School of Engineering
Showing 281-300 of 482 Results
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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.
