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Undergraduate Research at Purdue CS

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:

• Department of Computer Science Research Areas
• Department of Computer Science Research Seminars
• Purdue University Office of Undergraduate Research
• Purdue University Center for Programming Principles and Software Systems (PURPL)
• Purdue Summer Undergraduate Research Fellowship Program (SURF)
• Discovery Park Undergraduate Research Internship Program (DURI)

Research Opportunities off-campus:

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

Department of Computer Science, 305 N. University Street, West Lafayette, IN 47907

Purdue University Indianapolis, 723 W. Michigan St., Indianapolis, IN 46202

Phone: (765) 494-6010 • Fax: (765) 494-0739

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Trouble with this page? Accessibility issues ? Please contact the College of Science .

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.
• This is not a complete list of subfields, and every subfield is different!
• Positions will usually outline the prerequisite courses or skillsets that are expected, so use those to gauge whether you would be a competitive applicant for the position. Otherwise, you can always reach out to the faculty or graduate students you are interested in working with to see if there are other openings that match your background better.
• 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?
• 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. If you are interested in theory, the process is slightly different since there are fewer theory researchers. Your best bet is reaching out directly to theory faculty  with some topics of interest, and continuing to take theory-related courses.

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 researchers 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. This can help you discover what you may be interested in.
• Connect with PhD students about undergraduate opportunities. Faculty are very busy, so most undergraduate research opportunities are with PhD students.

Step 2: Discuss your research interests with a potential 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 PhD Student with the knowledge of their projects and how your experience and background can benefit them. Contact them 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.

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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.

*Updated August 1, 2024
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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)

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

Available for single-semester IW, 2024-2025. No longer available for senior thesis advising.

• 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

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

Available for Fall 2024 single-semester IW advising, only

• 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

• Research area: theory

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: algorithmic decision making, machine learning and society
• Theoretical foundations for algorithmic decision making (e.g. mathematical modeling of data-driven decision processes, societal level dynamics)
• Societal impacts of algorithms and AI through a socio-technical lens (e.g. normative implications of worst case ML metrics, prediction and model arbitrariness)
• Machine learning for social impact domains, especially education (e.g. responsible development and use of LLMs for education equity and access)
• Evaluation of human-AI decision making using statistical methods (e.g. causal inference of long term impact)

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

• My.SiebelSchool

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.

Research Opportunities

Undergraduate research in computer science.

For specific information on undergraduate research opportunities in Computer Science visit  https://csadvising.seas.harvard.edu/research/ .

General Information about Undergraduate Research

Opportunities for undergraduates to conduct research in engineering, the applied sciences, and in related fields abound at Harvard. As part of your coursework, or perhaps as part of individual research opportunities working with professors, you will have the chance to  take part in or participate in  some extraordinary projects covering topics ranging from bioengineering to cryptography to environmental engineering.

Our dedicated undergraduate research facilities and Active Learning Labs also provide opportunities for students to engage in hands-on learning. We encourage undergraduates from all relevant concentrations to tackle projects during the academic year and/or over the summer.

Keep in mind, many students also pursue summer research at private companies and labs as well as at government institutions like the National Institutes of Health.

If you have any questions, please contact or stop by the Office of Academic Programs, located in the Science and Engineering Complex, Room 1.101, in Allston.

Research FAQs

The SEAS website has a wealth of information on the variety of cross-disciplinary research taking place at SEAS. You can view the concentrations available at SEAS here , as well as the research areas that faculty in these concentrations participate in. Note that many research areas span multiple disciplines; participating in undergraduate research is an excellent way to expand what you learn beyond the content of the courses in your concentration! 

To view which specific faculty conduct research in each area, check out the All Research Areas section of the website. You can also find a helpful visualization tool to show you the research interests of all the faculty at SEAS, or you can filter the faculty directory by specific research interests. Many faculty’s directory entry will have a link to their lab’s website, where you can explore the various research projects going on in their lab.

The Centers & Initiatives page shows the many Harvard research centers that SEAS faculty are members of (some based at SEAS, some based in other departments at Harvard). 

Beyond the website, there are plenty of research seminars and colloquia happening all year long that you can attend to help you figure out what exactly you are interested in. Keep an eye on the calendar at https://events.seas.harvard.edu ! 

There are several events that are designed specifically for helping undergraduate students get involved with research at SEAS, such as the Undergraduate Research Open House and Research Lightning Talks . This event runs every fall in early November and is a great opportunity to talk to representatives from research labs all over SEAS. You can find recordings from last year’s Open House on the SEAS Undergraduate Research Canvas site .

Most of our faculty have indicated that curiosity, professionalism, commitment and an open mind are paramount. Good communication skills, in particular those that align with being professional are critical. These skills include communicating early with your mentor if you are going to be late to or miss a meeting, or reaching out for help if you are struggling to figure something out. Good writing skills and math (calculus in particular) are usually helpful, and if you have programming experience that may be a plus for many groups. So try to take your math and programming courses early (first year) including at least one introductory concentration class, as those would also add to your repertoire of useful skills.

Adapted from the Life Sciences Research FAQs

Start by introducing yourself and the purpose of your inquiry (e.g. you’d like to speak about summer research opportunities in their lab). Next, mention specific aspects of their research and state why they interest you (this requires some background research on your part). Your introduction will be stronger if you convey not only some knowledge of the lab’s scientific goals, but also a genuine interest in their research area and technical approaches.

In the next paragraph tell them about yourself, what your goals are and why you want to do research with their group. Describe previous research experience (if you have any). Previous experience is, of course, not required for joining many research groups, but it can be helpful. Many undergraduates have not had much if any previous experience; professors are looking for students who are highly motivated to learn, curious and dependable.

Finally, give a timeline of your expected start date, how many hours per week you can devote during the academic term, as well as your summer plans.

Most faculty will respond to your email if it is clear that you are genuinely interested in their research and have not simply sent out a generic email. If you don’t receive a response within 7-10 days, don’t be afraid to follow up with another email. Faculty are often busy and receive a lot of emails, so be patient.

There are several ways that undergraduate research can be funded at SEAS. The Program for Research in Science and Engineering ( PRISE ) is a 10-week summer program that provides housing in addition to a stipend for summer research. The Harvard College Research Program ( HCRP ) is available during the academic year as well as the summer.  The Harvard University Center for the Environment ( HUCE ) has a summer undergraduate research program. The Harvard College Office of Undergraduate Research and Fellowships ( URAF ) has more information on these, as well as many other programs.

Students that were granted Federal Work Study as part of their financial aid package can use their Work Study award to conduct undergraduate research as well (research positions should note that they are work-study eligible to utilize this funding source).  

Research labs may have funding available to pay students directly, though we encourage you to seek out one of the many funding options available above first.

Yes! Some students choose to do research for course credit instead of for a stipend. To do so for a SEAS concentrations, students must enroll in one of the courses below and submit the relevant Project Application Form on the Course’s Canvas Page:

• Applied Mathematics 91r (Supervised Reading and Research)
• Computer Science 91r (Supervised Reading and Research)
• Engineering Sciences 91r (Supervised Reading and Research)

In general, you should expect to spend a minimum of one semester or one summer working on a project. There are many benefits to spending a longer period of time dedicated to a project. It’s important to have a conversation early with your research PI (“Principal Investigator”, the faculty who runs your research lab or program) to discuss the intended timeline of the first phase of your project, and there will be many additional opportunities to discuss how it could be extended beyond that.

For students who are satisfied with their research experience, remaining in one lab for the duration of their undergraduate careers can have significant benefits. Students who spend two or three years in the same lab often find that they have become fully integrated members of the research group. In addition, the continuity of spending several years in one lab group often allows students to develop a high level of technical expertise that permits them to work on more sophisticated projects and perhaps produce more significant results, which can also lead to a very successful senior thesis or capstone design project. 

However, there is not an obligation to commit to a single lab over your time at Harvard, and there are many reasons you may consider a change:

• your academic interests or concentration may have changed and thus the lab project is no longer appropriate
• you would like to study abroad (note that there is no additional cost in tuition for the term-time study abroad and Harvard has many fellowships for summer study abroad programs)
• your mentor may have moved on and there is no one in the lab to direct your project (it is not unusual for a postdoctoral fellow who is co-mentoring student to move as they secure a faculty position elsewhere)
• the project may not be working and the lab hasn’t offered an alternative
• or there may be personal reasons for leaving.  It is acceptable to move on

If you do encounter difficulties, but you strongly prefer to remain in the lab, get help.  Talk to your PI or research mentor, your faculty advisor or concentration advisor, or reach out to [email protected] for advice. The PI may not be aware of the problem and bringing it to their attention may be all that is necessary to resolve it.

Accepting an undergraduate into a research group and providing training for them is a very resource-intensive proposition for a lab, both in terms of the time commitment required from the lab mentors as well as the cost of laboratory supplies, reagents, computational time, etc. It is incumbent upon students to recognize and respect this investment.

• One way for you to acknowledge the lab’s investment is to show that you appreciate the time that your mentors set aside from their own experiments to teach you. For example, try to be meticulous about letting your mentor know well in advance when you are unable to come to the lab as scheduled, or if you are having a hard time making progress. 
• On the other hand, showing up in the lab at a time that is not on your regular schedule and expecting that your mentor will be available to work with you is unrealistic because they may be in the middle of an experiment that cannot be interrupted for several hours. 
• In addition to adhering to your lab schedule, show you respect the time that your mentor is devoting to you by putting forth a sincere effort when you are in the lab.  This includes turning off your phone, ignoring text messages, avoiding surfing the web and chatting with your friends in the lab etc. You will derive more benefit from a good relationship with your lab both in terms of your achievements in research and future interactions with the PI if you demonstrate a sincere commitment to them.
• There will be “crunch” times, maybe even whole weeks, when you will be unable to work in the lab as many hours as you normally would because of midterms, finals, paper deadlines, illness or school vacations. This is fine and not unusual for students, but remember to let your mentor know in advance when you anticipate absences. Disappearing from the lab for days without communicating with your mentor is not acceptable. Your lab mentor and PI are much more likely to be understanding about schedule changes if you keep the lines of communication open but they may be less charitable if you simply disappear for days or weeks at a time. From our conversations with students, we have learned that maintaining good communication and a strong relationship with the lab mentor and/or PI correlates well with an undergraduate’s satisfaction and success in the laboratory.
• Perhaps the best way for you to demonstrate your appreciation of the lab’s commitment is to approach your project with genuine interest and intellectual curiosity. Regardless of how limited your time in the lab may be, especially for first-years and sophomores, it is crucial to convey a sincere sense of engagement with your project and the lab’s research goals. You want to avoid giving the impression that you are there merely to fulfill a degree requirement or as a prerequisite for a post-graduate program.

There are lots of ways to open a conversation around how to get involved with research.

• For pre-concentrators: Talk to a student who has done research. The Peer Concentration Advisor (PCA) teams for Applied Math , Computer Science and Engineering mention research in their bios and would love to talk about their experience. Each PCA team has a link to Find My PCA which allows you to be matched with a PCA based on an interest area such as research. 
• For SEAS concentrators: Start a conversation with your ADUS, DUS, or faculty advisor about faculty that you are interested in working with. If you don’t have a list already, start with faculty whose courses you have taken or faculty in your concentration area. You may also find it helpful to talk with graduate student TFs in your courses about the work they are doing, as well as folks in the Active Learning Labs, as they have supported many students working on research and final thesis projects.
• For all students: Attend a SEAS Research Open House event to be connected with lab representatives that are either graduate students, postdocs, researchers or the PI for the labs. If you can’t attend the event, contact information is also listed on the Undergraduate Research Canvas page for follow-up in the month after the event is hosted. 

For any student who feels like they need more support to start the process, please reach out to [email protected] so someone from the SEAS Taskforce for Undergraduate Research can help you explore existing resources on the Undergraduate Research Canvas page . We especially encourage first-generation and students from underrepresented backgrounds to reach out if you have any questions.

In Computer Science

• First-Year Exploration
• Concentration Information
• Secondary Field
• Senior Thesis
• AB/SM Information
• Student Organizations
• How to Apply
• PhD Timeline
• PhD Course Requirements
• Qualifying Exam
• Committee Meetings (Review Days)
• Committee on Higher Degrees
• Research Interest Comparison
• Collaborations
• Cross-Harvard Engagement
• Lecture Series
• Clubs & Organizations
• Centers & Initiatives
• Alumni Stories
• Graduate Student Stories
• Undergraduate Student Stories

Carnegie Mellon University School of Computer Science

Scs undergraduate research, independent study and honors undergraduate research thesis.

SCS undergraduates generally participate in research projects in two ways: as independent study or as an honors undergraduate research thesis. (Often, in fact, the former leads to the latter.)

You can start your research journey by exploring faculty research projects on the SCS Research Portal and comparing how they align with your own goals and interests. You can also examine our list of undergraduate thesis topics and advisors from previous years to understand what's possible at the undergrad level. Finally, you can check out the university's Meeting of the Minds during the spring semester, when students present the results of their work.

SCS also hosts summer research programs designed to give undergrads the chance to gain valuable research experience while considering their plans after graduation.

Explore Summer Research

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Undergraduate research opportunities, get involved.

Duke undergraduates have numerous opportunities to gain hands-on project and research experience in Computer Science.  A wide range of research projects guided by Duke's world-class faculty engage undergraduates, who often become co-authors on papers in major academic conferences. Undergraduates can pursue independent study courses guided by faculty, participate in the summer research and/or the  Identity in Computing Research  programs, and graduate with a distinction in research.

To stay tapped in and receive info about the latest Computer Science opportunities and events, add yourself to our Duke mailing list [email protected] ! Go to: https://lists.duke.edu/sympa  and enter "compsci" in the search box to find the CS Undergraduate listserv.

Graduation with Distinction » Alumni who Graduated with Distinction »

If you meet the requirements, including completion of a substantial project, you may qualify to graduate with distinction.

• Independent Study

Interested in pursuing independent study of computer science research or non-research projects in a specific field of interest with a faculty member?

Undergraduate Project Showcase

This event celebrates student inquiry in computer science. Students present posters on projects from mentored research, class projects, and independent work.

CS+ Program Summer Research »

Not sure what to do this summer? Enjoy computer science and want to explore in more depth? Check out some projects Computer Science faculty are working on and are seeking help for!

Research Resources

• Getting into Research as an Undergraduate:   Information and guidance from Computing Research Association (CRA)
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Undergraduate Research

Honors majors are required to complete two consecutive semesters of research . Other advanced undergraduate students are also encouraged to seek research opportunities with regular full-time faculty.

Why research?

Besides the intellectual challenge, there are many practical advantages in getting engaged in research.

• You must have some research experience if you intend to pursue a Ph.D. after you graduate, whether or not you take gap years. The recommendation letter you get from your research advisor is usually one of the most important piece of material in your graduate school application.
• Research is much more challenging than classes. If you are doing very well in classes, you should consider doing research. Unlike homework, projects and exams which deal with easily-solvable problems, research projects are open-ended, take a much longer time to solve and is a lot more difficult.
• Research projects are usually collaborative. As a result of working closely with PhD students and your faculty advisor, you end up making strong connections with them. These connections may become very handy when it comes to being recommended to graduate schools or industry jobs.

All the above benefits do not come by easily, as research is a serious undertaking. Typically, the workload of research is equal to that of one or two regular classes. Therefore, make sure you can devote the required time and energy before searching for research opportunities.

How to prepare yourself for research

Discover your research interests

Contrary to what some NYU advisers may tell you, you should take as many CS classes as early as possible . To make room for CS classes, postpone your humanities and other general class requirements to your senior year if possible. Doing many CS classes early on allows you to start taking advanced undergraduate classes (the electives) and graduate-level classes in your junior or even sophomore year. Sample a few of these advanced classes in different areas and you will find out what you like and what you are particular good at.

You should consider attending the CS colloquium in the spring. The colloquiums in the spring are typically given by faculty job candidates. They target a broad audience. As such, they provide a good overview on the current state-of-art in a specific field of research.

Find a faculty research advisor

The best approach is to take an advanced class from a full-time faculty member who has active research projects . You need to do really, really well in his/her class. As faculty members usually teach classes in their area of research, taking their classes gives you some required background to do research in that area. Faculty members are also more open to providing research opportunities to top students in their class.

You can browse the homepages of individual faculty to find out his/her research interests and active projects. For the list of research areas and the corresponding faculty, please see here .

You may also directly email faculty members to ask for research opportunities without having taking their classes. In this case, you should attach an informal transcript and your Github projects to show your level of experience.

Summer is a great time to gain research experience. Faculty research advisers typically provide funding to undergraduates who have demonstrated productivity in the projects. Sometimes, faculty advisers also fund undergraduates during normal semester time. As such funding comes from a faculty member's own research grant, it varies across individual faculty and you should talk to your faculty research advisor about funding.

The department has a dedicated fund for undergraduate summer research. You need to be nominated by a faculty member. Again, talk to your research advisor about this.

NYU also provides the Dean's Undergraduate Research Fund that you can apply for.

Getting advice

Every Fall semester, the department runs a "how to prepare for graduate school" panel where faculty and interested students get together to discuss their graduate-school plans. The undergraduate advisor will advertise this event via email.

You are welcome to ask for advice in person from individual faculty member that you've taken classes from, the undergraduate director and administrator.

Getting credits for research

Undergraduate students can get credits for their research work by registering for either of the following two courses.

• CSCI-UA.0520/0521 (Undergraduate Research)
• CSCI-UA.0997/0998 (Independent Study)

CSCI-UA.0520/0521 Undergraduate Research

To fulfill the research requirement, honors students are required to register for CSCI-UA.0520/0521 for two consecutive semesters, starting in their sixth semester of study (spring of junior year). Non-honors students may also register for this course with either a one or two semester commitment. In order to register for this course, the student must have an approved research proposal and a faculty sponsor, who will have agreed to guide and review the research project. The faculty sponsor will need to send email to the Program Administrator confirming the arrangement.

At the conclusion of the research project, the student will be required to submit a write-up (or a thesis for Honors students) on the research work, which the student can then present at NYU's Undergraduate Research Conference .

CSCI-UA.0997/0998 Independent Study

Honors and non-honors students may also participate in research projects and receive credit by registering for CSCI-UA.0997/0998 , which may be taken for either two or four credits per semester. Research done under Independent Study will not count toward the CS major and will not fulfill any program requirements. The steps for registering for the Independent Study course are similar to the ones listed above: the student must have an approved research proposal and a faculty sponsor.

Requirements for Independent Study in Computer Science:

• Student must be a declared Computer Science major
• Student must have at least a 3.5 GPA
• Student must have completed at least 50% of the Computer Science major courses

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Research Opportunities

As a student at Johns Hopkins, the nation’s leading research university, you’ll have opportunities that other schools simply cannot offer.

Our collaborative culture and the emphasis we place on the integration of research and education means that as an undergraduate, you’ll have the chance to conduct research and create knowledge, working side-by-side with renowned faculty from across the School of Engineering as well as with researchers and clinicians from the Johns Hopkins Schools of Medicine and Public Health.

Our stellar reputation with employers from across a wide variety of industries and the access you’ll have to our global alumni network translate into excellent with internship and career opportunities for our students.

Undergraduate Research Opportunities

Approximately 40% of computer science undergraduates elect to participate in research. Whether on the Homewood campus, the medical campus, or at the Johns Hopkins University Applied Physics Laboratory, there are so many ways to get involved.

Interested? Here are some suggestions for how to get started:

• Speak to your faculty advisor about your interest in conducting research and talk to other students about their experiences.
• Review the computer science faculty members’ bios and lab pages, identify faculty whose research interests align with yours, and get in touch with them to let them know that you want to participate in their research activities.
• Check out the many Johns Hopkins-sponsored undergraduate research opportunities .
• The Senior Honors Thesis program (Undergraduate Advising Manual).
• The Pistritto Research Fellowship —this fellowship is an application-based program that provides an annual stipend for students doing research in the area of information visualization. A call for applications is emailed to undergraduates each spring for the upcoming academic year. Fellowship recipients may choose to pursue their research during the summer or during the regular school year and in conjunction with a sponsoring faculty member.
• CS Undergraduate Research Support – students may apply for partial funding support from the CS department when conducting research with a CS faculty member. The faculty member must agree to pay 50% of the support requested. Use this form to apply.
• Visit the national Computing Research Association’s website for information about undergraduate research programs.
• Check out the NSF listing of summer Research Experiences for Undergraduates (REU) sites .

Visit the WSE Advising FAQ page for details about how to register for research, found in the Independent Academic Work section.

2024 Research Projects

Learn about the amazing research our undergraduates are pursuing this year.

CLERC: A Dataset for Legal Case Retrieval and Retrieval-Augmented Analysis Generation Abe Hou (BS ’25)

Faculty Research Advisor: Benjamin Van Durme

Abstract: Legal professionals need to write analyses that rely on citations to relevant precedents (i.e., previous case decisions). Intelligent systems assisting legal professionals in writing such documents provide great benefits, but are challenging to design. Such systems need to help locate, summarize, and reason over salient precedents in order to be useful. To enable systems for such tasks, we work with legal professionals to transform a large, open-source legal corpus into a dataset supporting two important backbone tasks: information retrieval and retrieval-augmented generation. This dataset, which we term CLERC (Case Law Evaluation Retrieval Corpus), is constructed for training and evaluating models on their ability to (1) find corresponding citations for a given piece of legal analysis and to (2) compile the text of these citations (as well as previous context) into a cogent analysis that supports a reasoning goal. We benchmark state-of-the-art models on CLERC, showing that current approaches still struggle: GPT-4o generates analyses with the highest ROUGE F-scores but hallucinates the most, while zero-shot IR models only achieve 48.3% recall@1000.

Full paper >>

About Abe: Abe Hou is an undergraduate senior, triple majoring in computer science, sociology, and pure math. He is interested the bidirectional interactions between AI and society, specifically on 1) building natural language processing (NLP) and AI applications for public policies and law and 2) AI safety and governance. He has published four papers at top computer science conferences and founded the Technology and Policy Society at Johns Hopkins , a student group sponsored by the Johns Hopkins Data Science and AI Institute . Abe has also served as the director of programming at the Johns Hopkins Undergraduate Law Review for two years. He is currently working on NLP for law and generative agent-based modeling for public policy. In his free time, Abe loves poetry and soccer and hosts a book club at the Center for Language and Speech Processing .

Pure Demand Operational Semantics with Applications to Program Analysis Robert Zhang, Engr ’23 (BS/MSE)

Faculty Research Advisor: Scott Smith

Abstract: Our work develops novel minimal-state operational semantics for higher-order functional languages that use only the call stack and a program point or lexical level as complete state information—there is no environment, substitution, or continuation. We carry out a proof that this form of operational semantics is equivalent to standard presentations. This maximal compression of the program state opens the door to potential new applications; we define a program analysis as a direct finitization of these operational semantics. The program analysis that naturally emerges has a number of novel and interesting properties compared to standard program analyses for higher-order programs; for example, it can infer recurrences and does not need value widening. We formally define the analysis and implement it in OCaml. Evaluating our program analysis against related work, it analyzes most benchmarks orders of magnitude faster and more accurately, and thanks to our analysis being purely rule-based, it makes for much more straightforward implementation and mechanization.

About Robert: Robert Zhang recently graduated from John Hopkins with a combined BS/MSE degree in computer science. They are interested in many topics spanning programming languages and formal methods—particularly program analysis verification, and synthesis and their applications to real-world software. Since 2022, Robert has been collaborating with Scott Smith on developing novel operational semantics and an associated program analysis technique, which led to a publication at Object-Oriented Programming, Systems, Languages, and Applications—a top conference in programming languages. For this work, Robert was selectively chosen as the sole recipient of the 2023 Masson Fellowship based on research merit. Aside from research, Robert served as a course assistant for six consecutive semesters over the past three years, during which they helped with Principles of Programming Languages, Functional Programming in Software Engineering, and Full-Stack JavaScript. They served on the executive board of the ACM JHU Chapter, in which they helped organize and participated in student-oriented events bridging academia and industry. They are also a member of Upsilon Pi Epsilon, the nation’s first computer science honor society. This fall, Robert will join the University of Texas at Austin to pursue a PhD in computer science, specializing in programming languages and formal methods.

Student Spotlight: Alisa Yang & Michelle Wang

The third-year computer science students partnered with Amazon Web Services to simulate the viral spread of COVID-19 in airports.

Prior Research

Learn more about previous years' undergraduate research.

• 2023 Research
• 2022 Research

Email forwarding for @cs.stanford.edu is changing. Updates and details here .

Research & Impact

Making an Impact for a Better World

As computing continues to transform our world, the research we're pursuing at Stanford Computer Science seeks to ethically create, shape, and empower the new frontier. From the latest in robotics to foundation models to cryptocurrency, Stanford computer scientists are making an impact on the world beyond our academic walls. 

Faculty Spotlight:  Omer Reingold, the Rajeev Motwani Professor in Computer Science

"A computer scientist teaching a theater class is a bit unusual, I’ll grant you that. But is it so strange? For me, classifying different parts of campus to left-brain-versus-right-brain kind of thinking is just an unfortunate stereotype. I'd much rather go with ‘creativity is creativity is creativity.'" Read Omer Reingold's Story  

In the News: See Our Research in Action

Best Paper Award: "Breaking the Metric Voting Distortion Barrier"

Stanford professor, Moses Charikar, and his two co-authors, Kangning Wang (postdoc) and Prasanna Ramakrishnan (PhD student), win Best Paper Award at the ACM-SIAM Symposium on Discrete Algorithms (SODA24).

Click here to read more as Kangning and Prasanna discuss their passion for research, the challenges they faced, and the significance of this award.

A Robotic Diver Connects Human's Sight and Touch to the Deep Sea

The Future of AI Chat: Foundation Models and Responsible Innovation

Guest Percy Liang is an authority on AI who says that we are undergoing a paradigm shift in AI powered by foundation models, which are general-purpose models trained at immense scale, such as ChatGPT.

CS Faculty & Their Research

Explore our network of faculty members and the innovation conceived by their research. They are shaping a new era of solutions and the next generation of thought leaders and entrepreneurs.

Meet Our Faculty & Their Research

Stanford Computer Science faculty members work on the world's most pressing problems, in conjunction with other leaders across multiple fields. Fueled by academic and industry cross-collaborations, they form a network and culture of innovation.

The Emmy Award-winning video looks back at a remarkable six decades of AI work at Stanford University.

Stanford has been a leader in AI almost since the day the term was dreamed up by John McCarthy in the 1950s. McCarthy would join the Stanford faculty in 1962 and found the Stanford Artificial Intelligence Lab (SAIL), initiating a six-decades-plus legacy of innovation. Over the years, the field has grown to welcome a diversity of researchers and areas of exploration, including robotics, autonomous vehicles, medical diagnostics, natural language processing, and more. All the while, Stanford has been at the forefront in research and in educating the next generation of innovators in AI. Artificial intelligence would not be what it is today without Stanford.  

Research at the Affiliate Programs

Stanford Computer Science has a legacy of working with industry to advance real-world solutions. Membership in our affiliate programs provides companies with access to the research, faculty, and students to accelerate their innovations.

Join the Affiliates Programs

Interested in the benefits of memberships to our affiliate programs, sponsored research, executive education programs, or student recruitment? Get started by contacting:

Joseph Huang, PhD | Executive Director of Strategic Research Initiatives Stanford University, Computer Science [email protected]  

Connecting Students & Research: Jump In

At Stanford, students do amazing research. Their projects are widely recognized as some of the best in the world. Stanford's reputation as one of the top CS programs comes in large part from this. If you're a student with a passion for participating in meaningful research, our CURIS and LINXS programs are designed to get you started.

LINXS Program

The Stanford LINXS Program is an eight-week summer residential program that brings innovative undergraduates, who are currently attending Historically Black Colleges & Universities and Hispanic Serving Institutions, to Stanford for an immersive academic research and graduate school preparation experience. 

CURIS Program

CURIS is the undergraduate research program of Stanford's Computer Science Department. Each summer, 100+ undergraduates conduct and participate in computer science research advised and mentored by faculty and PhD students.  

Undergraduate Research Internship – Computing

Region: North America

Applications for Summer 2025 open 9/23

Position: Internship

Lab/Location: Redmond , New York City , New England

This program is for candidates who are passionate about technology and offer diverse perspectives. We don’t just value differences, we seek them out. We invite them in. We are a family of individuals at a truly global company, united by a single mission.

Microsoft’s mission: “Empower every person and every organization on the planet to achieve more”. To achieve our mission, we need to create a workforce that represents the rich diversity of our customers. The Microsoft Undergrad Internship Program is a tool to do just that. Our mission, “To unlock everyone’s engineering talent so they can achieve more”.

The Microsoft Undergrad Internship Program is focused on developing talent and experience for careers in computing research.

The program is a 12-week summer internship program specifically designed for advanced undergraduate students. It offers the opportunity to do state-of-the-art research in one of our US-based Microsoft Research Labs. You will work with our researchers and extended network of visiting faculty, postdoctoral researchers, data and applied scientists, engineers, designers, and doctoral students to make important contributions to new and ongoing research. One of the program’s goals is to encourage participants to pursue advanced degrees in computing fields. Your on-the-job learning will be augmented with mentoring, community building, and networking opportunities.

Qualifications

By the start of the internship program (~May 2025):

• Candidates must be rising junior or rising senior students enrolled in a bachelor’s degree program majoring in computer science, computer engineering, software engineering, information science or related major.
• Candidates must have at least two years of programming experience, completed courses in Calculus, Probability and Statistics and/or Machine Learning OR demonstrated training in at least one social science methodology.

We especially encourage applications from groups currently underrepresented in engineering and computer science, including those who self-identify as a woman, African American, Black, Hispanic, Latinx, American Indian, Alaska Native, Native Hawaiian, Pacific Islander, person with a disability, and/or LGBTQI+.

Responsibilities

Interns put inquiry and theory into practice. Alongside doctoral interns, and some of the world’s best researchers, interns learn, collaborate, and network for life. Interns not only advance their own careers, but they also contribute to exciting research and development. During the 12-week internship, students are paired with mentors and are expected to collaborate with other interns and researchers, present findings, and are invited to contribute to the vibrant life of the Microsoft Research community. Research internships are available in all areas of research and are offered in the summer.

How to Apply

Applications will be accepted from Monday, September 23rd until Monday, October 21st at 5:00 PM ET.

Reference letters will be accepted from Monday, September 23rd until Monday, October 28th at 5:00 PM ET.

You will be asked to provide the following application materials:

• Choose a track: Research, Research Software Engineering, or Research Program Management
• CV or Resume
• Up to 2 Reference Letters (these are due by Monday, October 28, 2024 at 5:00 PM ET.)
• Select up to three desired research areas. (See section “MSR Research Areas” below).
• Answer the prompt: What draws you to the desired research area(s) you selected and what impact would you like to have in that space?
• any relevant experience in this/these research area(s) or related technologies and
• how you expect to personally benefit from participating in this internship.
• Please describe ways you have contributed to increasing diversity and inclusion in your field and/or any unique challenges you may have faced and how you navigated those obstacles.

MSR Research Areas

• Artificial intelligence
• Audio and acoustics
• Computer vision
• Data platforms and analytics
• Ecology and environment
• Graphics and multimedia
• Hardware and devices
• Human-computer interaction
• Human language technologies
• Mathematics
• Medical, health and genomics
• Programming languages and software engineering
• Search and information retrieval
• Security, privacy, and cryptography
• Social sciences
• Systems and networking

Please see About Microsoft Research for more information.

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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.
• CSE students can do an independent study (EECS 399/499) with faculty outside of EECS if they also find an EECS (ECE or CSE) professor to be a co-director.

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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Academics   /   Undergraduate Undergraduate Research

In the Department of Computer Science at Northwestern University, undergraduate students have ample, rich, and varied opportunities for conducting practical research in labs alongside graduate students and faculty members.

This means that papers are being published with undergraduate students’ names on them, students are participating in research projects that result in conference papers being accepted, and students are finding out firsthand what life as a graduate student is like.

Research Track

In fall 2023, Northwestern Computer Science (CS) is launching a new research track designed to enable second-year students majoring in computer science to learn the fundamentals of academic research through a collaborative group project.

The research track program aims to provide undergraduate students with a structured and mentored research experience through the completion of two new courses — COMP_SCI 298: Introduction to Research Track and COMP_SCI 398: Research Track Practicum — and a project demonstration.

COMP_SCI 298: Introduction to Research Track

In fall 2023, the research track cohort will enroll in COMP_SCI 298, a new one-credit course designated as an unrestrictive elective. Led by a faculty adviser, the course will provide a foundational introduction to the research process. Students will be assigned to teams of four to five members based on research interests and experience from prior coursework. Teams will kick off their projects by conducting literature review, gathering data or resources, and gaining any project-specific skills. A CS faculty member will provide guidance and project mentorship.

COMP_SCI 398: Research Track Practicum

In winter 2024, the cohort will enroll in COMP_SCI 398, a one-credit course which can be counted either as a technical elective or a project course. Teams will continue progress on their research projects under the supervision of the faculty adviser and another CS faculty member. Teams will present their projects during a CS research showcase event in spring 2024.

The program application for fall 2023 enrollment is now closed.

Research Opportunities

The department maintains a list of research opportunities currently available to undergraduates. This list contains information about requirements for joining the lab and/or leading a project, as well as information on how to apply to join the lab. the list is updated periodically as research opportunities become available.

To learn more about the major research activities in the Department of Computer Science, you can also explore our various Research Areas:

• Artificial Intelligence and Machine Learning
• Computer Engineering ( in collaboration with the Department of Electrical and Computer Engineering )
• Human-Computer Interaction and Information Visualization

For more information about research opportunities in the department, visit our groups and labs page and contact a professor with whom you would like to work.

Learn more about undergraduate research opportunities at McCormick

Research Pathways

Before joining a research lab you will need to build the background that is necessary for participating in the research of the lab. The most effective way to do so is through courses, which requires planning ahead what courses to take and when. The earliest you complete the series of courses that will give you a solid background for an area of research, the fastest you will be able to get involved in a research project and benefit from the research activity in the department.

To help you prepare for research, the department maintains a collection of accelerated course pathways . Each pathway targets a specific area of research and consists of one or two courses per quarter. Starting on a pathway during your freshman year will get your ready for a research project in the pathway's area during your sophomore year.

Undergraduate Thesis

Undergraduate students have the option to complete a senior thesis as a part of their undergraduate degree. The senior thesis is documentation of an attempt to contribute new knowledge to the general understanding of some problem of computer science.

Research Experiences for Undergraduates (REU) supplemental funding is available specifically for undergraduate researchers, and primary investigators routinely ask for this funding on National Science Foundation proposals.

In addition, undergraduate students can receive reimbursement for travel expenses to conferences when their papers are accepted.

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Sara Sood Associate Chair for Undergraduate Education Phone: 847-491-5708 Email Sara

Apply to McCormick

“A student’s first research experience can be a daunting. With this new research track program, we hope to create a support structure and community to enable to positive first CS research experience.”

— Sara Owsley Sood, Chookaszian Family Teaching Professor and Associate Chair for Undergraduate Education

“Research is one of the greatest learning experiences that colleges have to offer. With this program, we want to support many more of our CS undergraduates getting involved in research early, and make it possible for many of our students to progress towards self-directing their own research projects, and eventually completing a senior thesis.”

— Haoqi Zhang, Associate Professor of Computer Science and Director of the Design, Technology, and Research (DTR) Program

“I’m very excited to help lead an undergraduate research track in computer science, and open a pathway for all students who might be interested in undergraduate research.”

— Joseph Hummel, Professor of Instruction

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.

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Computer science deals with the theory and practice of algorithms, from idealized mathematical procedures to the computer systems deployed by major tech companies to answer billions of user requests per day.

Primary subareas of this field include: theory, which uses rigorous math to test algorithms’ applicability to certain problems; systems, which develops the underlying hardware and software upon which applications can be implemented; and human-computer interaction, which studies how to make computer systems more effectively meet the needs of real people. The products of all three subareas are applied across science, engineering, medicine, and the social sciences. Computer science drives interdisciplinary collaboration both across MIT and beyond, helping users address the critical societal problems of our era, including opportunity access, climate change, disease, inequality and polarization.

Research areas

Our goal is to develop AI technologies that will change the landscape of healthcare. This includes early diagnostics, drug discovery, care personalization and management. Building on MIT’s pioneering history in artificial intelligence and life sciences, we are working on algorithms suitable for modeling biological and clinical data across a range of modalities including imaging, text and genomics.

Our research covers a wide range of topics of this fast-evolving field, advancing how machines learn, predict, and control, while also making them secure, robust and trustworthy. Research covers both the theory and applications of ML. This broad area studies ML theory (algorithms, optimization, …), statistical learning (inference, graphical models, causal analysis, …), deep learning, reinforcement learning, symbolic reasoning ML systems, as well as diverse hardware implementations of ML.

We develop the next generation of wired and wireless communications systems, from new physical principles (e.g., light, terahertz waves) to coding and information theory, and everything in between.

We bring some of the most powerful tools in computation to bear on design problems, including modeling, simulation, processing and fabrication.

We design the next generation of computer systems. Working at the intersection of hardware and software, our research studies how to best implement computation in the physical world. We design processors that are faster, more efficient, easier to program, and secure. Our research covers systems of all scales, from tiny Internet-of-Things devices with ultra-low-power consumption to high-performance servers and datacenters that power planet-scale online services. We design both general-purpose processors and accelerators that are specialized to particular application domains, like machine learning and storage. We also design Electronic Design Automation (EDA) tools to facilitate the development of such systems.

Educational technology combines both hardware and software to enact global change, making education accessible in unprecedented ways to new audiences. We develop the technology that makes better understanding possible.

The shared mission of Visual Computing is to connect images and computation, spanning topics such as image and video generation and analysis, photography, human perception, touch, applied geometry, and more.

The focus of our research in Human-Computer Interaction (HCI) is inventing new systems and technology that lie at the interface between people and computation, and understanding their design, implementation, and societal impact.

We develop new approaches to programming, whether that takes the form of programming languages, tools, or methodologies to improve many aspects of applications and systems infrastructure.

Our work focuses on developing the next substrate of computing, communication and sensing. We work all the way from new materials to superconducting devices to quantum computers to theory.

Our research focuses on robotic hardware and algorithms, from sensing to control to perception to manipulation.

Our research is focused on making future computer systems more secure. We bring together a broad spectrum of cross-cutting techniques for security, from theoretical cryptography and programming-language ideas, to low-level hardware and operating-systems security, to overall system designs and empirical bug-finding. We apply these techniques to a wide range of application domains, such as blockchains, cloud systems, Internet privacy, machine learning, and IoT devices, reflecting the growing importance of security in many contexts.

From distributed systems and databases to wireless, the research conducted by the systems and networking group aims to improve the performance, robustness, and ease of management of networks and computing systems.

Theory of Computation (TOC) studies the fundamental strengths and limits of computation, how these strengths and limits interact with computer science and mathematics, and how they manifest themselves in society, biology, and the physical world.

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Student Spotlight: Krithik Ramesh

Today’s Student Spotlight focuses on Krithik Ramesh, a member of the class of 2025 majoring in 6-4, Artificial Intelligence and Decision-Making.

3Qs: Dirk Englund on the quantum computing track within 6-5, “Electrical Engineering With Computing”.

In the new undergraduate engineering sequence in quantum engineering, students learn the foundations of the quantum computing “stack” before creating their own quantum engineered systems in the lab.

Dirk Englund, Associate Professor in EECS, has been part of a team of instructors developing the quantum course sequence.

3Qs: Jelena Notaros on the new Silicon Photonics class within 6-5, Electrical Engineering With Computing

One of the signal changes of 6.5, Electrical Engineering With Computing, is the organization of upper-level classes into tracks, including an undergraduate engineering sequence in Electromagnetics and Photonics. Jelena Notaros, Assistant Professor in EECS, developed a new class included in that track, “Silicon Photonics”.

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Computer Science Undergraduate Research Program (CSURP) to Convene in Summer 2022. Apply now.

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CSURP now in its second year is for undergraduates to get involved in research in computer science. Have you wondered what computer science research is like, or have you pondered doing a Ph.D. in CS after you graduate? CSURP may be for you! CSURP works like an alternative to a summer internship. You apply for the program giving some idea of what areas in CS you are interested in and prepared to work in. You don’t need to know exactly what you want to do or which lab you want to work in—but you do need to have a letter of recommendation from the instructor of a CS course you have taken. The selection committee considers your background and interests and, if you are accepted, matches you with a research lab and project. The CSURP program runs for up to 10 weeks over the summer. Here’s what you can expect from the program:

• Guidance from faculty and Ph.D. students to pursue the matched research project.
• Funding awarded up to $5,000.
• A series of enrichment talks on technical and career topics throughout the summer.
• Social events with other CSURP scholars and mentors.

To be eligible, you must be a Cornell undergraduate who is returning and registered for fall 2022. You also need to be interested in the idea of pursuing a Ph.D. in CS after you graduate—we have in mind CS majors, early-stage students who plan to major in CS, or majors in adjacent areas with a demonstrated interest in a CS topic.  

Application

Applications are open now.  Please fill out  this form . The application deadline has been extende to December 20, 2021. We encourage interested students to apply by December 15th, as we will start reviewing applications on that date. However, we will keep the application site open till December 20th, and will consider later arriving applications.

Please login with your Cornell NetID. The name and (NetID) email address associated with your Cornell Google account will be recorded when you upload files and submit this form.

The application form consists of:

• A one-page statement describing your research interests and any relevant experience.
• An unofficial transcript.
• The name and email address of the instructor of a Cornell CS course you have taken, whom the committee will ask to provide a reference. (You do not necessarily need to have contacted the instructor ahead of time. The instructor also does not need to know you personally; they just need to vouch for your performance in a Cornell CS course.)
• Optionally, the name and email address of  prospective research mentor you have already been in touch with about a potential summer project.

You will receive a decision from the selection committee by February 15, 2022. If accepted, you will then have two weeks to decide whether to take the offer.

For questions about the CSURP program, please contact  [email protected]

Special Positions for Some Groups

A portion of CSURP positions will be allocated for researchers from disadvantaged and underrepresented groups. There are two kinds of special positions: one for students from disadvantaged backgrounds (as defined below), and one for women (sponsored by the  Clare Boothe Luce Program for Women in STEM ).

The application has checkboxes that ask whether you want to be considered for these two categories. If you check these boxes, the form will ask you for a brief (300 words maximum) statement describing your fit for either or both categories. If you identify as a woman or fit any of the below categories, we encourage you to check these boxes, but doing so is not required.

The positions for researchers from disadvantaged backgrounds will be open to people meeting one of these criteria:

• Being a member of an ethnic or racial group historically underrepresented in higher education (African American, American Indian/Alaskan Native, Native Hawaiian or other Native Pacific Islander, Mexican American, Puerto Rican, or other Hispanic American; permanent residents whose ethnicity corresponds to these groups also meet this criterion)
• Being a participant in one of the following programs: McNair Scholar, Mellon Mays Scholar, Posse Program, LSAMP Scholars, Ryan Scholars, NACME Scholars, Pre-Professional Programs (P3), HEOP/EOP, Gates Millennium Scholars
• Having experiences overcoming any significant challenges in your path toward college (examples include, but are not limited to, being a first generation college student, being a Veteran, being a single parent, holding DACA status, and/or managing a disability)

• Department of Computer Science

Undergraduate

Undergraduate research.

What is Undergraduate Research?

Involving yourself in undergraduate research adds a dimension to your university experience that will better prepare you for your future. As a researcher, whether in the lab or field, you will find discovery, challenges, and learn problem-solving skills beyond those you develop in the classroom or teaching laboratory. In the field of computing, some projects are theoretical, which involve developing and analyzing new algorithms and techniques. Other projects are more applied, which involve experiments, design, implementation and testing.

Undergraduate Research Events - Fall 2024

• Wed., September 4, 2024 , 11:30am-1PM, PGH 563: Undergraduate Research Info Session.
• Thurs., September 26, 2024 , 5:30pm-7pm, PGH 563: Research Opportunities in the CS Department.

Undergraduate Research Opportunities - Fall 2024

• TBA Research Opportunities

Why Undergraduate Research?

• Intellectual challenge. Research will challenge you in new ways and can help you decide your path in the field of computing.
• Apply classroom learning to hands-on project(s). Research allows you to pursue existing new interests and apply what you have learned. You have the potential to discover something new and advance existing knowledge.
• Impact society and the future. Many projects address the needs and problems of society.
• Prepares you for graduate school if you intend to pursue a Ph.D. or a M.S.
• Networking and Collaboration. Research is usually a collaborative effort. The strong connections you make may become beneficial when it comes to seeking recommendation letters for graduate school or employment references.

Ways to Get Involved

• Paid (e.g. employment, participant stipend)
• COSC 4X98 Independent Study
• COSC 3396/4396 Senior Research Project
• COSC 3399/4399 Senior Honors Thesis
• Provost's Undergraduate Research Scholarship (PURS)
• Summer Undergraduate Research Fellowship (SURF)
• [Summer] NSF Research Experience for Undergraduates (REU)
• NSF REU Sites
• IBP – Pathways to Science
• CRA Conquer – Computing Science Undergraduate Research

How to Get Started

Unsure about undergraduate research? Or maybe you have an interest, but don’t know where to start? Here are a few ways to learn more about research itself as well as undergraduate research opportunities.

• Websites : to get started in research, you must first do some research! Visit the faculty and research sections of the department website to get a glimpse of faculty research.
• Faculty : professors are the focal point of university research. Maybe you’ve taken a class and a particular topic discussed piqued your interest. Or maybe you’ve gained interest after viewing research projects on their website. Connect with them after class or schedule an appointment with them to learn more about their research and inquire about undergraduate research opportunities.
• Teaching Assistants : many of you are in a class with a Teaching Assistant who is likely a MS or PhD student in the department. Don’t be shy! Ask them about their research and what they do.
• Peers : the person sitting next to you in class or a friend from a student organization has probably participated in undergraduate research. Ask them about their experience and how they got started.
• Department Seminars and Presentations : our department regularly hosts seminars where researchers from across the nation discuss a research topic. Attend a few seminars to expose yourself to the various research projects in the discipline. Each year, we also host Faculty Mini-Talks where all faculty give a quick talk about their research.
• Workshops and Conferences : attend events on-campus or off-campus with “poster session” and “research day” in the event title. These are avenues to hear from undergraduate students, graduate students and faculty about their research projects and findings.

Search for Opportunities

Below are websites that list undergraduate research opportunities internal and external to UH. For the internal UH websites, don't limit yourself to what you see listed as not all faculty post their available undergraduate research opportunities here.

• NSM/COSC Research Portal
• UH Office of Undergraduate Research postings

Still have questions? Need help? Want to learn more?

• Visit the UH Office of Undergraduate Research
• Contact Dr. Omprakash  Gnawali at  [email protected]
• Contact Dr. Feng Yan at  [email protected]  
• Contact Dr. Dan Biediger at  [email protected]  
• Contact Matthew Pariyothorn at [email protected]

Undergraduate Research Opportunities

Office of undergraduate education.

PairMe, hosted by UROP, is a dynamic platform dedicated to connecting students with research opportunities and mentors in their field of interest. It is a centralized hub where undergraduates can explore various research projects thereby networking with faculty members or graduate students, and gain valuable hands-on experience in academic research.

Our site streamlines the process of finding research opportunities by providing a user-friendly interface where students can browse, filter, and apply to projects based on their academic interests and expertise. With this site, undergraduates can take their academic journey to the next level by engaging in meaningful research experiences that contribute to their personal and professional growth. 

How to Get Paid to Do Cool Stuff

Thursday, september 26, 11 am - 12 pm in clough (culc) 211.

Join us for a workshop (with pizza!) on finding internships, co-ops, research positions, and federal work study opportunities. Learn how to navigate resources to help you secure paid on-campus experiences. Registration is encouraged, but not required.

Spring 2025 PURA Salary Award

Application deadline: monday, september 16 by 5 pm et.

President's Undergraduate Research Awards (PURA) Salary Awards offer $1,500 to undergraduate students who are conducting research under the supervision and mentorship of a Georgia Tech or Georgia Tech Research Institute faculty member. The proposal consists of a 2 parts: a 2-page project description and a 1-page statement of mentor/mentee expectations.

Writing a PURA Salary Award proposal will help you better understand and explain your research. And if your proposal is funded, you can list this on your resume or CV.

As an undergraduate at one of the foremost institutions in the nation, there are many reasons to delve into research. Undergraduate research sparks critical thinking and creativity. By engaging in research and scholarship, students actively contribute to discovery and deepen their understanding within and beyond the classroom. Research is the innate pursuit of progress and service and the catalyst of innovation. We work to enhance it.

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Student Research Mentors

Undergraduate student research and creative projects may be produced by students at all levels in classes, labs, recitals, as part of a distinction or thesis program or independently. Participating in undergraduate research helps you apply what you are learning in class, find a mentor, improve your critical thinking and problem-solving skills, and pursue a topic that fascinates you.

SOURCE Fall Deadlines

• Faculty Research Assistant Grant Faculty-led research projects that allow students to develop key research skills within their discipline Application due Sept. 26, 2024 Apply Now
• Bridge Award (Funding for Spring 2025) Short-term, renewable support for students at all levels to pursue mentored research experiences (up to $2,000) Application due Oct. 31, 2024 Apply Now
• Explore Program (Will take place Spring 2025) Hands-on, short term research experiences for first- and second-year students Application due Dec. 5, 2024 (online application available late-Sept.)

Undergraduate Research FAQs

What is research and creative engagement.

Undergraduate research work takes many forms but all follow a similar structure:

• Beginning with a sense of curiosity in the exploration of a topic of interest
• An understanding of the current landscape of a scholarly, professional or creative field of study.
• Designing of a study or project using the methods and tools of a discpline to present evidence that responds to a question or theme.
• All undergraduate research students are supported by a faculty mentor in their field.
• Student research and creative projects may be produced by students at all levels in classes, labs, recitals, as part of a distinction or thesis program, or independently. 

Why should I participate in undergraduate research?

Participating in undergraduate research allows you to:

• Apply your knowledge to real-world problems and issues
• Develop a strong faculty mentor relationship
• Improve your problem-solving and creative thinking skills
• Explore potential career areas
• Develop skills you can use on the job market or in grad school
• Explore a topic you find fascinating and participate in the creation of new knowledge

How does undergraduate research work at Syracuse University?

• Students are guided by a faculty mentor (typically a tenured or tenure-track professor) or research staff member.
• In humanities, communications/journalism, business/management, social sciences, arts: students work as part of a research team or one-on-one with a professor to either assist with an ongoing project or design an independent project.
• In STEM fields: students work as part of a lab team, led by a professor (or Primary Investigator, “PI”): students assist with ongoing projects and may take leadership on part of the lab’s work.
• Students may also work off-campus, with a community organization, another university, or do research as part of the study abroad experience.

What are some examples of undergraduate research?

• Miguel Guzman, ’24   – lab research on bioactive protein-cholesterol-based nanoparticles
• Sophie Clinton, ’24   – conducted social science research while abroad in Santiago, Chile
• Ngai Lan Tam ’23   – created an exhibition with structural design, film and performance
• Fátima Bings Martínez ’24   – worked as a research assistant for a literary journal
• Ruchatneet Printup ’23   – directed a film set in his Native community

How do I find a research topic or area of interest?

Jot down a few notes in response to these prompts:

• Readings or lectures from a class that sparked your interest and made you want to learn more or share with a friend
• Problems or issues that you’d like to contribute to solving or improving
• Gaps in your education
• Skills that you’re interested in developing
• Passions, hobbies, and personal interests
• Goals or outcomes that could build your portfolio and be shared with a future employer or graduate school

Connect with others

• Talk to your professors during their office hours about how they first discovered their research interests
• Get inspired at a student research presentation event on campus: the SOURCE Fall Expo, Spring Showcase, or Summer Symposium, or a school/college event
• Go to lectures and talks on campus and ask questions
• Chat with fellow students doing research (you could start with SOURCE   Student Research Mentors ) about how they found their focus
• If you have a specific post-graduate goal (career, graduate study, etc.), speak with career and academic advisors about the skills you should be building

Office of Undergraduate Research

PCCM's REU program provides opportunities for undergraduates to carry out research at the forefront of materials science and engineering.For 9 weeks the REU students work on projects under the guidance of faculty from the departments of Physics, Chemistry, Molecular Biology, Chemical and Biological Engineering, Electrical and Computer Engineering, Mechanical and Aerospace Engineering, and Civil and Environmental Engineering.

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Msc computer science (software engineering) (one year programme).

• Study at Maynooth /

Qualification : MASTER OF SCIENCE DEGREE

Award Type and NFQ level : TAUGHT MASTERS (9)

CAO/PAC code : MHG50

CAO Points :

Closing Date : 31 July 2025

View FETAC details

• Entry Requirements
• Department People
• Course Structure
• Career Options
• How to Apply

The emphasis of the MSc in Computer Science (Software Engineering) is on the principles of good scientific software engineering practice, grounded in a hands-on understanding of the applicability of these practices, so that successful participants will be able to design and lead major software engineering projects.

Topics include requirements engineering, system design, testing strategies, software development and augmented software development, with an overall emphasis on the development of reliable software systems. The course is designed to give participants a strong foundation in advanced computer science topics ensuring longevity of their skills and their ability to adapt in a rapidly evolving field.

The following criteria must be fulfilled by students applying for the MSc in Computer Science (Software Engineering):

- An honours degree in Computer Science or a closely related discipline, containing significant Computer Science content. An average grade of at least 60% (equivalent to a II.1 from an Irish NFQ Level 8 degree course) or above. Applicants with less Computer Science experience should consider MHG68 instead. Applications from students graduating from MHG70:Higher Diploma in Science (Software Development) with a first class honours are encouraged. 

Minimum English language requirements:

Applicants for whom English is not their first language are required to demonstrate their proficiency in English in order to benefit fully from their course of study. For information about English language tests accepted and required scores, please see here . The requirements specified are applicable for both EU and International applicants..

Maynooth University's TOEFL code is 8850

Information regarding visas etc. may be obtained from the International Office at Maynooth University . 

Dr Aidan Mooney

Head of Department

Department of Computer Science

Department of ALL Institute

Read Full Biography

Dr Ralf Bierig

Department of Hamilton Institute

Dr Stephen Brown

Dr kevin casey.

Dr Natalie Culligan

Department of Maynooth International Engineering College

Prof Rozenn Dahyot

Dr Thomas Dowling

Joseph Duffin

Dr Behnam Faghih

Dr Edgar Galvan

Dr Bryan Hennelly

Department of Electronic Engineering

Department of Human Health Institute

Dr John Keating

Department of Faculty of Science & Engineering

Dermot Kelly

Dr Liadh Kelly

Taina lehtimaki.

Thomas Lysaght

Dr Phil Maguire

Dr Charles Markham

Prof Tim McCarthy

Department of National Centre for Geocomputation (NCG)

Department of Social Sciences Institute (MUSSI)

Department of ICARUS

Prof John McDonald

Prof Rosemary Monahan

Dr peter mooney.

Prof Thomas Naughton

Dr Diarmuid O'Donoghue

Prof Barak Pearlmutter

Prof Ronan Reilly

Prof Damien Woods

Each module is delivered as a series of intensive lectures. Each module also has associated practical work.

Participants submit a dissertation based on a Software Engineering topic. 

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