Institute for Computational and Mathematical Engineering (ICME)
Showing 41-60 of 159 Results
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Ron Fedkiw
Canon Professor in the School of Engineering
BioFedkiw's research is focused on the design of new computational algorithms for a variety of applications including computational fluid dynamics, computer graphics, and biomechanics.
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Emily Fox
Professor of Statistics and of Computer Science
BioEmily Fox is a Professor in the Departments of Statistics and Computer Science at Stanford University. Prior to Stanford, she was the Amazon Professor of Machine Learning in the Paul G. Allen School of Computer Science & Engineering and Department of Statistics at the University of Washington. From 2018-2021, Emily led the Health AI team at Apple, where she was a Distinguished Engineer. Before joining UW, Emily was an Assistant Professor at the Wharton School Department of Statistics at the University of Pennsylvania. She earned her doctorate from Electrical Engineering and Computer Science (EECS) at MIT where her thesis was recognized with EECS' Jin-Au Kong Outstanding Doctoral Thesis Prize and the Leonard J. Savage Award for Best Thesis in Applied Methodology.
Emily has been awarded a CZ Biohub Investigator Award, Presidential Early Career Award for Scientists and Engineers (PECASE), a Sloan Research Fellowship, ONR Young Investigator Award, and NSF CAREER Award. Her research interests are in modeling complex time series arising in health, particularly from health wearables and neuroimaging modalities. -
Oliver Fringer
Professor of Civil and Environmental Engineering and of Oceans
BioFringer's research focuses on the development and application of numerical models and high-performance computational techniques to the study of fundamental processes that influence the dynamics of the coastal ocean, rivers, lakes, and estuaries.
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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
Current 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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Julia Gillespie
Director of Finance and Operations, Institute for Computational and Mathematical Engineering (ICME)
Current Role at StanfordI am the Director of Finance and Operations for the Institute for Computational Mathematics and Engineering within the School of Engineering.
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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
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.
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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.
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Pat Hanrahan
Canon Professor in the School of Engineering and Professor of Electrical Engineering, Emeritus
BioProfessor Hanrahan's current research involves rendering algorithms, high performance graphics architectures, and systems support for graphical interaction. He also has worked on raster graphics systems, computer animation and modeling and scientific visualization, in particular, volume rendering.
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Jerry Harris
The Cecil H. and Ida M. Green Professor in Geophysics, Emeritus
Current Research and Scholarly InterestsBiographical Information
Jerry M. Harris is the Cecil and Ida Green Professor of Geophysics and Associate Dean for the Office of Multicultural Affairs. He joined Stanford in 1988 following 11 years in private industry. He served five years as Geophysics department chair, was the Founding Director of the Stanford Center for Computational Earth and Environmental Science (CEES), and co-launched Stanford's Global Climate and Energy Project (GCEP). Graduates from Jerry's research group, the Stanford Wave Physics Lab, work in private industry, government labs, and universities.
Research
My research interests address the physics and dynamics of seismic and electromagnetic waves in complex media. My approach to these problems includes theory, numerical simulation, laboratory methods, and the analysis of field data. My group, collectively known as the Stanford Wave Physics Laboratory, specializes on high frequency borehole methods and low frequency labratory methods. We apply this research to the characterization and monitoring of petroleum and CO2 storage reservoirs.
Teaching
I teach courses on waves phenomena for borehole geophysics and tomography. I recently introduced and co-taught a new course on computational geosciences.
Professional Activities
I was the First Vice President of the Society of Exploration Geophysicists in 2003-04, and have served as the Distinguished Lecturer for the SPE, SEG, and AAPG. -
Trevor Hastie
John A. Overdeck Professor, Professor of Statistics and of Biomedical Data Sciences
Current Research and Scholarly InterestsFlexible statistical modeling for prediction and representation of data arising in biology, medicine, science or industry. Statistical and machine learning tools have gained importance over the years. Part of Hastie's work has been to bridge the gap between traditional statistical methodology and the achievements made in machine learning.
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Gianluca Iaccarino
Professor of Mechanical Engineering
Current Research and Scholarly InterestsComputing and data for energy, health and engineering
Challenges in energy sciences, green technology, transportation, and in general, engineering design and prototyping are routinely tackled using numerical simulations and physical testing. Computations barely feasible two decades ago on the largest available supercomputers, have now become routine using turnkey commercial software running on a laptop. Demands on the analysis of new engineering systems are becoming more complex and multidisciplinary in nature, but exascale-ready computers are on the horizon. What will be the next frontier? Can we channel this enormous power into an increased ability to simulate and, ultimately, to predict, design and control? In my opinion two roadblocks loom ahead: the development of credible models for increasingly complex multi-disciplinary engineering applications and the design of algorithms and computational strategies to cope with real-world uncertainty.
My research objective is to pursue concerted innovations in physical modeling, numerical analysis, data fusion, probabilistic methods, optimization and scientific computing to fundamentally change our present approach to engineering simulations relevant to broad areas of fluid mechanics, transport phenomena and energy systems. The key realization is that computational engineering has largely ignored natural variability, lack of knowledge and randomness, targeting an idealized deterministic world. Embracing stochastic scientific computing and data/algorithms fusion will enable us to minimize the impact of uncertainties by designing control and optimization strategies that are robust and adaptive. This goal can only be accomplished by developing innovative computational algorithms and new, physics-based models that explicitly represent the effect of limited knowledge on the quantity of interest.
Multidisciplinary Teaching
I consider the classical boundaries between disciplines outdated and counterproductive in seeking innovative solutions to real-world problems. The design of wind turbines, biomedical devices, jet engines, electronic units, and almost every other engineering system requires the analysis of their flow, thermal, and structural characteristics to ensure optimal performance and safety. The continuing growth of computer power and the emergence of general-purpose engineering software has fostered the use of computational analysis as a complement to experimental testing in multiphysics settings. Virtual prototyping is a staple of modern engineering practice! I have designed a new undergraduate course as an introduction to Computational Engineering, covering theory and practice across multidisciplanary applications. The emphasis is on geometry modeling, mesh generation, solution strategy and post-processing for diverse applications. Using classical flow/thermal/structural problems, the course develops the essential concepts of Verification and Validation for engineering simulations, providing the basis for assessing the accuracy of the results.
