Bowers Undergraduate Research Experience (BURE)
Overview
Have you ever wondered what research is like, or considered pursuing a PhD after you graduate? If so, then BURE may be for you!
BURE helps Cornell Bowers CIS undergraduates gain new skills and explore research with a hands-on summer experience. Open to all Cornell Bowers CIS undergraduates, BURE pairs undergraduates with a faculty mentor for a 10-week summer research program. Applicants rank prospective faculty mentors from a list of participating mentors, and are then matched with a mentor based on both student and faculty preference. Applicants must have taken a core CS, IS, or SDS course, and are highly encouraged to speak with a prospective faculty mentor prior to applying. BURE is a full time summer job, with students required to attend all BURE programming throughout the summer.
Here’s what you can expect from the program:
-
Hourly wage (40 hours/week for 10 weeks)
-
Series of enrichment talks on technical and career topics throughout the summer
-
End of summer Research Symposium on August 9, 2024
- Social events with other BURE scholars and mentors
Summer 2024 program dates are June 3, 2024 through August 9, 2024.
Participating Faculty Mentors
Below we list faculty mentors for summer 2024 along with any required or suggested skills for working in their lab:
- Brennan Antone
- Suggested: Interest in organizational social network analysis or in studying how people learn about Generative AI tools from a social science perspective
- Yoav Artzi (Cornell Tech)
- Suggested: Experience with machine learning, deep learning, web development
- Kavita Bala
- Required: Taken either an undergraduate level Computer Vision or Machine Learning course
- Tapo Bhattacharjee
- Required: Knowledge of Python (ROS and C++ are bonus)
- Suggested: Taken CS4750
- Claire Cardie
- Required: Interest in NLP
- Sanjiban Choudhury
- Required: Programming expertise (Python and PyTorch)
- Suggested: Taken CS4780
- Anil Damle
- Cristian Danescu-Niculescu-Mizil
- Required: Strong Python skills
- Suggested: Interest in natural language processing and coversation analysis
- Abe Davis
- Sarah Dean
- Suggested: Taken ML4780
- Saikat Dutta (remote mentor)
- Required: Strong programming experience (Python/JAVA)
- Suggested: Experience with machine learning, compilers, or software engineering
- Raaz Dwivedi (Cornell Tech)
- Required: Proficiency in at least one of the following three topics, and familiarity with the others: machine learning, statistics, Python
- Kevin Ellis
- Required: Knowledge of python
- Suggested: Experience with deep learning and functional programming languages
- Sainyam Galhotra
- Kyra Gan
- Required: Interest in the intersection of causality, statistics, and optimization
- Suggested: Strong math/stats background, extensive coding experience
- Giulia Guidi
- Required: Taken CS5220 and familiar with C++
- Suggested: Taken CS3410
- Bharath Hariharan
- Justin Hsu
- Required: Taken CS3110 (received at least A-)
- Suggested: Taken one or more of the following: CS4110, CS6110, CS6117, CS6861
- Thorsten Joachims
- Required: Taken an introductory machine learning course
- Jaehee Kim
- Michael Kim
- Bobby Kleinberg
- Required: Taken CS4820, interest and aptitude for reading and writing mathematical proofs
- Suggested: Received A or higher in CS4820, completion of at least one semester of probability (e.g., BTRY/STSCI3080, CS4850, ECE3100, ECON3130, ENGRD2700, MATH4710)
- Amy Kuceyeski
- Owolabi Legunsen
- David Mimno
- Rajalakshmi Nandakumar (Cornell Tech)
- Required: Experience with signal processing, working with sensors, data science skills
- Thijs Roumen (Cornell Tech)
- Suggested: Experience with CAD tools
- Adrian Sampson
- Suggested: Taken CS3110 or CS3410
- David Shmoys
- Noah Snavely (Cornell Tech)
- Required: Taken an undergraduate level computer vision course or have experience with web development
- Noah Stephens-Davidowitz
- Eva Tardos
- Angelique Taylor (Cornell Tech)
- Required: Proficient in python, experience with machine learning and particularly with reinforcement learning
- Suggested: Familiar with Pytorch and an understanding of Gym RL environments
- Aditya Vashistha
- Hakim Weatherspoon
- Walker White
- Qian Yang
- Required: Knowledge of at least one of the following - python and web app development, javascript and front-end development, qualitative user research (e.g., conducting user interviews, analyzing user interview data), python for data analysis, NLP
- Suggested: Knowledge in multiple areas listed above
- Yian Yin
- Required: Introductory-level background in probability and statistics, programming experience with Python (preferred) or R
- Suggested: Experience with network analysis or large-scale data analysis
- Cheng Zhang
- Suggested: Experience with machine learning, signal processing, hardware prototyping
Application Information
Applications are now OPEN! Apply here: https://forms.gle/pGYHrWJjPH5BMpCR7
Applications are due by 11:59 pm on Sunday, February 11, 2024. Please note, no late applications will be accepted.
The application form consists of:
- A one-page statement describing your research interests and any relevant experience
- An unofficial transcript
- Ranking of 5 prospective faculty mentors
- Optional statement (see immediately below for more information)
Additional Opportunities for BURE Students
Some BURE positions will be allocated for researchers from disadvantaged backgrounds (as defined below) and underrepresented groups (sponsored by the Clare Boothe Luce Program for Women in STEM). If you would like to be considered for these opportunities, please follow the application prompt and complete a brief (300 words maximum) statement. Completing this statement 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: first generation college student, Veteran, single parent, holding DACA status, and/or managing a disability)
FAQ
-
How do I apply?
-
The application link will be provided on this page during the application window.
-
-
Who can participate in BURE?
-
You must be a Cornell Bowers CIS undergraduate who is returning to Cornell as a student for the fall semester to participate in BURE. Applicants also must have taken a core CS, IS, or SDS course.
-
-
Who can serve as a faculty mentor?
-
All participating Cornell Bowers CIS faculty mentors are listed on this website. Students will request to work with faculty from this list as part of their application.
-
-
Is BURE open to both Ithaca and Cornell Tech campuses?
-
Yes, BURE may occur at either Ithaca or Cornell Tech campuses, depending on your faculty mentor. However, program activities will take place at the Ithaca campus, and extra funding is not available for Cornell Tech projects. Please note, BURE students are required to attend all BURE programming.
-
-
How much funding does BURE provide?
-
Students are hired as hourly employees, and are expected to work 40 hours/week. Students are not allowed to work more than 40 hours/week or participate in outside employment.
-
-
Is there funding available to cover housing and meals?
-
Unfortunately, BURE does not cover housing and meals.
-
-
What are the dates of the program?
-
Program dates for Summer 2024 are June 3, 2024 through August 9, 2024. Please note, BURE students are required to attend all BURE programming.
-
For questions about the BURE program, please contact bure@cornell.edu