School of Engineering
Showing 401-458 of 458 Results
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Benjamin Van Roy
Professor of Electrical Engineering, of Management Science and Engineering and, by courtesy, of Computer Science
BioBenjamin Van Roy is a Professor at Stanford University, where he has served on the faculty since 1998. His current research focuses on reinforcement learning. Beyond academia, he leads a DeepMind Research team in Mountain View, and has also led research programs at Unica (acquired by IBM), Enuvis (acquired by SiRF), and Morgan Stanley.
He is a Fellow of INFORMS and IEEE and has served on the editorial boards of Machine Learning, Mathematics of Operations Research, for which he co-edited the Learning Theory Area, Operations Research, for which he edited the Financial Engineering Area, and the INFORMS Journal on Optimization. He received the SB in Computer Science and Engineering and the SM and PhD in Electrical Engineering and Computer Science, all from MIT, where his doctoral research was advised by John N. Tstitsiklis. He has been a recipient of the MIT George C. Newton Undergraduate Laboratory Project Award, the MIT Morris J. Levin Memorial Master's Thesis Award, the MIT George M. Sprowls Doctoral Dissertation Award, the National Science Foundation CAREER Award, the Stanford Tau Beta Pi Award for Excellence in Undergraduate Teaching, the Management Science and Engineering Department's Graduate Teaching Award, and the Lanchester Prize. He was the plenary speaker at the 2019 Allerton Conference on Communications, Control, and Computing. He has held visiting positions as the Wolfgang and Helga Gaul Visiting Professor at the University of Karlsruhe, the Chin Sophonpanich Foundation Professor and the InTouch Professor at Chulalongkorn University, a Visiting Professor at the National University of Singapore, and a Visiting Professor at the Chinese University of Hong Kong, Shenzhen. -
John Weyant
Professor (Research) of Management Science and Engineering, of Energy Science Engineering and Senior Fellow at the Precourt Institute for Energy
BioJohn P. Weyant is Professor of Management Science and Engineering, Professor of Energy Science & Engineering, and Director of the Energy Modeling Forum (EMF) at Stanford University. He is also a Senior Fellow of the Precourt Institute for Energy and an an affiliated faculty member of the Stanford School of Earth, Environment and Energy Sciences, the Woods Institute for the Environment, and the Freeman-Spogli Institute for International Studies at Stanford. His current research focuses on analysis of multi-sector, multi-region coupled human and earth systems dynamics, global change systems analysis, energy technology assessment, and models for strategic planning.
Weyant was a founder and serves as chairman of the Integrated Assessment Modeling Consortium (IAMC), a seventeen-year old collaboration among over 60 member institutions from around the world. He has been an active adviser to the United Nations, the European Commission, U.S.Department of Energy, the U.S. Department of State, and the Environmental Protection Agency. In California, he has been and adviser to the California Air Resources, the California Energy Commission and the California Public Utilities Commission..
Weyant was awarded the US Association for Energy Economics’ 2008 Adelmann-Frankel award for unique and innovative contributions to the field of energy economics and the award for outstanding lifetime contributions to the Profession for 2017 from the International Association for Energy Economics, and a Life Time Achievement award from the Integrated Assessment Modeling Consortium in 2018. Weyant was honored in 2007 as a major contributor to the Nobel Peace prize awarded to the Intergovernmental Panel on Climate Change and in 2008 by Chairman Mary Nichols for contributions to the to the California Air Resources Board's Economic and Technology Advancement Advisory Committee on AB 32.
Fields of Specialization:
Energy/Environmental Policy Analysis, Strategic Planning
Interests:
Overall goal is to accelerate the use of systems models at state, country, and global scales, aiming to provide the best available information and insights to government and private-sector decision makers. Specific areas include energy, climate change, and sustainable development policy, including emerging technologies and market design alternatives. Draws on concepts and techniques from science and engineering fundamentals (e.g., thermodynamics, fluid mechanics, materials science, and electrical power systems), operations research, economics, finance, and decision theory. -
Chenghan Xie
Ph.D. Student in Management Science and Engineering, admitted Autumn 2024
Masters Student in Management Science and Engineering, admitted Autumn 2026Current Research and Scholarly InterestsOptimization, theory & practice. Energy-aware AI, nerual-network structure & data center management.
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Renyuan Xu
Assistant Professor of Management Science and Engineering
BioRenyuan Xu is an assistant professor of Management Science and Engineering (MS&E) at Stanford University. Prior to joining Stanford, she held positions at New York University (2024-2025) and the University of Southern California (2021–2024), and was a Hooke Research Fellow at the Mathematical Institute, University of Oxford (2019–2021). She received her Ph.D. in Operations Research from the University of California, Berkeley in 2019. Renyuan's current research interests include mathematical finance, stochastic analysis, stochastic controls and games, and machine learning theory. She received an NSF CAREER Award in 2024, the SIAM Activity Group on Financial Mathematics and Engineering Early Career Prize in 2023, and two JP Morgan AI Faculty Research Awards in 2022 and 2025.
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Yinyu Ye
Kwoh-Ting Li Professor in the School of Engineering, Emeritus
Current Research and Scholarly InterestsMy current research interests include Continuous and Discrete Optimization, Algorithm Development and Analyses, Algorithmic Game/Market Theory and Mechanism-Design, Markov Decision Process and Reinforcement Learning, Dynamic/Online Optimization and Resource Allocation, and Stochastic and Robust Decision Making. These areas have been the unique and core disciplines of MS&E, and extended to new application areas in AI, Machine Learning, Data Science, and Business Analytics.
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Angelina You
Masters Student in Management Science and Engineering, admitted Spring 2023
BioAngelina is a MS student in Management Science & Engineering at Stanford University, specializing in technology and engineering management. She is passionate about leveraging technology and analytics to address societal issues and assist the underprivileged. She has four years of data science and product experience at Meta and two other high-growth tech startups but she is also interested in entrepreneurship. Outside of work, she serves as a project-client manager for Statistics Without Borders and co-leads a graduate student startup community at Stanford. An explorer at heart, Angelina has a wide range of interests, including dancing, boxing, cooking, traveling, and cuddling with her baby Yorkie, Yoyo.
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Tijana Zrnic
Assistant Professor of Statistics, of Management Science and Engineering and, by courtesy, of Computer Science
BioTijana Zrnic is an Assistant Professor at Stanford University, jointly appointed between Statistics, Management Science & Engineering, and, by courtesy, Computer Science. She works on foundational questions in machine learning, statistics, and data-driven decision-making. Example topics of interest include AI-assisted statistical inference and data collection, performative prediction, and studying selection bias.
