School of Engineering
Showing 201-300 of 458 Results
-
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.
-
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. -
Robert McGinn
Professor (Teaching) of Management Science and Engineering, Emeritus
Current Research and Scholarly Interestsexploration of ethical issues related to nanotechnology
-
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. -
Holden Moore
Undergraduate, Management Science and Engineering
Undergraduate, Symbolic SystemsBioStanford University undergraduate student majoring in symbolic systems with a concentration in neuroscience. Pursuing an interdisciplinary degree across diverse fields of study including computer science, mathematics, neuroscience, statistics, philosophy, and psychology.
-
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.
-
Dale Nesbitt
Affiliate, 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 InterestsMy goal is to improve patient safety by understanding how teamwork and coordination shape care in complex clinical settings. I develop methodology that leverages electronic health record metadata to enable the study of health care delivery at scale. During my PhD, I aim to build on this foundation to connect methodological advances with organizational understanding and meaningful improvements in care.
-
Doug Owens
Henry J. Kaiser, Jr. Professor, Senior Fellow at the Freeman Spogli Institute for International Studies and Professor, by courtesy, of Management Science and Engineering
Current Research and Scholarly InterestsMy research uses decision modeling, cost-effectiveness analysis, and meta-analysis to evaluate clinical and health policy problems. Much of my work involves development of national guidelines for prevention and treatment.
-
M Elisabeth Pate-Cornell
Burton J. and DeeDee McMurtry Professor in the School of Engineering and Professor, by courtesy, of Aeronautics and Astronautics
BioDr. Marie-Elisabeth Paté-Cornell is the Burt and Deedee McMurtry Professor in the School of Engineering, and a Professor and Founding Chair of the Department of Management Science and Engineering at Stanford University (2000-2011). Previously, she was the Professor and Chair of the Stanford Department of Industrial Engineering and Engineering Management and an Assistant Professor of Civil Engineering at MIT. Her specialty is engineering risk analysis with application to complex systems (seismic risk, space systems, medical procedures and devices, offshore oil platforms, cyber security, etc.). Her earlier research has focused on the optimization of warning systems and the explicit inclusion of human and organizational factors in the analysis of systems’ failure risks. Her more recent work is on the use of game theory in risk analysis with applications that have included counterterrorism and cyber security.
She is a member of the National Academy of Engineering where she chairs the section of Interdisciplinary Engineering and Special Fields, of the French Académie des Technologies, and of the NASA Advisory Council. She is co-chair of the committee of the National Academies (NASEM) on risk analysis methods for nuclear war and nuclear terrorism. She is a Fellow (and past president) of the Society for Risk Analysis and of the Institute for Operations Research and Management Science. She is the author of more than one hundred publications, with several best paper awards, and the co-editor of a book on Perspectives on Complex Global Problems (2016). She was a member of the Board of Advisors of the Naval Postgraduate School, which she chaired from 2004 to 2006, and of the Navy War College. Dr. Paté-Cornell was also a member of the President’s (Foreign) Intelligence Advisory Board (2001-2008), of the board of the Aerospace Corporation (2004-2013) of Draper Laboratory (2009-2016), and of InQtel (2006-2017). She was awarded the Frank Ramsey Medal of the Decision Analysis Society, the 2021 IEEE Ramo medal in Systems Engineering and Science, and the 2022 PICMET Award for Leadership in Technology Management. She is a Fellow (and past president) of the Society for Risk Analysis and of the Institute for Management Science and Operations Research, and a Distinguished Vising Scientist of the NASA Jet Propulsion Laboratory. She is the author of more than one hundred publications, for which she got several best paper awards, and the co-editor of a book on Perspectives on Complex Global Problems (2016). She holds a BS in Mathematics and Physics, Marseille (France), an Engineering degree (Applied Math/CS) from the Institut Polytechnique de Grenoble (France), an MS in Operations Research and a PhD in Engineering-Economic Systems, both from Stanford University.
She and her late husband, Dr. Allin Cornell had two children, Philip Cornell (born 1981) and Ariane Cornell (1984). She is married to Admiral James O. Ellis Jr. (US Navy, Ret.). -
Markus Pelger
Associate Professor of Management Science and Engineering and, by courtesy, of Finance at the Graduate School of Business
Current Research and Scholarly InterestsHis research focuses on understanding and managing financial risk. He develops mathematical financial models and statistical methods, analyzes financial data and engineers computational techniques. His research is divided into three streams: machine learning solutions to big-data problems in empirical asset pricing, statistical theory for high-dimensional data and stochastic financial modeling.
