School of Engineering
Showing 201-300 of 482 Results
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Irene Lo
Assistant Professor of Management Science and Engineering
BioIrene is an assistant professor in Management Science & Engineering at Stanford University. Her research is on designing matching markets and assignment processes to improve market outcomes, with a focus on public sector applications and socially responsible operations research. She is also interested in mechanism design for social good and graph theory.
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Ramesh Manian
Ph.D. Student in Management Science and Engineering, admitted Autumn 2024
Masters Student in Management Science and Engineering, admitted Summer 2022BioRamesh is a Principal Program Manager at Microsoft, where he works to help Fortune 500 companies with digital transformation of their modern work processes. He was a member of the founding team of TIBCO, a provider of integration and analytics solutions. He has founded several other startups in robotics, AI, and education. Ramesh is a life-long learner with diverse interests and currently interested on educating himself in biology and quantum computing, in addition to working toward his MS degree in MS&E.
He also ran Station Cafe, an Italian restaurant, in San Carlos between 2010 and 2014. -
Holly McCall
Program Manager, Management Science and Engineering - Technology Ventures Program
Current Role at StanfordProgram Manager STVP
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Robert McGinn
Professor (Teaching) of Management Science and Engineering, Emeritus
Current Research and Scholarly Interestsexploration of ethical issues related to nanotechnology
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Anay Mehrotra
Postdoctoral Scholar, Management Science and Engineering
BioI am a Postdoctoral Scholar at Stanford, where I am excited to work with Amin Saberi. I completed my Ph.D. at Yale University where I was fortunate to be advised by Amin Karbasi and Manolis Zampetakis.
My research focuses on machine learning under complex conditions where traditional assumptions break down. My work has two parts. First, I develop foundations for machine learning with missing and selectively observed data (spanning causal inference, limited-dependence, truncated statistics, and omissions shaped by societal biases). Second, I study why generative AI systems (including language models) are effective and design methods to evaluate and improve their safety.
My work has received the Best Paper Award at COLT, been featured in WIRED, and received the Sir Binay Kumar Sinha award from IIT Kanpur. As an undergraduate, I represented IIT Kanpur at the ICPC World Final. While at Yale, I also taught at the Yale ICPC Club. -
Paul Milgrom
Shirley R. and Leonard W. Ely, Jr. Professor in the School of Humanities and Sciences, Professor of Economics, Senior Fellow at SIEPR and Professor, by courtesy, of Economics at the GSB and of Management Science and Engineering
BioPaul Milgrom is the Shirley and Leonard Ely professor of Humanities and Sciences in the Department of Economics at Stanford University and professor, by courtesy, in the Stanford Graduate School of Business and in the Department of Management Sciences and Engineering. Born in Detroit, Michigan on April 20, 1948, he is a member of both the National Academy of Sciences and the American Academy of Arts and Sciences and a winner of the 2008 Nemmers Prize in Economics, the 2012 BBVA Frontiers of Knowledge award, the 2017 CME-MSRI prize for Innovative Quantitative Applications, and the 2018 Carty Award for the Advancement of Science.
Milgrom is known for his work on innovative resource allocation methods, particularly in radio spectrum. He is coinventor of the simultaneous multiple round auction and the combinatorial clock auction. He also led the design team for the FCC's 2017 incentive auction, which reallocated spectrum from television broadcast to mobile broadband.
According to his BBVA Award citation: “Paul Milgrom has made seminal contributions to an unusually wide range of fields of economics including auctions, market design, contracts and incentives, industrial economics, economics of organizations, finance, and game theory.” As counted by Google Scholar, Milgrom’s books and articles have received more than 80,000 citations.
Finally, Milgrom has been a successful adviser of graduate students, winning the 2017 H&S Dean's award for Excellence in Graduate Education. -
Walter Murray
Professor (Research) of Management Science and Engineering, Emeritus
BioProfessor Murray's research interests include numerical optimization, numerical linear algebra, sparse matrix methods, optimization software and applications of optimization. He has authored two books (Practical Optimization and Optimization and Numerical Linear Algebra) and over eighty papers. In addition to his University work he has extensive consulting experience with industry, government, and commerce.
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Dale Nesbitt
Adjunct Lecturer, Management Science and Engineering
BioDr. Nesbitt has been teaching MSE 252 (Decision Analysis), MSE 352 (Professional Decision Analysis), MSE 353 (Advanced Decision Analysis), MSE 299 (Coercion Free Social Systems), and MSE 254 (The Ethical Analyst) in the department. He has practiced and taught in these fields, and economic modeling, for several decades.
Dr. Nesbitt has been researching Bayesian statistical analysis, ethics, and ethical theories in a general setting (i.e., personal ethics not necessarily associated with any particular field or discipline). His research focuses on ethics per se, not ethics related to a specific technology, commodity, discipline, area, or practice. He is currently focused on ethics from a socio-personal perspective, one in which coercion is minimized or sanctioned, one that blends the utilitarian approach of Harsanyi, Mill, Bentham, and others with the uncoerced game theory approach of Nash and Harsanyi. The objective of this research is to give a roadmap for people (and groups) to behave ethically and do good and also to be able to consider ethical decision making under uncertainty.
Dr. Nesbitt is completing a monograph on Bayesian Linear Regression intended to unify key dimensions of the field around a pure Bayesian probabilistic viewpoint, what he calls “unabashed Bayes.” The monograph is scheduled for completion in 2022. Dr. Nesbitt continues to research and practice Bayesian regression and probabilistic analysis, recently applying it to disciplines such as automobile selection, jet technology and fuel projection, and petrochemicals demand.
Dr. Nesbitt has focused for many years on building economic-environmental models of the key energy commodities—oil and refined products, natural gas, petrochemicals, automobiles, electric power generation, natural gas and electricity storage, renewable energy, environmental emissions and remediation, and demand/emission. His models and work in the field are well known, extending the classical economic equilibrium approach.
Dr. Nesbitt has worked and published in the field of semi-Markovian Decision Problems (the area of his thesis at Stanford), energy economics, cartels and monopolies, methods for modeling markets, Bayesian statistics, and free (meaning uncoerced) social systems. -
Liem M. Nguyen
Ph.D. Student in Management Science and Engineering, admitted Summer 2026
Masters Student in Management Science and Engineering, admitted Autumn 2019Current Research and Scholarly InterestsDevelopment of machine learning methods to identify structures and processes that promote high quality health care using large databases of electronic health record metadata.
