Stanford HAI 迎来 15 位新数据科学学者

Stanford HAI Welcomes 15 New Data Science Scholars

Date

October 01, 2026

Topics

The two-year fellowship supports PhD researchers across law, medicine, physics, statistics, education, and engineering.

Stanford HAI has named 15 new Data Science Scholars, the latest cohort in a two-year fellowship for PhD students whose work advances the foundations, applications, and human dimensions of data science and artificial intelligence.

The Stanford HAI Data Science Scholars Program focuses on existing Stanford PhD students across all seven schools whose work advances the foundations, applications, and human dimensions of data science and AI. This includes foundational research on methods, models, and theory; applied research across domains such as scientific discovery, health and biomedicine, mental health, language and culture, education, and the arts; and research on the ethical, societal, and policy implications of these technologies.

Scholars are selected for the significance of their research questions, the rigor of their methods, and their commitment to interdisciplinary collaboration. The program is designed to create a critical mass of early-career researchers whose collective work reflects the full breadth of HAI's mission, spanning scholarly discovery, educational transformation, and societal impact.

This year's cohort spans computer science, statistics, law, neuroscience, sociology, education, physics, materials science, and business, with research ranging from AI governance and cyber conflict to protein design, radiology models, and the structure of the universe.

“AI and data science are reshaping nearly every field of human inquiry, and the scholars we've selected this year reflect exactly the kind of breadth and depth that mission demands,” says Stanford HAI Denning Director James Landay. “From AI governance to protein design, from radiology to the structure of the universe, this cohort is asking questions that matter—and bringing the rigor and creativity to answer them. We're proud to support their work at this critical moment.”

The 2026 Data Science Scholars

Ruishi Chen

Ruishi is a PhD student in the Education Data Science program. Her research examines how evaluative processes shape knowledge diffusion and how emerging technologies transform these processes. In particular, she studies how institutions assess ideas and innovations using computational methods such as natural language processing, social network analysis, causal inference, and LLM-based simulation. Her current work focuses on peer review and patent examination as key sites of knowledge evaluation, as well as the adoption of AI-enabled tools in educational settings.

Sarah-Eve Dill

Eve is a PhD student and NSF Graduate Research Fellow in Sociology. Her research uses data science to study how socioeconomic inequality is experienced in everyday urban life, applying computational and machine learning techniques in two ways. First, she uses mobile device GPS data to measure economic segregation through daily activity patterns, analyzing where people go and whom they encounter. Second, she applies computer vision methods to Google Street View imagery to identify indicators of investment and inequality in the built environment. She holds an M.A. in Sociology from Stanford University and a B.A. in Development Studies and East Asian Studies from Brown University. Prior to starting her PhD, she worked as a research program manager with the Stanford Center on China's Economy and Institutions, studying the effects of community health interventions on early human capital formation in rural China.

Steven Dillmann

Steven is a PhD candidate in Computational Mathematics. His research focuses on accelerating scientific discovery across several disciplines through the development and rigorous evaluation of agentic research assistants, scientific foundation models, and probabilistic inference methods. He leads Terminal-Bench-Science, a community effort to build the standard for evaluating AI agents on real research workflows, developed in collaboration with domain experts at research institutions worldwide. He has also worked on the safety and ethical evaluation of the genomic foundation model Evo 2, simulation-based inference for strong gravitational lensing, and a unified probabilistic framework for multi-wavelength astronomical imaging. Steven previously studied Aerospace Engineering at Imperial College London and Data Intensive Science at the University of Cambridge. He has had research placements at the European Space Agency, NASA JPL, and the Harvard-Smithsonian Center for Astrophysics, where he developed machine learning methods for astronomy and planetary science.

Bruria Friedman Feldman

Bruria is a JSD candidate at Stanford Law School. Her research examines the role of technology and cybersecurity firms in AI governance and international cyber conflict. Her work combines legal analysis with empirical and computational methods to study firms as strategic actors in international cyber conflict, focusing on their motivations and decision-making when responding to nation-state-led cyberattacks. She holds an LL.B. and LL.M. from Tel Aviv University and completed Stanford's Master of the Science of Law program. Before entering legal academia, she served for more than six years as an intelligence officer, including as a cyber and network analyst, course commander, and later as Ethics Lead for cyber operations.

Yifan Guo

Yifan is a PhD student in Operations, Information, and Technology at the Stanford Graduate School of Business. His research focuses on human–AI decision-making and AI alignment. In particular, he studies how tools from operations research, economics, and computer science can be used to design better frameworks for human–AI collaboration and more effective systems for evaluating AI agents. Before coming to Stanford, Yifan earned a BS in Mathematics from the University of Science and Technology of China.

Yu He

Yu is a PhD candidate in Computer Science. Her research interests lie in studying the algorithmic and structural reasoning abilities of neural networks, such as large language models. Algorithmic reasoning provides a rule-based approach to solving complex problems in a principled manner, and she works toward an algorithmically driven paradigm to build AI systems that are not only more data-efficient and capable at solving complex tasks, but also more reliable and safer for human-centered applications. Before Stanford, she graduated from the University of Cambridge with a BA and a Master’s of Engineering in Computer Science.

Rohan Koodli

Rohan is a PhD candidate in Biomedical Informatics. He is interested in machine learning for drug discovery. He develops ML methods that can design, modify, and estimate properties of small molecule drugs, allowing medicinal chemists to efficiently find candidate drugs for a target. Prior to Stanford, he received his bachelor's and master's degrees in computer science from the University of California, Berkeley.

Chelsea Li

Chelsea is an M.D./Ph.D. candidate in the Neurosciences program. Her research focuses on how the brain builds coherent internal models of the world and what happens when those models break down. She combines large-scale, multi-region Neuropixels recordings in mice with causal optogenetic manipulation and population-geometry analysis to map how ketamine disrupts higher-order representations of space and emotion. She connects these findings to questions in artificial intelligence about how structured, low-dimensional representations are constructed and lost. Prior to Stanford, she completed her undergraduate degree in Human Biology at the University of Virginia, studied neural circuits underlying feeding regulation at UCSF, and conducted brainstem circuit research spanning single-cell sequencing and optogenetics.

Junyi Liu

Junyi is a PhD student in the Department of Statistics. His research focuses on developing statistical methods for evaluating and improving modern AI systems, with particular interest in preference modeling, reliable statistical inference, and reinforcement learning from human feedback. More broadly, he is interested in problems at the intersection of statistics and machine learning, especially those motivated by real-world AI applications.

Matthew Liu

Matthew is a PhD student in Computer Science. His research develops DNA language models to enable safe AI-driven design of novel biological sequences and systems. More broadly, he is interested in building machine learning methods that make biological design more predictable and programmable. Previously, he earned B.A. degrees in computer science, applied mathematics, and statistics from UC Berkeley.

Luca Mondonico

Luca is a PhD candidate in Materials Science and Engineering, with a minor in Computer Science. His research lies at the intersection of machine learning, materials science, and human-centered sensing. He develops predictive and generative models for molecular design and materials discovery, combining large-scale computational screening with experimental validation to accelerate next-generation energy-storage technologies. In parallel, he develops efficient machine learning methods for wearable and edge devices, with applications in wearable health and activity sensing where computation, latency, power, and privacy are key constraints. Previously, he worked on Tesla's battery teams as a Cell Engineer and Materials Engineer. He earned his B.S. from Politecnico di Milano and his M.S. from ETH Zürich.

Jinhee Paeng

Jinhee is a PhD candidate in Computational and Mathematical Engineering (ICME). Her research focuses on understanding how intelligence emerges in large networks of neurons, both biological and artificial. Her approach is to model a network mathematically at the appropriate scale, derive its behavior analytically, and test those predictions against experimental data. Her work spans from rigorous mathematical modeling and analysis to developing new statistical methods for assessing how well a model explains the data. Before Stanford, she studied mathematics and statistics at Seoul National University, where she worked on the ergodic behavior of randomized optimizers and on synchronization models. Building on this experience with the long-time behavior of chaotic interacting systems, she now aims to uncover the hidden mathematics of neuronal systems.

Eva Prakash

Eva is a PhD student in Computer Science. Her research focuses on developing multimodal AI methods for healthcare, with an emphasis on vision-language models for radiology and temporal reasoning in medical imaging. She is interested in building models that can reason about longitudinal patient data and understand how clinical findings evolve over time. Before her PhD, Eva earned her B.S. in Computer Science and a minor in Creative Writing from Stanford University, as well as her M.S. in Computer Science with Distinction in Research from Stanford University.

Jing Shang

Jing is a PhD candidate in the Department of Statistics. Her research develops statistically grounded data science methods for adaptive decision-making under heterogeneous conditions, with a focus on problems in energy systems and healthcare. In energy systems, she collaborates with the National Laboratory of the Rockies to develop weather-aware electricity pricing methods that use forecasts and demand patterns to reduce grid stress and improve resilience to extreme weather. In healthcare, she develops personalized learning methods that account for patient heterogeneity to improve risk prediction and clinical decision-making.

Victor Wu

Victor is a JD/PhD candidate in Political Science at Stanford University and Stanford Law School. His research focuses on AI governance, environmental sustainability, and the intersection of the two. Previously, he graduated as valedictorian from Dartmouth College in 2022 with a triple major in Government, Environmental Studies, and Quantitative Social Science.

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Stanford HAI Welcomes 15 New Data Science Scholars | Stanford HAI