School of Engineering
Showing 1-8 of 8 Results
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Margot Gerritsen
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.
Teaching
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.
Professional Activities
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 -
Kay Giesecke
Professor of Management Science and Engineering
On Leave from 04/01/2025 To 06/30/2025Current Research and Scholarly InterestsKay is a financial technologist whose research agenda is driven by significant applied problems in areas such as investment management, risk analytics, lending, and regulation, where data streams are increasingly large-scale and dynamical, and where computational demands are critical. He develops and analyzes statistical machine learning methods to make explainable data-driven decisions in these and other areas and efficient numerical algorithms to address the associated computational issues.
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Peter Glynn
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
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Ashish Goel
Professor of Management Science and Engineering and, by courtesy, of Computer Science
On Leave from 04/01/2025 To 06/30/2025BioAshish 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.
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Andrea Goldsmith
Stephen Harris Professor in the School of Engineering, Emerita
BioAndrea Goldsmith is the Dean of Engineering and Applied Science and the Arthur LeGrand Doty Professor of Electrical and Computer Engineering at Princeton University. She was previously the Stephen Harris Professor of Engineering and Professor of Electrical Engineering at Stanford University, where she is now Harris Professor Emerita. Her research interests are in information theory, communication theory, and signal processing, and their application to wireless communications, interconnected systems, and biomedical devices. She founded and served as Chief Technical Officer of Plume WiFi (formerly Accelera, Inc.) and of Quantenna (QTNA), Inc, and she serves on the Board of Directors for Intel (INTC), Medtronic (MDT), Crown Castle Inc (CCI), and the Marconi Society. She also serves on the Presidential Council of Advisors on Science and Technology (PCAST). Dr. Goldsmith is a member of the National Academy of Engineering, the Royal Academy of Engineering, and the American Academy of Arts and Sciences. She is a Fellow of the IEEE and has received several awards for her work, including the Marconi Prize, the ACM Sigmobile Outstanding Contribution Award, the IEEE Sumner Technical Field Award, the ACM Athena Lecturer Award, the ComSoc Armstrong Technical Achievement Award, the Kirchmayer Graduate Teaching Award, the WICE Mentoring Award, and the Silicon Valley/San Jose Business Journal’s Women of Influence Award. She is author of the book ``Wireless Communications'' and co-author of the books ``MIMO Wireless Communications,” “Principles of Cognitive Radio,” and “Machine Learning and Wireless Communications,” all published by Cambridge University Press, as well as an inventor on 29 patents. She received the B.S., M.S. and Ph.D. degrees in Electrical Engineering from U.C. Berkeley.
Dr. Goldsmith is the founding Chair of the IEEE Board of Directors Committee on Diversity and Inclusion. She served as President of the IEEE Information Theory Society in 2009, as founding Chair of its Student Committee, and as founding Editor-in-Chief of the IEEE Journal on Selected Areas in Information Theory. She has also served on the Board of Governors for both the IEEE Information Theory and Communications Societies. At Stanford she served as Chair of Stanford’s Faculty Senate and for multiple terms as a Senator, and on its Academic Council Advisory Board, Budget Group, Committee on Research, Planning and Policy Board, Commissions on Graduate and on Undergraduate Education, Faculty Women’s Forum Steering Committee, and Task Force on Women and Leadership. -
Kenneth Goodson
Vice Provost for Graduate Education and Postdoctoral Affairs, Davies Family Provostial Professor, and Professor, by courtesy, of Materials Science and Engineering
Current Research and Scholarly InterestsProf. Goodson’s Nanoheat Lab studies heat transfer in electronic nanostructures, microfluidic heat sinks, and packaging, focussing on basic transport physics and practical impact for industry. We work closely with companies on novel cooling and packaging strategies for power devices, portables, ASICs, & data centers. At present, sponsors and collaborators include ARPA-E, the NSF POETS Center, SRC ASCENT, Google, Intel, Toyota, Ford, among others.
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Catherine Gorle
Associate Professor of Civil and Environmental Engineering and, by courtesy, of Mechanical 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.
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Leonidas Guibas
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.