School of Engineering
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M Elisabeth Pate-Cornell
Burton J. and DeeDee McMurtry Professor in the School of Engineering
BioDr. Marie-Elisabeth Paté-Cornell is the Burt and Deedee McMurtry Professor in the School of Engineering and Professor and Founding Chair (2000-2011) of the Department of Management Science and Engineering at Stanford University. Her specialty is engineering risk analysis with application to complex systems (space, medical, offshore oil platforms, 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 recent work is on the use of game theory in risk analysis with applications that have included counter-terrorism, nuclear counter-proliferation problems and cyber security. She is the author of more than one hundred publications, and the co-editor of a book on Perspectives on Complex Global Problems (2016).
She is a member of the National Academy of Engineering, of the French Académie des Technologies, of the NASA Advisory Council and of several boards including the Board of Advisors of the Naval Postgraduate School and the Navy War College. Dr. Paté-Cornell was a member of the President’s Foreign Intelligence Advisory Board from December 2001 to 2008, of the board of the Aerospace Corporation (2004-2013) of Draper Laboratory (2009-2016), and of InQtel (2006-2017). 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.).
Assistant Professor of Management Science and Engineering
Current Research and Scholarly InterestsHis research focuses on understanding and managing financial risk. He develops mathematical financial model and statistical methods, analyzes financial data and engineers computational techniques. His research is divided into three streams: stochastic financial modeling, high-frequency statistics and statistical learning in high-dimensional financial data sets. His most recent work includes developing machine learning solutions to big-data problems in empirical risk management and asset pricing.