in the on-campus sections of the course with a UNITE-approved proctor.

Any deviation from the same day/same time proctored exams for these courses - including the request to take the exams with the on-campus students - must be approved by the instructor.  UNITE will NOT grant these permissions. Work out these arrangements with the instructor before the 100% refund period ends.  

Students who arrange to come to campus and take in-class, proctored exams with the students enrolled in the on-campus section of a course do not need to find/submit a local proctor - note that this must be arranged with the instructor to verify permission/space (enrollment in a UNITE section does not hold a physical classroom seat in the classroom).

Students are responsible for finding and submitting proctor information to UNITE to evaluate and approve. UNITE will contact all students enrolled through UNITE to initiate this process shortly after the semester begins.

Final Exams: Final exam dates are posted in the official University of Minnesota Class Schedule.  UNITE will stream video on Saturdays. If you are enrolled in a UNITE section with an exam on a Saturday, you will need to have a proctor administer the exam. If you need to make other arrangements you will need to contact the instructor directly to seek approval.

Courses with Exams Not Requiring Live, In-Person Proctoring

For courses for which the instructors are using other types of exams - take-home exams, online exams (with a video proctoring service or without) -  instead of in-class, proctored exams, there is no need for students who enroll in the UNITE section of a course to find and submit a proctor to UNITE for approval.

Presentations

For courses with required live presentations by students - individually or as a group - UNITE will work with the student(s) and instructor to provide a live webconference between the remote student(s) and the classroom in real time.  In some instances, UNITE-enrolled students are able to join the on-campus students in the classroom to present in person (though that is not required).  For courses with required, live presentations  it is best to note that commitment for the course with the instructor before the 100% refund period ends.  

Homework Submission and Return

Increasing, faculty and TAs are using Canvas course sites for submission and return of homework.

For those faculty and TAs who do not, homework may be submitted to UNITE via email. Our office will record submissions and deliver to instructors and/or TAs for grading. Graded materials will be returned to your University email account when we receive it.

For more information, refer to the "Step Two: Know How UNITE Works" of UNITE Steps to Success .

The courses offered are subject to change. For the summer semester, UNITE will stop recording/streaming a course if there are no students enrolled in that course through UNITE.

Course descriptions taken from the University of Minnesota's Schedule Builder . Courses topics may be revised per instructor. Contact instructor for more detailed and up-to-date information.

Grad 0999 – 51566 Call Number – UNITE students must register online themselves for this status. Graduate students registering for this status must register before the semester begins or they will be charged the normal late registration fees.

Undergraduate students taking classes on campus may enroll in UNITE courses with instructors' permission. Learn more about Undergraduate Credit Enrollment though UNITE .

Please note Important Fall Semester Dates .

Students enrolled in on-campus sections have limited access to UNITE Media; refer to UNITE Streaming Video Access for On-Campus Students for more details.

FALL SCHEDULE

(Updated April 2nd, 2024)

Use online tools to search all University credit offerings:  Aerospace Engineering's Class Schedules by Department online search tool  Humphrey School of Public Affairs' ClassInfo online search tool  (Note: These tools list ALL offerings - on-campus, including UNITE offerings)

AEROSPACE ENGINEERING

AEM 5321 (also offered as EE 5231) - Linear Systems and Optimal Control (3.0 cr)   Instructor TBA UNITE streams live video of on-campus section on MW 2:30 p.m. - 3:45 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   [EE 3015, CSE grad student] or instr consent  Description:   Properties and modeling of linear systems. Linear quadratic and linear-quadratic-Gaussian regulators. Maximum principle.

AEM 5401 - Intermediate Dynamics (3.0 cr)   Yohannes Ketema   UNITE streams live video of on-campus section on MWF 11:15 a.m.–12:05 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   CSE upper div or grad, 2012, Math 2243  Description:   Three-dimensional Newtonian mechanics, kinematics of rigid bodies, dynamics of rigid bodies, generalized coordinates, holonomic constraints, Lagrange equations, applications.

AEM 5451 (also offered as EE 5251) - Optimal Filtering and Estimation (3.0 cr)   Demoz Gerbe-Egziabher UNITE streams live video of on-campus section on TTh 1:00 p.m. - 2:15 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   [[MATH 2243, STAT 3021] or equiv], CSE grad student] or dept consent; EE 3025, EE 4231 recommended  Description:   Basic probability theory, stochastic processes. Gauss-Markov model. Batch/recursive least squares estimation. Filtering of linear/nonlinear systems. Continuous-time Kalman-Bucy filter. Unscented Kalman filter, particle filters. Applications.

BIOMEDICAL ENGINEERING

BMEN 5001 - Advanced Biomaterials (3.0)  Wei Shen   UNITE streams live video of on-campus section on TTh 11:15 a.m. - 12:30 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   3301 or MatS 3011 or grad student or instr consent   Description:   Commonly used biomaterials. Chemical/physical aspects. Practical examples from such areas as cardiovascular/orthopedic applications, drug delivery, and cell encapsulation. Methods used for chemical analysis and for physical characterization of biomaterials. Effect of additives, stabilizers, processing conditions, and sterilization methods.

BMEN 5401 - Advanced Biomedical Imaging (3.0 cr)   Alexander Opitz UNITE streams live video of on-campus section on TTh 2:30 p.m. - 3:45 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   CSE upper div or grad student or instr consent Description:   Functional biomedical imaging modalities. Principles/applications of technologies that offer high spatial/temporal resolution. Bioelectromagnetic and magnetic resonance imaging. Other modalities.

BMEN 5411 - Neural Engineering (3.0 cr)   Tay Netoff UNITE streams live video of on-campus section on TTh 11:15 a.m. - 12:30 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   BMEN 3401 recommended  Description:   Theoretical basis. Signal processing techniques. Modeling of nervous system, its response to stimulation. Electrode design, neural modeling, cochlear implants, deep brain stimulation. Prosthetic limbs, micturition control, prosthetic vision. Brain machine interface, seizure prediction, optical imaging of nervous system, place cell recordings in hippocampus.

BMEN 5910 - Special Topics in Biomedical Engineering: Biomedical Science Data (3.0 cr)   Matthew Johnson   UNITE streams live video of on-campus section on MW 1:00 p.m. - 2:15 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   CSE student, upper div or grad  Description:   Description coming from department.

BMEN 8001 - Polymeric Biomaterials (3.0 cr)   Chun Wang UNITE streams live video of on-campus section on MW 1:00 p.m. - 2:15 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   [5001, [CHEN 4214 or MATS 4214 or equiv]] or instr consent Description:   Introduction to polymeric biomaterial research. Molecular engineering, characterization of properties, material-cell interaction, biocompatibility/bioactivity. Applications in biology and medicine.

BMEN 8601 - Biomedical Engineering Seminar (1.0 cr)   Seminars and Colloquia taken for credit are offered only as live and archived streaming video - NO downloadable video or audio podcast versions are offered.   Wei Shen   UNITE streams live video of on-campus section on MW 3:35 p.m. - 4:30 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Description:   Lectures and demonstrations of university and industry research introducing students and faculty to methods and goals of biomedical engineering.  For more information, see the Biomedical Engineering Graduate Seminar Web Site .

Looking for a course not listed here? Ask for it! We already offer many College of Science and Engineering courses through UNITE, but are looking for other courses that we can offer through UNITE.  Use our online  Course Request Form . 

NOTE: UNITE WILL NOT TAKE REQUESTS FOR ADDITIONAL COURSES FOR FALL 2024 AFTER AUGUST 1ST, 2024.

COMPUTER SCIENCE AND ENGINEERING

CSCI 5106 - Programming Languages (3.0)  UNITE section enrollment limited by department to 10 (8 graduate and 2 undergraduate) Instructor TBA UNITE streams live video of on-campus section on TTh 1:00 p.m.–2:15 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   4011 or instr consent  Description:   Design and implementation of high-level languages. Course has two parts: (1) language design principles, concepts, constructs; (2) language paradigms, applications. Note: course does not teach how to program in specific languages.

CSCI 5204 (also offered as EE 5364) - Advanced Computer Architecture (3.0 cr)   UNITE section enrollment limited by department to 10 (8 graduate and 2 undergraduate) Instructor TBA UNITE streams live video of on-campus section on TTh 9:45 a.m. - 11:00 a.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   4203 or EE 4363; Credit will not be granted if credit has been received forEE 5364  Description:   Instruction set architecture, processor microarchitecture, memory, I/O systems. Interactions between computer software and hardware. Methodologies of computer design.

CSCI 5421 - Advanced Algorithms and Data Structures (3.0 cr)   UNITE section enrollment limited by department to 10 (8 graduate and 2 undergraduate) Instructor TBA UNITE streams live video of on-campus section on MW 8:15 a.m. - 9:30 a.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   CSCI 4041 or instr consent  Description:   Fundamental paradigms of algorithm and data structure design. Divide-and-conquer, dynamic programming, greedy method, graph algorithms, amortization, priority queues and variants, search structures, disjoint-set structures. Theoretical underpinnings. Examples from various problem domains.

CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming (3.0 cr)   UNITE section enrollment limited by department to 10 (8 graduate and 2 undergraduate) Instructor TBA UNITE streams live video of on-campus section on MW 8:15 a.m. - 9:30 a.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   4041 or instr consent  Description:   Parallel architectures design, embeddings, routing. Examples of parallel computers. Fundamental communication operations. Performance metrics. Parallel algorithms for sorting. Matrix problems, graph problems, dynamic load balancing, types of parallelisms. Parallel programming paradigms. Message passing programming in MPI. Shared-address space programming in openMP or threads.

Looking for a course not listed here? Ask for it! We already offer many College of Science and Engineering courses through UNITE, but are looking for other courses that we can offer through UNITE.  Use our online  Course Request Form .    NOTE: UNITE WILL NOT TAKE REQUESTS FOR ADDITIONAL COURSES FOR FALL 2024 AFTER AUGUST 1ST, 2024.

CSCI 5481 - Computational Techniques for Genomics (3.0 cr)   UNITE section enrollment limited by department to 10 (8 graduate and 2 undergraduate) Instructor TBA UNITE streams live video of on-campus section on MW 1:00 p.m. - 2:15 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   CSCI 4041 or instr consent  Description:   Techniques to analyze biological data generated by genome sequencing, proteomics, cell-wide measurements of gene expression changes. Algorithms for single/multiple sequence alignments/assembly. Search algorithms for sequence databases, phylogenetic tree construction algorithms. Algorithms for gene/promoter and protein structure prediction. Data mining for micro array expression analysis. Reverse engineering of regulatory networks.

CSCI 5525 - Machine Learning (3.0 cr)   UNITE section enrollment limited by department to 10 (8 graduate and 2 undergraduate) Instructor TBA UNITE streams live video of on-campus section on TTh 2:30 p.m. - 3:45 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   Grad student or instr consent  Description:   Models of learning. Supervised algorithms such as perceptrons, logistic regression, and large margin methods (SVMs, boosting). Hypothesis evaluation. Learning theory. Online algorithms such as winnow and weighted majority. Unsupervised algorithms, dimensionality reduction, spectral methods. Graphical models.

CSCI 5541 - Natural Language Processing (3.0 cr)    UNITE section enrollment limited by department to 10 (8 graduate and 2 undergraduate) Instructor TBA UNITE streams live video of on-campus section on TTh 11:15 a.m.– 12:30 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   CSCI 2041  Description:   Computers are poor conversationalists, despite decades of attempts to change that fact. This course will provide an overview of the computational techniques developed in the attempt to enable computers to interpret and respond appropriately to ideas expressed using natural languages (such as English or French) as opposed to formal languages (such as C++ or Python). Topics in this course will include parsing, semantic analysis, machine translation, dialogue systems, and statistical methods in speech recognition.

CSCI 5707 - Principles of Database Systems (3.0 cr)   UNITE section enrollment limited by department to 10 (8 graduate and 2 undergraduate) Instructor TBA UNITE streams live video of on-campus section on TTh 2:30 p.m. - 3:45 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   4041 or instr consent], grad student; Credit will not be granted if credit has been received for CSCI 4707 or INET 4707  Description:   Concepts, database architecture, alternative conceptual data models, foundations of data manipulation/analysis, logical data models, database designs, models of database security/integrity, current trends.

CSCI 8115 - Human-Computer Interaction and User Interface Technology (3.0 cr)   UNITE section enrollment limited by department to 10  Instructor TBA UNITE streams live video of on-campus section on MW 2:30 p.m. - 3:45 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   5115 or instr consent Description:   Current research issues in human-computer interaction, user interface toolkits and frameworks, and related areas. Research techniques, model-based development, gesture-based interfaces, constraint-based programming, event processing models, innovative systems, HCI in multimedia systems.

CSCI 8523 - AI for Earth: Monitoring Changes in the Environment via Deep Learning (3.0) UNITE section enrollment limited by department to 10  Vipin Kumar UNITE streams live video of on-campus section on MW 2:30 p.m.–3:45 a.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   CSci 5523, CSci 5521, or equivalent Description:   Advances in machine learning in conjunction with massive amounts of data from Earth observing satellites offer huge potential for improving our understanding of how the Earth's environment and ecosystems have been changing and how they are being impacted by humans actions and changing climate. Deep learning approaches, that have had phenomenal success in the domain of computer vision and language/speech translation, hold promise in dealing with environmental problems. However, due to challenges that are unique to environmental applications, off-the-shelf deep learning techniques developed for related applications such as computer vision often have limited utility. This class will introduce to the students the promise and challenges in using deep learning techniques to analyze complex, multi-scale, spatio-temporal data for monitoring changes in the Earth and its environment on a global scale.

CSCI 8970 (also offered as DSCI 8970) - Computer Science Colloquium (1.0 cr)   UNITE section enrollment limited by department to 10  Seminars and Colloquia taken for credit are offered only as live and archived streaming video - NO downloadable video or audio podcast versions are offered.   Instructor TBA UNITE streams live video of on-campus section on M 11:15 a.m. - 12:15 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Description:   Recent developments in computer science and related disciplines. Students must attend 13 of the 15 lectures.  For the entire schedule, see the Computer Science & Engineering Colloquia Series Web Site

DATA SCIENCE

DSCI 8970 (also offered as CSCI 8970) - Data Science Colloquium (1.0 cr)   UNITE section enrollment limited by department to 10 Instructor TBA UNITE streams live video of on-campus section on M 11:15 a.m. - 12:15 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Description:   Recent developments in computer science and related disciplines. Students must attend 13 of the 15 lectures.  For the entire schedule, see the Computer Science & Engineering Colloquia Series Web Site

ELECTRICAL AND COMPUTER ENGINEERING

EE 4389W (also offered as EE 5389) - Introduction to Predictive Learning (3.0 cr)   Vladimir Cherkassky UNITE streams live video of on-campus section on MW 2:30 p.m. - 3:45 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   [3025, ECE student] or STAT 3022; computer programming or MATLAB or similar environment is recommended for ECE students Description:   Empirical inference and statistical learning. Classical statistical framework, model complexity control, Vapnik-Chervonenkis (VC) theoretical framework, philosophical perspective. Nonlinear methods. New types of inference. Application studies.

EE 4541 - Digital Signal Processing (3.0 cr)   Georgios Giannakis   UNITE streams live video of on-campus section on MW 9:45 a.m. - 11:00 a.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:    [3015, 3025] or instr consent  Description:   Review of linear discrete time systems and sampled/digital signals. Fourier analysis, discrete/fast Fourier transforms. Interpolation/decimation. Design of analog, infinite-impulse response, and finite impulse response filters. Quantization effects.

EE 5163 - Semiconductor Properties and Devices I (3.0 cr)   Tony Low   UNITE streams live video of on-campus section on MW 11:15 a.m. - 12:30 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:    [3161, 3601, CSE grad student] or dept consent  Description:   Principles/properties of semiconductor devices. Selected topics in semiconductor materials, statistics, and transport. Aspects of transport in p-n junctions, heterojunctions.

EE 5171 - Microelectronic Fabrication (4.0 cr)   Steven Koester   UNITE streams live video of on-campus section on TTh 1:00 p.m. - 2:15 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   CSE grad student or dept consent  Description:   Fabrication of microelectronic devices. Silicon integrated circuits, GaAs devices. Lithography, oxidation, diffusion. Process integration of various technologies, including CMOS, double poly bipolar, and GaAs MESFET.

EE 5181 - Micro and Nanotechnology by Self Assembly (3.0 cr)  - cancelled by department 4/10/24

EE 5231 (also offered as AEM 5321) - Linear Systems and Optimal Control (3.0 cr)   Instructor TBA UNITE streams live video of on-campus section on MW 2:30 p.m. - 3:45 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   [EE 3015, CSE grad student] or instr consent  Description:   Properties and modeling of linear systems. Linear quadratic and linear-quadratic-Gaussian regulators. Maximum principle.

EE 5239 - Introduction to Nonlinear Optimization (3.0)  Mingyi Hong   UNITE streams live video of on-campus section on MW 1:00 p.m. - 2:15 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   [3025, Math 2373, Math 2374, CSE grad student] or dept consent  Description:   Nonlinear optimization. Analytical/computational methods. Constrained optimization methods. Convex analysis, Lagrangian relaxation, non-differentiable optimization, applications in integer programming. Optimality conditions, Lagrange multiplier theory, duality theory. Control, communications, management science applications.

EE 5241 - Optimal Control and Reinforcement Learning (3.0 cr)   Andrew Lamperski   UNITE streams live video of on-campus section on MW 9:45 a.m. - 11:00 a.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   CSE grad student or instructor consent  Description:   A wide variety of control problems such as "walk from home to school via the shortest path" or "maintain a constant temperature" can be modeled using optimization. This course will survey a variety of methods for modeling and solving optimal control problems. In particular, we will cover numerical optimal control, model predictive control, system identification, dynamic programming, and reinforcement learning. Examples from robotics and aerospace systems will be given.

EE 5251 (also offered as AEM 5451) - Optimal Filtering and Estimation (3.0 cr)   Demoz Gerbe-Egziabher UNITE streams live video of on-campus section on TTh 1:00 p.m. - 2:15 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   [[MATH 2243, STAT 3021] or equiv], CSE grad student] or dept consent; EE 3025, EE 4231 recommended  Description:   Basic probability theory, stochastic processes. Gauss-Markov model. Batch/recursive least squares estimation. Filtering of linear/nonlinear systems. Continuous-time Kalman-Bucy filter. Unscented Kalman filter, particle filters. Applications.

EE 5271 - Robot Vision (3.0 cr)   Changhyun Choi   UNITE streams live video of on-campus section on TTh 2:30 p.m. - 3:45 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   [Math 2373 or equivalent; EE 1301 or equivalent basic programming course]  Description:   Modern visual perception for robotics that includes position and orientation, camera model and calibration, feature detection, multiple images, pose estimation, vision-based control, convolutional neural networks, reinforcement learning, deep Q-network, and visuomotor policy learning.

EE 5301 - VLSI Design Automation I (3.0 cr)    Kia Bazargan   UNITE streams live video of on-campus section on TTh 1:00 p.m. - 2:15 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   [2301, CSE grad student] or dept consent  Description:   Basic graph/numerical algorithms. Algorithms for logic/high-level synthesis. Simulation algorithms at logic/circuit level. Physical-design algorithms.

EE 5323 - VSLI Design I (3.0 cr)   Gerald Sobelman This course uses software that is only available to students in CSELabs due to vendor licensing - there is no off-campus software option. Students will need to come to campus to use the software.   UNITE streams live video of on-campus section on MWF 3:35 p.m. - 4:25 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   [2301, 3115, CSE grad student] or dept consent  Description:   Combinational static CMOS circuits. Transmission gate networks. Clocking strategies, sequential circuits. CMOS process flows, design rules, structured layout techniques. Dynamic circuits, including Domino CMOS and DCVS. Performance analysis, design optimization, device sizing.

EE 5329 - VLSI Digital Signal Processing Systems (3.0 cr)   Instructor TBA This course uses software that is only available to students in CSELabs due to vendor licensing - there is no off-campus software option. Students will need to come to campus to use the software.   UNITE streams live video of on-campus section on MWF 3:35 p.m. - 4:25 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   [[5323 or concurrent registration is required (or allowed) in 5323], CSE grad student] or dept consent   Description:   Programmable architectures for signal/media processing. Data-flow representation. Architecture transformations. Low-power design. Architectures for two's complement/redundant representation, carry-save, and canonic signed digit. Scheduling/allocation for high-level synthesis.

EE 5333 - Analog Integrated Circuit Design   Ramesh Harjani   UNITE streams live video of on-campus section on TTh 8:15 a.m. - 9:30 a.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   [3115, CSE grad student] or dept consent  Description:   Fundamental circuits for analog signal processing. Design issues associated with MOS/BJT devices. Design/testing of circuits. Selected topics (e.g., modeling of basic IC components, design of operational amplifier or comparator or analog sampled-data circuit filter).

EE 5340 - Introduction to Quantum Computing and Physical Basics of Computing (3.0 cr)     Ulya Karpuzcu   UNITE streams live video of on-campus section on MW 9:45 a.m. - 11:00 a.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   CSE grad student; A previous course in computer architecture is suggested but not required.  Description:   Physics of computation will explore how physical principles and limits have been shaping paradigms of computing. A key goal of this course is to understand how (and to what extent) a paradigm shift in computing can help with emerging energy problems. Topics include physical limits of computing, coding and information theoretical foundations, computing with beyond-CMOS devices, reversible computing, quantum computing, stochastic computing.

EE 5351 - Applied Parallel Programming (3.0 cr) - cancelled by department 4/10/24

EE 5364 (also offered as CSCI 5204) - Advanced Computer Architecture (3.0 cr)   UNITE section enrollment limited by department to 10  Pen-Chung Yew UNITE streams live video of on-campus section on TTh 9:45 a.m. - 11:00 a.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:    [[4363 or CSci 4203], CSE grad student] or dept consent; Credit will not be granted if credit has been received for: CSCI 5204  Description:   Instruction set architecture, processor microarchitecture. Memory and I/O systems. Interactions between computer software and hardware. Methodologies of computer design.

EE 5389 (also offered as EE 4389W) - Introduction to Predictive Learning (3.0 cr)   Vladimir Cherkassky UNITE streams live video of on-campus section on MW 2:30 p.m. - 3:45 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   EE 3025, STAT 3022 or equivalent; computer programming or MATLAB or similar environment is recommended. Description:   Empirical inference and statistical learning. Classical statistical framework, model complexity control, Vapnik-Chervonenkis (VC) theoretical framework, philosophical perspective. Nonlinear methods. New types of inference. Application studies.

EE 5531 - Probability and Stochastic Processes (3.0 cr)   Soheil Mohajer   UNITE streams live video of on-campus section on MW 11:15 a.m. - 12:30 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:    [3025, CSE grad student] or dept consent  Description:   Probability, random variables and random processes. System response to random inputs. Gaussian, Markov and other processes for modeling and engineering applications. Correlation and spectral analysis. Estimation principles. Examples from digital communications and computer networks.

EE 5561 - Image Processing and Applications: From linear filters to artificial intelligence (3.0)  Mehmet Akcakaya   UNITE streams live video of on-campus section on TTh 1:00 p.m. - 2:15 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:    [4541, 5581, CSE grad student] or instr consent  Description:   Image enhancement, denoising, segmentation, registration, and computational imaging. Sampling, quantization, morphological processing, 2D image transforms, linear filtering, sparsity and compression, statistical modeling, optimization methods, multiresolution techniques, artificial intelligence concepts, neural networks and their applications in classification and regression tasks in image processing. Emphasis is on the principles of image processing. Implementation of algorithms in Matlab/Python and using deep learning frameworks.

EE 5601 - Introduction to RF/Microwave Engineering (3.0 cr)   Rhonda Franklin   UNITE streams live video of on-campus section on MW 1:00 p.m. - 2:15 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   [EE 3601, CSE grad student] or dept consent  Description:   Fundamentals of EM theory and transmission lines concepts. Transmission lines and network analysis. CAD tool. Lumped circuit component designs. Passive circuit components. Connectivity to central communication theme.

EE 5624 - Optical Electronics (4.0 cr)  - cancelled by department 4/10/24

EE 5653 - Physical Principles of Magnetic Materials (3.0 cr)   Randall Victora   UNITE streams live video of on-campus section on MWF 2:30 p.m. - 3:20 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   CSE grad student or dept consent  Description:   Physics of diamagnetism, paramagnetism, ferromagnetism, antiferromagnetism, ferrimagnetism. Ferromagnetic phenomena. Static/dynamic theory of micromagnetics, magneto-optics, and magnetization dynamics. Magnetic material applications.

EE 5811 - Biological Instrumentation (3.0) - cancelled by department 4/10/24

EE 5940 - Special Topics in Electrical Engineering I (3.0) - cancelled by department 4/10/24

EE 8351 - Design Automation Techniques for Variation-Aware Computer (3.0 cr)   Instructor TBA UNITE streams live video of on-campus section on MW 9:45 a.m. - 11:00 a.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   CSE grad student. Some background in VLSI design and/or design automation is suggested but not required. Such prior exposure will make the experience in the class much more meaningful.  Description:   High-performance chip design can only be performed with the assistance of design automation tools that comprehend the needs of the designer and deliver solutions that can correctly analyze and optimize these systems. The objective of this class is to provide a view of this emerging universe and acquaint students with new research in this area. Specific topics to be covered include 1) Overview of technology trends and emerging systems 2) Variation-aware design and 3) Design automation issues.

EE 8591 - Predictive Learning from Data   Instructor TBA UNITE streams live video of on-campus section on TTh 11:15 a.m. - 12:30 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   CSE grad student or instr consent  Description:   Methods for estimating dependencies from data have been traditionally explored in such diverse fields as: statistics (multivariate regression and classification), engineering (pattern recognition, system identification), computer science (artificial intelligence, machine learning, data mining) and bioinformatics. Recent interest in learning methods is triggered by the widespread use of digital technology and availability of data. Unfortunately, developments in each field are seldom related to other fields. This course is concerned with estimation of predictive data-analytic models that are estimated using past data, but are used for prediction or decision making with new data. This course will first present general conceptual framework for learning predictive models from data, using Vapnik-Chervonenkis (VC) theoretical framework, and then discuss various methods developed in statistics, pattern recognition and machine learning. Course descriptions will emphasize methodological aspects of machine learning, rather than development of new algorithms.

EE 8660 - Magnetics Seminar (1.0 cr)   Seminars and Colloquia taken for credit are offered only as live and archived streaming video - NO downloadable video or audio podcast versions are offered.   Beth Stadler   UNITE streams live video of on-campus section on F 1:25 p.m. - 2:15 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Description:   Current literature, individual assignments (no online seminar schedule available to share).

INDUSTRIAL AND SYSTEMS ENGINEERING

IE 3521 - Statistics, Quality and Reliability (4.0 cr)   Instructor TBA UNITE streams live video of on-campus section on TTh 3:35 p.m. - 5:20 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   MATH 1372 or equiv  Description:   Random variables/probability distributions, statistical sampling/measurement, statistical inferencing, confidence intervals, hypothesis testing, single/multivariate regression, design of experiments, statistical quality control, quality management, reliability, maintainability.

IE 5511 - Human Factors and Work Analysis (4.0 cr)    Instructor TBA  UNITE streams live video of on-campus section on TTh 10:10 a.m. - 12:05 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   Upper div CSE or grad student; Credit will not be granted if credit has been received for: HUMF 5211, IE 4511 or ME 5211 Description:   Human factors engineering (ergonomics), methods engineering, and work measurement. Human-machine interface: displays, controls, instrument layout, and supervisory control. Anthropometry, work physiology and biomechanics. Work environmental factors: noise, illumination, toxicology. Methods engineering, including operations analysis, motion study, and time standards.

IE 5531 - Engineering Optimization I (4.0 cr)    Instructor TBA  UNITE streams live video of on-campus section on MW 11:15 a.m. - 1:10 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   Upper div or grad student or CNR  Description:   Linear programming, simplex method, duality theory, sensitivity analysis, interior point methods, integer programming, branch/bound/dynamic programming. Emphasizes applications in production/logistics, including resource allocation, transportation, facility location, networks/flows, scheduling, production planning.

IE 5532 - Stochastic Models (3.0 cr)   Instructor TBA UNITE streams live video of on-campus section on TTh 10:10 a.m. - 12:05 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   Undergraduate probability and statistics. Familiarity with computer programming in a high level language.  Description:   Introduction to stochastic modeling and stochastic processes. Probability review, random variables, discrete- and continuous-time Markov chains, queueing systems, simulation. Applications to industrial and systems engineering including production and inventory control.

IE 8521 - Optimization (4.0 cr)   Instructor TBA UNITE streams live video of on-campus section on TTh 1:25 p.m. - 3:20 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   Familiarity with linear algebra and calculus. Description: Theory and applications of linear and nonlinear optimization. Linear optimization: simplex method, convex analysis, interior point method, duality theory. Nonlinear optimization: interior point methods and first-order methods, convergence and complexity analysis. Applications in engineering, economics, and business problems.

IE 8564 - Optimization for Machine Learning (4.0 cr)   Instructor TBA UNITE streams live video of on-campus section on M 2:45 p.m. - 6:05 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   Graduate Student Description: Machine learning has been widely used in many areas such as computer vision, search engines, speech recognition, robotics, recommender systems, bioinformatics, social networks, and finance. It has become an important tool in prediction and data analysis. This course provides a comprehensive overview of important optimization models for machine learning. It also systematically provides a theoretical and computational study on various optimization methods for solving these models and more general problems.

MECHANICAL ENGINEERING

ME 5312 -  Solar Thermal Technologies(3.0) Natasha Wright UNITE streams live video of on-campus section on MW 10:10 p.m.–12:05 a.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   [3333, CSE upper Div] or grad student Description:   Solar radiation fundamentals. Measurement/processing needed to predict solar irradiance dependence on time, location, and orientation. Characteristics of components in solar thermal systems: collectors, heat exchangers, thermal storage. System performance, low-temperature applications. Concentrating solar energy, including solar thermo-chemical processes, to produce hydrogen/solar power systems and photovoltaics. Solar design project.

ME 8446 - Advanced Combustion (3.0) Sayan Biswas UNITE streams live video of on-campus section on TTh 11:15 a.m.–12:05 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   Undergrad courses in thermodynamics, fluid mechanics, heat transfer, IT grad student; 5446 or 8641 highly recommended Description:   Fundamental understanding of linkage between thermodynamics, chemical kinetics, and transport phenomena in combustion systems. Heat release rate, flame stability, and emissions. How those issues arise in furnaces, internal combustion engines, and rockets.  

STAT 5021 - Statistical Analysis (4.0 cr)   Enrollment in STAT 5021 includes on-campus lab in section 2 of the lab sections (T 10:10 a.m. - 11:00 a.m.), live-streamed from a UNITE classroom   Instructor TBA UNITE streams live video of on-campus lecture section on MWF 10:10 a.m. - 11:00 a.m.  UNITE streams live video of on-campus lab section on T 10:10 a.m. - 11:00 a.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   college algebra or instr consent; credit will not be granted if credit has been received for STAT 3011  Description:   Intensive introduction to statistical methods for graduate students needing statistics as a research technique.

STAT 5102 - Theory of Statistics II (4.0 cr)   Enrollment in STAT 5101 includes on-campus lab in section 2 of the lab sections (T 2:30 p.m. - 3:20 p.m.), live-streamed from a UNITE classroom   Instructor TBA UNITE streams live video of on-campus lecture section on MWF 2:30 p.m. - 3:20 p.m.  UNITE streams live video of on-campus lab section on T 2:30 p.m. - 3:20 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   5101 or Math 5651  Description:   Sampling, sufficiency, estimation, test of hypotheses, size/power. Categorical data. Contingency tables. Linear models. Decision theory.

STAT 5302 - Applied Regression Analysis (4.0 cr)   Enrollment in STAT 5302 includes on-campus lab in section 2 of the lab sections (Th 11:15 a.m. - 12:05 p.m.), live-streamed from a UNITE classroom   Instructor TBA UNITE streams live video of on-campus lecture section on MWF 1:25 p.m. - 2:15 p.m.  UNITE streams live video of on-campus lab section on Th 11:15 a.m. - 12:05 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   3032 or 3022 or 4102 or 5021 or 5102 or instr consent Please note this course generally does not count in the Statistical Practice BA or Statistical Science BS degrees. Please consult with a department advisor with questions.  Description:   Simple, multiple, and polynomial regression. Estimation, testing, prediction. Use of graphics in regression. Stepwise and other numerical methods. Weighted least squares, nonlinear models, response surfaces. Experimental research/applications.

STAT 5421 - Statistical Analysis (3.0 cr)   Instructor TBA UNITE streams live video of on-campus section on MWF 1:25 p.m. - 2:15 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   STAT 3022 or 3032 or 3301 or 5302 or 4051 or 8051 or 5102 or 4102  Description:   Varieties of categorical data, cross-classifications, contingency tables. Tests for independence. Combining 2x2 tables. Multidimensional tables/log linear models. Maximum-likelihood estimation. Tests for goodness of fit. Logistic regression. Generalized linear/multinomial-response models.

STAT 5511 - Time Series Analysis (3.0 cr)   Instructor TBA UNITE streams live video of on-campus section on MWF 2:30 p.m. - 3:20 p.m.  Archived videos typically available to UNITE-enrolled students within an hour  Prerequisites:   STAT 4102 or STAT 5102 Description:   Characteristics of time series. Stationarity. Second-order descriptions, time-domain representation, ARIMA/GARCH models. Frequency domain representation. Univariate/multivariate time series analysis. Periodograms, non parametric spectral estimation. State-space models.

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