Institute for Computational and Mathematical Engineering (ICME)
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Professor of Energy Resources Engineering, Emerita
Current Research and Scholarly InterestsResearch
My work is about understanding and simulating complicated fluid flow problems. My research focuses on the design of highly accurate and efficient parallel computational methods to predict the performance of enhanced oil recovery methods. I'm particularly interested in gas injection and in-situ combustion processes. These recovery methods are extremely challenging to simulate because of the very strong nonlinearities in the governing equations. Outside petroleum engineering, I'm active in coastal ocean simulation with colleagues from the Department of Civil and Environmental Engineering, yacht research and pterosaur flight mechanics with colleagues from the Department of Mechanical and Aeronautical Engineering, and the design of search algorithms in collaboration with the Library of Congress and colleagues from the Institute of Computational and Mathematical Engineering.
I teach courses in both energy related topics (reservoir simulation, energy, and the environment) in my department, and mathematics for engineers through the Institute of Computational and Mathematical Engineering (ICME). I also initiated two courses in professional development in our department (presentation skills and teaching assistant training), and a consulting course for graduate students in ICME, which offers expertise in computational methods to the Stanford community and selected industries.
Senior Associate Dean, School of Earth, Energy and Environmental Sciences, Stanford (from 2015); Director, Institute for Computational and Mathematical Engineering, Stanford (from 2010); Stanford Fellow (2010-2012); Magne Espedal Professor II, Bergen University (2011-2014); Aldo Leopold Fellow (2009); Chair, SIAM Activity group in Geosciences (2007, present, reelected in 2009); Faculty Research Fellow, Clayman Institute (2008); Elected to Council of Society of Industrial and Applied Mathematics (SIAM) (2007); organizing committee, 2008 Gordon Conference on Flow in Porous Media; producer, Smart Energy podcast channel; Director, Stanford Yacht Research; Co-director and founder, Stanford Center of Excellence for Computational Algorithms in Digital Stewardship; Editor, Journal of Small Craft Technology; Associate editor, Transport in Porous Media; Reviewer for various journals and organizations including SPE, DoE, NSF, Journal of Computational Physics, Journal of Scientific Computing, Transport in Porous Media, Computational Geosciences; member, SIAM, SPE, KIVI, AGU, and APS
Professor of Management Science and Engineering
Current Research and Scholarly InterestsKay is a financial technologist and engineer. He develops stochastic financial models, designs statistical methods for analyzing financial data, examines simulation and other numerical algorithms for solving the associated computational problems, and performs empirical analyses. Much of Kay's work is driven by important applications in areas such as credit risk management, investment management, and, most recently, housing finance.
Thomas W. Ford Professor in the School of Engineering and Professor, by courtesy, of Electrical Engineering
Current Research and Scholarly InterestsStochastic modeling; statistics; simulation; finance
Professor of Management Science and Engineering and, by courtesy, of Computer Science
BioAshish Goel is a Professor of Management Science and Engineering and (by courtesy) Computer Science at Stanford University. He received his PhD in Computer Science from Stanford in 1999, and was an Assistant Professor of Computer Science at the University of Southern California from 1999 to 2002. His research interests lie in the design, analysis, and applications of algorithms.
Assistant Professor of Civil and Environmental Engineering
Current Research and Scholarly InterestsGorle's research focuses on the development of predictive flow simulations to support the design of sustainable buildings and cities. Specific topics of interest are the coupling of large- and small-scale models and experiments to quantify uncertainties related to the variability of boundary conditions, the development of uncertainty quantification methods for low-fidelity models using high-fidelity data, and the use of field measurements to validate and improve computational predictions.
Paul Pigott Professor of Engineering and Professor, by courtesy, of Electrical Engineering
Current Research and Scholarly InterestsGeometric and topological data analysis and machine learning. Algorithms for the joint analysis of collections of images, 3D models, or trajectories. 3D reconstruction.