Research Experience for Undergraduates
Stanford’s School of Engineering is home to amazing researchers and research.
And undergraduates can be a part of it through the Research Experience for Undergraduates (REU) program, which provides opportunities to work on cutting-edge research guided by Stanford faculty and PhD students. Faculty and student mentors are excited to work with bright, enthusiastic students.
There are two REU programs in MS&E, an academic year program where students receive independent study course credit, and a summer program where students are paid to work full time. For general advice on how to get involved in research at Stanford, refer to the Stanford Undergraduate Advising and Research (UAR) Guide. To learn more about MS&E's REU programs and apply, see below.
Academic Year REU
MS&E's Academic Year REU program offers research opportunities to students from any STEM-inclined major at Stanford during the academic year (fall, winter, and spring quarters).
Academic Year REU participants are paired with an MS&E mentor, who can be a faculty member or PhD student in the department, to learn and engage in their mentor's research group and receive independent study course credit. The cohort meets at the end of each quarter to present their research projects.
Learn more and apply to the Academic Year REU program
Summer REU
For the Summer REU program, current Stanford undergraduate students are matched with participating MS&E faculty members and PhD students. Undergraduates participate in 10 weeks of research with their faculty and student mentors, culminating in a presentation to all participants and mentors toward the end of summer quarter. Weekly group meetings are held with all participants in the cohort.
Applications for the summer REU program are available each year between January 15 and February 15, with the goal of matching students to faculty mentors by mid-March. Questions about the program? Contact Patty Padilla
Apply to the Summer REU program
See some of the work that Summer REU participants have done, below.
Summer REU projects 2025
Six undergraduate students in MS&E's Summer Research Experience for Undergraduates program (REU) presented their findings at the end of summer quarter.
This year was the first time that none of the rising sophomores, juniors, and seniors in the program were MS&E majors, although some of them intend to pursue master's or PhD degrees in the department. The variety of majors represented highlights a core strength of the department, which is its interdisciplinary nature and broad appeal, and the students' research focused on emerging AI capabilities and data analysis.
Students were paid to work full-time with MS&E faculty and PhD students who guided their research over the course of the 10-week summer quarter, funded by the Vice Provost for Undergraduate Education. They met weekly with MS&E faculty and PhD students, who shared their research experiences. The program was organized by MS&E's Director of Undergraduate Studies, Associate Professor Ross Shachter, and Student Services Officer Patty Padilla. The student presentations, which spanned a variety of fields and techniques, are highlighted below.
Big data on the dairy farm
Symbolic Systems major Martin Blue worked with Professor Pamela Hinds and MS&E postdoctoral scholar Angelos Kostis to discover how dairy farms are utilizing machine learning to improve their operations.
Their work followed dairy farmers in California’s central valley as they utilized robotic milking machines, not only to increase efficiency, but also to collect data on their cows, such as which ones might have sustained an injury or might have an infection.
Blue described his experience of learning how to perform ethnographic research in a challenging field environment. Two learning experiences he mentioned were learning what to take field notes on in the midst of a chaotic and fast-paced environment, and how to then code those notes for analysis—particularly, how to code serendipity, which has a particular definition in an academic context.
Learn more about big data on the dairy farm
Prior authorizations in health care
Myra Kirmani, a rising junior in Mathematics, studied the origin and impact of prior authorizations in the US health care system, as well as how to improve the process, working with Associate Professor Ross Shachter.
She described how, although prior authorizations are intended to save money by eliminating waste, they are estimated to cost $93 billion per year, as they can actually increase costs in the long run because a number of legitimate treatments get delayed or denied in error, patients get sicker, and treatments get more expensive.
Through a combination of interviews with physicians, analysis of the literature, and data analysis of datasets involving hepatitis C, Kirmani arrived at several policy recommendations that could help improve efficiency and quality of care: Eliminate prior authorizations for certain urgent conditions, and utilize AI-based tools for predictive approval to fast track claims that are most likely to be approved.
Exploring lay theories of AI augmentation across occupational groups
Rising sophomore Diya Bansal worked with Assistant Professor Arvind Karunakaran and PhD student Devesh Narayanan to discover how workers in different roles and industries perceive AI in the workplace.
The team noticed that scholarly discourse about AI at work tends to focus on predictions from researchers and consultants, but doesn't often feature the perspectives of workers themselves. So, they set out to "document the lived experiences of workers," said Bansal, to fill in the gaps in the conversation.
To do so, they interviewed workers in a range of roles with different levels of exposure to AI, from plumbers to administrators to creative workers, as well as a range of seniority levels, from individual contributors to C-suite executives to solo entrepreneurs.
Two of their findings: Overall, workers expect their colleagues to be more affected by AI than themselves, and the higher status someone has, the less fear they tend to have about being replaced by AI.
Big data pipelines for LLM pretraining
Computer Science major Stephen Makuol worked with Professor Kay Giesecke to address a problem in the financial technology field: Training large language models (LLMs) with economic data is cumbersome and slow.
Their research goal was to build an efficient infrastructure to train "super-intelligent economic robots," according to Makuol. To address several known challenges in the field, they improved access to private data, wrote an algorithm that facilitates faster downloads of massive datasets, allowed for choice in the mix of training data to feed into a model, and provided an evaluation framework to assess the quality and efficiency of a trained model.
Impact of IRA on climate venture activity
Rising sophomore Amir Zeinali worked with Associate Professor Chuck Eesley and PhD student Yikai Cao to answer the question: How did the Inflation Reduction Act (IRA) impact the clean energy industry?
The team utilized data from Pitchbook to analyze over 16,000 companies and over 37,000 tech deals in the sector. They found the number of deals increased after the IRA was passed, and the share of clean energy deals relative to total deals also increased.
Using the "difference in differences" method, the team concluded that, post-IRA, the likelihood of a clean energy venture receiving venture capital funding increased. They also are exploring whether the companies receiving funding tended to be early-stage ventures or more established firms.
Four entrepreneurship projects
Rising sophomore Jolie Li took on four different projects for the summer, all with guidance from Professor Riitta Katila in addition to PhD students and MS&E alums.
One project, with PhD student Phil Reinecke, studied how decision-making and delegation occurs in decentralized autonomous organizations—that is, organizations with no human managers, blockchain-based governance, and in which decision-making is open and public.
Another project aimed to define and study entrepreneurial resilience. With PhD student Chris Flowers and visiting student researcher Fabian Tingelhoff, Li developed an interview protocol, conducted interviews, and coded transcripts of those interviews for data analysis.
Other projects studied startup formation outcomes after entrepreneurs receive negative feedback—with alum Carrington Motley (PhD '22) and Tingelhoff—and the quality of innovations offered by startups versus incumbent organizations based on patent filings—with alum Jiang Bian (PhD '21).