Discover research with BURE.

The Bowers Undergraduate Research Experience (BURE) offers Cornell Bowers undergraduates a unique opportunity to explore the world of research through a hands-on, 10-week summer program.

Open to all Cornell Bowers undergraduates, BURE pairs students with a faculty mentor to work on a real research project. BURE students gain valuable skills, build connections, and learn how to conduct an independent research project.

View 2026 Summer Program

Participate in cutting-edge research.

A student smiles and looks at the camera while sitting at a desk with two computer monitors

“One thing I love about doing research at Cornell is that they really put the students at the forefront of the research. You really get to work on the things you find most interesting.”

Benny Rubin ’25

Why join BURE?

  • Work one-on-one with a faculty mentor
  • Develop research and technical skills
  • Explore whether a Ph.D. or research career is right for you
  • Join a community of curious, motivated peers
  • Attend a series of enrichment talks on technical and career topics throughout the summer
  • Present your project at the end of summer Research Symposium

%20bure [at] cornell.edu (Contact the BURE team)

  • Duration: 10 weeks during the summer
  • Format: Full-time, in-person research experience
  • Goal: Help students explore independent research and discover future graduate study paths and research careers
  • Payment: Hourly wage or research stipend
  • Open to all Cornell Bowers undergraduates
  • Applicants must have completed at least one core course in CS, IS, or SDS
  • BURE is a full-time commitment, with students required to attend all program events in-person

Applications for summer 2026 open January 12, 2026 and close at 11:59pm on February 15, 2026. A link to the application will be available on this website during this time. No late applications will be accepted.

Application Components include:

  • One-page statement describing your research interests and any relevant experience
  • Official transcript (be sure to request your transcript in a timely manner)
  • Ranking of 4 prospective faculty mentors
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Cornell Bowers

Research takes time, and undergraduates don’t have much time to spare when the semester starts. But come summer, thanks to BURE, Cornell Bowers undergraduates can give research the time it requires.

BURE Basics

Learn more about eligibility, the application process, expectations, and more.

To participate in BURE, you must be a Cornell Bowers undergraduate major who will be returning to campus as a student in the fall semester following the program. Applicants must have completed at least one core course in CS, IS, or SDS.

If you have not yet officially affiliated with a Bowers CIS major but intend to do so as soon as you are eligible, you are still welcome to apply.

BURE projects may be based at either the Ithaca campus or Cornell Tech, depending on your faculty mentor’s location. However, all official BURE programming and events will be held in person at the Ithaca campus.

Please note that students are required to attend all BURE programming, and additional funding is not available for projects based at Cornell Tech.

Students are hired as either hourly employees or as researchers on REU supplements. Hourly employees are expected to work 40 hours/week, are not allowed to work more than 40 hours/week, and are not allowed to participate in outside employment. Researchers on REU supplements will receive a stipend.

BURE does not cover housing and meals.

Program dates for Summer 2026 are June 1, 2026 through August 7, 2026. Please note, BURE students are required to participate in the full duration of the program and all events.

Participating Cornell Bowers faculty mentors are listed below, and are updated for each summer of the program.

Applicants are strongly encouraged to speak with a prospective faculty mentor before submitting their application to discuss potential research interests and fit.

Participating Faculty Mentors

Below we list faculty mentors for Summer 2025 along with any required or suggested skills for working in their lab.

  • Abe Davis
     
  • Aditya Vashistha
     
  • Adrian Sampson
    ​Suggested: Taken CS3110 or CS3410
     
  • Amy Kuceyeski
    Required: Python coding skills and knowledge of basic statistics
    Suggested: Intro AI skills
     
  • ​Andrew Myers
    ​Suggested: Taken CS3110
     
  • Bharath Hariharan
     
  • ​Chris Csikszentmihalyi
    Suggested: At least one of the following skills sets - (1) acoustic signal processing and ML; (2) web platform development in Python; (3) robotics/ROS development hardware and software. Students with an interest in social and environmental justice, tech and indigenous communities, or tech in the majority world may also apply
     
  • ​David Bindel
     
  • David Shmoys
    ​Suggested: Knowledge of Python and taken CS4820​
     
  • Eva Tardos
     
  • Hadar Averbuch-Elor (Cornell Tech)
     
  • Hakim Weatherspoon
     
  • Jaehee Kim
     
  • Jennifer Sun
     
  • Justin Hsu
    ​Required: Taken CS3110 (received at least A-)
    Suggested: Taken one or more of the following: CS4110, CS6110, CS6117, CS6861​
     
  • Kevin Ellis
  • ​Required: Knowledge of Python and experience with AI, ML, and/or web development
     
  • ​Kristina Monakhova
  • Suggested: Interest in imaging systems, inverse problems, signal processing
     
  • ​Kuan Fang
    Suggested: Background in AI, robotics, ML, computer vision; taken CS4780 or CS4756​
     
  • Michael Kim
     
  • Noah Snavely (Cornell Tech)
    Required: Taken an undergraduate level computer vision course or have experience with web development
     
  • Noah Stephens-Davidowitz
     
  • ​Saikat Dutta
    Required: Strong programming experience (Python/JAVA)
    Suggested: Experience with machine learning, compilers, or software engineering
     
  • Sainyam Galhotra
     
  • Sarah Dean
    Suggested: Taken ML (3780) and/or RL (4789); interest in control, dynamics, optimization
     
  • ​Steve Marschner
    ​Required: Strong in math and skills in rendering or simulation
     
  • ​Tanya Goyal
    ​Required: Strong programming skills (Python)
    Suggested: Proficient with Pytorch; familiar with basic probability and linear algebra
     
  • Tapo Bhattacharjee
    Required: Knowledge of Python (ROS and C++ are bonus)
    Suggested: Taken CS4750
     
  • Thijs Roumen (Cornell Tech)
    Suggested: Experience with CAD tools​
     
  • Thorsten Joachims
    ​Required: Taken CS3780 (or equivalent)​
     
  • Walker White
     
  • Wei-Chiu Ma
    Required: Taken an undergraduate level computer vision or ML course
    Suggested: Taken a graduate-level computer vision course
     
  • ​Wen Sun
  • Angelique Taylor (Cornell Tech)
     
  • Cheng Zhang
     
  • Cristian Danescu-Niculescu-Mizil
     
  • Cristobal Cheyre
     
  • David Mimno
     
  • Francois Guimbretiere
     
  • Gili Vidan
    Required: Experience with Python or R for data analysis (such as INFO 2950/2951-concurrent enrollment in Spring 2025 ok); demonstrated desire to study journalism, trust in online information ecosystems, transparency in AI, and online communitiesSuggested: Experience with one of the following aspects of qualitative research: interviewing participants, using a codebook to analyze interview transcripts, synthesizing scholarly literature
     
  • Qian Yang
    ​Required: At least one of the following skill sets - (1) experience in NLP, especially with LLMs such as GPT and Gemini Flash; (2) building web apps with Python; (3) experience in JavaScript and/or data visualization
    Suggested: Multiple skill sets as described above​