School of Engineering
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Associate Professor of Management Science and Engineering
BioProfessor Ugander's research develops algorithmic and statistical frameworks for analyzing social networks, social systems, and other large-scale data-rich contexts. He is particularly interested in the challenges of causal inference and experimentation in these complex domains. His work commonly falls at the intersections of graph theory, machine learning, statistics, optimization, and algorithm design.
Associate Professor of Management Science and Engineering
Current Research and Scholarly InterestsMelissa Valentine is an Assistant Professor at Stanford University in the Management Science and Engineering Department, and co-director of the Center for Work, Technology, and Organization (WTO).
Prof Valentine's research focuses on understanding how new technologies change work and organizations. She conducts in-depth observational studies to develop new understanding about new forms of organizing. Her work makes contributions to understanding classic and longstanding challenges in designing groups and organizations (e.g., the role of hierarchy, how to implement change, team stability vs. flexibility) but also brings in deep knowledge of how the rise of information technology has made possible new and different team and organizational forms. Her most recent study examined how the deployment of new algorithms changed the organizational structure of a retail tech company.
Prof. Valentine has won awards for both research and teaching. She and collaborators won a Best Paper Award at the CHI Conference on Human Factors in Computing Systems and the Outstanding Paper with Practical Implications award from the Organizational Behavior division of the Academy of Management. In 2013, she won the Organization Science/INFORMS dissertation proposal competition and received her PhD from Harvard University.
Benjamin Van Roy
Professor of Electrical Engineering, of Management Science and Engineering
BioBenjamin Van Roy is a Professor at Stanford University, where he has served on the faculty since 1998. His research focuses on understanding how an agent interacting with a poorly understood environment can learn over time to make effective decisions. He is interested in the design of efficient reinforcement learning algorithms, understanding what is possible or impossible in this domain, and applying the technology toward the benefit of society. 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-edits 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. 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, and the Management Science and Engineering Department's Graduate Teaching Award. 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.
Professor (Research) of Management Science and Engineering and Senior Fellow at the Precourt Institute for Energy
BioJohn P. Weyant is Professor of Management Science and 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 fourteen-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
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.
Kwoh-Ting Li Professor in the School of Engineering and Professor, by courtesy, of Electrical Engineering
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.