Institute for Computational and Mathematical Engineering (ICME)
Showing 1-100 of 182 Results
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Guillermo Aboumrad Sidaoui
Ph.D. Student in Computational and Mathematical Engineering, admitted Summer 2018
BioWillie was born and raised in Mexico City. He later moved to the UK to complete his high school studies. In the fall of 2014, Willie arrived at Stanford to begin his undergraduate career in Mathematics. Interested in applications of mathematical theory, he later gained admission to the Master's program at ICME. He is currently pursuing a doctoral degree under the advisory of Prof. Daniel Bump.
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Christiane Marie Otten Adcock
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2018
Current Research and Scholarly InterestsI research theoretical and computational methods to model, design, and control energy systems. These methods include computational fluid dynamics, uncertainty quantification, and high performance computing. Energy systems include wind turbines, vehicles, the electricity grid, and carbon sequestration. Currently, I am researching uncertainty quantification of numerical solutions to PDEs using Multilevel Monte Carlo and task-based parallel computing in the Uncertainty Quantification lab.
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Rehman Ali
Ph.D. Student in Electrical Engineering, admitted Autumn 2017
Masters Student in Computational and Mathematical Engineering, admitted Autumn 2017BioRehman Ali received the B.S. degree in biomedical engineering from Georgia Institute of Technology in 2016. He is currently an NDSEG fellow, completing a M.S. in Computational & Mathematical Engineering and pursuing a Ph.D. in Electrical Engineering at Stanford. His research interests include signal processing, inverse problems, computational modeling of acoustics, and real-time beamforming algorithms. His current research is developing accurate and spatially resolved speed-of-sound imaging in tissue based on phase aberration correction, spatial coherence, and computed tomography
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Juan Alonso
Vance D. and Arlene C. Coffman Professor
BioProf. Alonso is the founder and director of the Aerospace Design Laboratory (ADL) where he specializes in the development of high-fidelity computational design methodologies to enable the creation of realizable and efficient aerospace systems. Prof. Alonso’s research involves a large number of different manned and unmanned applications including transonic, supersonic, and hypersonic aircraft, helicopters, turbomachinery, and launch and re-entry vehicles. He is the author of over 200 technical publications on the topics of computational aircraft and spacecraft design, multi-disciplinary optimization, fundamental numerical methods, and high-performance parallel computing. Prof. Alonso is keenly interested in the development of an advanced curriculum for the training of future engineers and scientists and has participated actively in course-development activities in both the Aeronautics & Astronautics Department (particularly in the development of coursework for aircraft design, sustainable aviation, and UAS design and operation) and for the Institute for Computational and Mathematical Engineering (ICME) at Stanford University. He was a member of the team that currently holds the world speed record for human powered vehicles over water. A student team led by Prof. Alonso also holds the altitude record for an unmanned electric vehicle under 5 lbs of mass.
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Jing An
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2016
BioI am a fourth-year PhD student in the Institute for Computational and Mathematical Engineering. I obtained my B.S. degree in Mathematics of Computation from the University of California, Los Angeles. Currently I am interested in applying machine learning methods into causal inference problems, and PDE analysis in mathematical biology and fluid mechanics.
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Ryan Michael Aronson
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2018
BioI am a second year PhD student in the Institute for Computational and Mathematical Engineering (ICME). I am mainly interested in high-order methods for Numerical PDEs, particularly in the area of computational fluid dynamics and large eddy simulation. Prior to coming to Stanford, I earned a B.S. in Aerospace Engineering Sciences at the University of Colorado Boulder, where I worked with Professor John Evans on residual-based variational multiscale turbulence modeling and isogeometric, structure-preserving collocation methods.
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Amel Awadelkarim
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2017
Club Sports Coach, DAPER Departmentwide OperationsBioMy academic background is in Computational Fluid Dynamics, Finite Element Analysis, and Continuum Mechanics with an M.S. in Engineering Science and Mechanics from Penn State University. I am becoming more and more intrigued by data analytics & applying machine learning techniques to social sciences and networks.
Outside of academia, my interests include consuming music at all times (digitally and at live shows), competing on various Ultimate Frisbee teams (Club and National levels), cooking, and generally exploring the surrounding area. -
Corinne Beck
Affiliates & Partners Program Manager, Institute for Computational and Mathematical Engineering (ICME)
Current Role at StanfordPrograms Manager
Institute for Computational & Mathematical Engineering (ICME)
School of Engineering -
Biondo Biondi
Barney and Estelle Morris Professor
Current Research and Scholarly InterestsResearch
My students and I devise new algorithms to improve the imaging of reflection seismic data. Images obtained from seismic data are the main source of information on the structural and stratigraphic complexities in Earth's subsurface. These images are constructed by processing seismic wavefields recorded at the surface of Earth and generated by either active-source experiments (reflection data), or by far-away earthquakes (teleseismic data). The high-resolution and fidelity of 3-D reflection-seismic images enables oil companies to drill with high accuracy for hydrocarbon reservoirs that are buried under two kilometers of water and up to 15 kilometers of sediments and hard rock. To achieve this technological feat, the recorded data must be processed employing advanced mathematical algorithms that harness the power of huge computational resources. To demonstrate the advantages of our new methods, we process 3D field data on our parallel cluster running several hundreds of processors.
Teaching
I teach a course on seismic imaging for graduate students in geophysics and in the other departments of the School of Earth Sciences. I run a research graduate seminar every quarter of the year. This year I will be teaching a one-day short course in 30 cities around the world as the SEG/EAGE Distinguished Instructor Short Course, the most important educational outreach program of these two societies.
Professional Activities
2007 SEG/EAGE Distinguished Instructor Short Course (2007); co-director, Stanford Exploration Project (1998-present); founding member, Editorial Board of SIAM Journal on Imaging Sciences (2007-present); member, SEG Research Committee (1996-present); chairman, SEG/EAGE Summer Research Workshop (2006) -
Stephen Boyd
Samsung Professor in the School of Engineering and Professor, by courtesy, of Computer Science and of Management Science and Engineering
BioStephen P. Boyd is the Samsung Professor of Engineering, and Professor of Electrical Engineering in the Information Systems Laboratory at Stanford University. He has courtesy appointments in the Department of Management Science and Engineering and the Department of Computer Science, and is member of the Institute for Computational and Mathematical Engineering. His current research focus is on convex optimization applications in control, signal processing, machine learning, and finance.
Professor Boyd received an AB degree in Mathematics, summa cum laude, from Harvard University in 1980, and a PhD in EECS from U. C. Berkeley in 1985. In 1985 he joined Stanford's Electrical Engineering Department. He has held visiting Professor positions at Katholieke University (Leuven), McGill University (Montreal), Ecole Polytechnique Federale (Lausanne), Tsinghua University (Beijing), Universite Paul Sabatier (Toulouse), Royal Institute of Technology (Stockholm), Kyoto University, Harbin Institute of Technology, NYU, MIT, UC Berkeley, CUHK-Shenzhen, and IMT Lucca. He holds honorary doctorates from Royal Institute of Technology (KTH), Stockholm, and Catholic University of Louvain (UCL).
Professor Boyd is the author of many research articles and four books: Introduction to Applied Linear Algebra: Vectors, Matrices, and Least-Squares (with Lieven Vandenberghe, 2018), Convex Optimization (with Lieven Vandenberghe, 2004), Linear Matrix Inequalities in System and Control Theory (with El Ghaoui, Feron, and Balakrishnan, 1994), and Linear Controller Design: Limits of Performance (with Craig Barratt, 1991). His group has produced many open source tools, including CVX (with Michael Grant), CVXPY (with Steven Diamond) and Convex.jl (with Madeleine Udell and others), widely used parser-solvers for convex optimization.
Professor Boyd has received many awards and honors for his research in control systems engineering and optimization, including an ONR Young Investigator Award, a Presidential Young Investigator Award, and the AACC Donald P. Eckman Award. In 2013, he received the IEEE Control Systems Award, given for outstanding contributions to control systems engineering, science, or technology. In 2012, Michael Grant and he were given the Mathematical Optimization Society's Beale-Orchard-Hays Award, for excellence in computational mathematical programming. He is a Fellow of the IEEE, SIAM, and INFORMS, a Distinguished Lecturer of the IEEE Control Systems Society, a member of the US National Academy of Engineering, a foreign member of the Chinese Academy of Engineering, and a foreign member of the National Academy of Engineering of Korea. He has been invited to deliver more than 90 plenary and keynote lectures at major conferences in control, optimization, signal processing, and machine learning.
He has developed and taught many undergraduate and graduate courses, including Signals & Systems, Linear Dynamical Systems, Convex Optimization, and a recent undergraduate course on Matrix Methods. His graduate convex optimization course attracts around 300 students from more than 20 departments. In 1991 he received an ASSU Graduate Teaching Award, and in 1994 he received the Perrin Award for Outstanding Undergraduate Teaching in the School of Engineering. In 2003, he received the AACC Ragazzini Education award, for contributions to control education, with citation: “For excellence in classroom teaching, textbook and monograph preparation, and undergraduate and graduate mentoring of students in the area of systems, control, and optimization.” In 2016 he received the Walter J. Gores award, the highest award for teaching at Stanford University. In 2017 he received the IEEE James H. Mulligan, Jr. Education Medal, for a career of outstanding contributions to education in the fields of interest of IEEE, with citation "For inspirational education of students and researchers in the theory and application of optimization." -
Steven Brill
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2016
BioI am a second year PhD student in the Institute for Computational and Mathematical Engineering (ICME). I am interested in computational fluid dynamics, higher order methods for numerical PDEs, and high performance computing. I earned my bachelor's degree in mechanical engineering at the University of Notre Dame. I am originally from Cincinnati, Ohio. In my free time I enjoy juggling, hiking, and college football.
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Carlos Bustamante
Professor of Biomedical Data Science, of Genetics and, by courtesy, of Biology
Current Research and Scholarly InterestsMy genetics research focuses on analyzing genome wide patterns of variation within and between species to address fundamental questions in biology, anthropology, and medicine. We focus on novel methods development for complex disease genetics and risk prediction in multi-ethnic settings. I am also interested in clinical data science and development of new diagnostics.I am also interested in disruptive innovation for healthcare including modeling long-term risk shifts and novel payment models.
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Léopold Cambier
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2015
Current Research and Scholarly InterestsFast Sparse Linear Solvers
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Emmanuel Candes
Barnum-Simons Chair in Math and Statistics, and Professor of Statistics and, by courtesy, of Electrical Engineering
BioEmmanuel Candès is the Barnum-Simons Chair in Mathematics and Statistics, a professor of electrical engineering (by courtesy) and a member of the Institute of Computational and Mathematical Engineering at Stanford University. Earlier, Candès was the Ronald and Maxine Linde Professor of Applied and Computational Mathematics at the California Institute of Technology. His research interests are in computational harmonic analysis, statistics, information theory, signal processing and mathematical optimization with applications to the imaging sciences, scientific computing and inverse problems. He received his Ph.D. in statistics from Stanford University in 1998.
Candès has received several awards including the Alan T. Waterman Award from NSF, which is the highest honor bestowed by the National Science Foundation, and which recognizes the achievements of early-career scientists. He has given over 60 plenary lectures at major international conferences, not only in mathematics and statistics but in many other areas as well including biomedical imaging and solid-state physics. He was elected to the National Academy of Sciences and to the American Academy of Arts and Sciences in 2014. -
Gunnar Carlsson
Ann and Bill Swindells Professor, Emeritus
BioDr. Carlsson has been a professor of mathematics at Stanford University since 1991. In the last ten years, he has been involved in adapting topological techniques to data analysis, under NSF funding and as the lead PI on the DARPA “Topological Data Analysis” project from 2005 to 2010. He is the lead organizer of the ATMCS conferences, and serves as an editor of several Mathematics journals
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Ines Chami
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2018
Masters Student in Computational and Mathematical Engineering, admitted Autumn 2016BioI am a second-year Masters student in the ICME data science program. Prior to joining Stanford, I studied mathematics and computer science at Ecole Centrale Paris. My research interests include computer vision, natural language processing and, more specifically, multimodal analysis. My previous research was focused on cross-modal information retrieval (image annotation and automated text-illustration). I am currently working on information extraction from semi-structured data (pdf tables) within the Hazy Research group led by Prof. Ré at Stanford.
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Casey Chu
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2016
Current Research and Scholarly InterestsTheoretical foundations of inference.
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Eric Darve
Professor of Mechanical Engineering
Current Research and Scholarly InterestsProfessor Darve's research is focused on the development of numerical methods for high-performance scientific computing, numerical linear algebra, fast algorithms, parallel computing, and machine learning with applications in engineering.
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David Donoho
Anne T. and Robert M. Bass Professor in the School of Humanities and Sciences
BioDavid Donoho is a mathematician who has made fundamental contributions to theoretical and computational statistics, as well as to signal processing and harmonic analysis. His algorithms have contributed significantly to our understanding of the maximum entropy principle, of the structure of robust procedures, and of sparse data description.
Research Statement:
My theoretical research interests have focused on the mathematics of statistical inference and on theoretical questions arising in applying harmonic analysis to various applied problems. My applied research interests have ranged from data visualization to various problems in scientific signal processing, image processing, and inverse problems. -
Ron Dror
Associate Professor of Computer Science and, by courtesy, of Molecular and Cellular Physiology and of Structural Biology
BioRon Dror is an Associate Professor of Computer Science and, by courtesy, Molecular and Cellular Physiology and Structural Biology at Stanford University, where he is also affiliated with the Institute for Computational and Mathematical Engineering, the Stanford Artificial Intelligence Lab, Bio-X, ChEM-H, and the Biophysics and Biomedical Informatics Programs. Dr. Dror's research at Stanford addresses a broad set of computational biology problems related to the spatial organization and dynamics of biomolecules and cells.
Before joining Stanford in March 2014, Dr. Dror served as second-in-command of D. E. Shaw Research, a hundred-person company, having joined in 2002 as its first hire. At DESRES, he focused on high-performance computing and biomolecular simulation—in particular, developing technology that accelerates molecular dynamics simulations by orders of magnitude, and applying these simulations to the study of protein function, protein folding, and protein-drug interactions (part of a project highlighted by Science as one of the top 10 scientific breakthroughs of 2010).
Dr. Dror earned a PhD in Electrical Engineering and Computer Science at MIT, an MPhil in Biological Sciences as a Churchill Scholar at the University of Cambridge, and both a BA in Mathematics and a BS in Electrical and Computer Engineering at Rice University, summa cum laude. As a student, he worked in genomics, vision, image analysis, and neuroscience. He has been awarded a Fulbright Scholarship and fellowships from the National Science Foundation, the Department of Defense, and the Whitaker Foundation, as well as a Gordon Bell Prize and several Best Paper awards. -
Eric Dunham
Associate Professor of Geophysics
Current Research and Scholarly InterestsPhysics of natural hazards, specifically earthquakes, tsunamis, and volcanoes. Computational geophysics.
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Philip Etter
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2017
BioI'm is a third year PhD student in the Institute for Computational and Mathematical Engineering at Stanford University. My interests lie broadly in the realm of data science and computational mathematics, spanning machine learning, numerical linear algebra, theoretical computer science, and computational physics. In particular, my most recent research focuses on model order reduction, leveraging machine learning and linear algebra techniques to deliver massive performance boosts in many-query physics problems, e.g., Bayesian inference and uncertainty quantification, while simultaneously guaranteeing accurate results. I presented these techniques in talks at SIAM: CSE ’19 and at ICIAM ’19, and I am currently preparing two different journal publications based on this work. In the past, I've also worked as a data science research intern at Sandia National Laboratories, a software engineering intern at Google, and a research contractor at Bell Labs.
I received my undergraduate degree from Princeton, where I studied mathematics, computer science, and physics. While I was there, I wrote my undergraduate thesis on numerical methods for solitonic boson star evolution and ground state searching, graduating summa cum laude. Before that, I did some research in theoretical optics. And before that, I was interested in graph algorithms. But while I have a very broad background in mathematics and related fields, I'm particularly excited by finding ways of using data to accelerate computation, build fast approximation techniques, and make predictions about the future (and inferences about the present).
Going forward, I want to continue to develop better and faster algorithms by bringing the power of data science to bear on interesting computational and statistical challenges.
My other assorted interests include quantum physics, general relativity, computer graphics, and music.
I prefer tabs to spaces, and vim to emacs. -
Charbel Farhat
Vivian Church Hoff Professor of Aircraft Structures, Professor of Mechanical Engineering and Director of the Army High Performance Computing Research Center
Current Research and Scholarly InterestsCharbel Farhat and his Research Group (FRG) develop mathematical models, advanced computational algorithms, and high-performance software for the design and analysis of complex systems in aerospace, marine, mechanical, and naval engineering. They contribute major advances to Simulation-Based Engineering Science. Current engineering foci in research are on the nonlinear aeroelasticity and flight dynamics of Micro Aerial Vehicles (MAVs) with flexible flapping wings and N+3 aircraft with High Aspect Ratio (HAR) wings, layout optimization and additive manufacturing of wing structures, supersonic inflatable aerodynamic decelerators for Mars landing, and the reliable automated carrier landing via model predictive control. Current theoretical and computational emphases in research are on high-performance, multi-scale modeling for the high-fidelity analysis of multi-physics problems, high-order embedded boundary methods, uncertainty quantification, probabilistic machine learning, and efficient projection-based model order reduction as well as other forms of physics-based machine learning for time-critical applications such as design, active control, and digital twins.
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Ron Fedkiw
Professor of Computer Science
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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Jordi Feliu Faba
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2016
BioI am a PhD student in the Institute for Computational and Mathematical Engineering (ICME). I was born and I received my education in Spain. I received my two Bachelor's degrees in Industrial Technology Engineering and in Civil Engineering at Universitat Politècnica de Catalunya (UPC) in Barcelona. In 2014 I moved for 6 months to France to finish my Bachelor's degree in Civil Engineering at Ecole Centrale de Nantes. Next, I returned to Barcelona to course a MSc in Civil Engineering at UPC and gain work experience in civil engineering. My research interests lie in the area of computational engineering.
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Casey Fleeter
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2015
BioI am a PhD student at Stanford University's Institute of Computational and Mathematical Engineering (ICME). I graduated from Harvard University in 2015 with a Bachelor of Arts in Physics. My research interests lie in the applications of mathematical methods to the cardiovascular system. My project in the Marsden Lab specifically utilizes techniques in uncertainty quantification.
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Oliver Fringer
Professor of Civil and Environmental Engineering
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
Senior Associate Dean for Educational Affairs, Professor of Energy Resources Engineering, Senior Fellow at the Precourt Institute for Energy and Professor, by courtesy, of Civil and Environmental Engineering
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 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.
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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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Abeynaya Gnanasekaran
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2016
BioI am a first year PhD student in the Institute for Computational and Mathematical engineering at Stanford University. My research interests broadly lie in Linear Algebra and optimization. I have a bachelors degree (with Honours) in Chemical engineering from Indian Institute of Technology Madras, India. My undergraduate research was in the area of Computational Microfluidics. Also I did a summer research internship in Process Control at EPFL, Switzerland.
I was born and brought up in Neyveli, an industrial town in south India. I enjoy listening to Indian music and reading novels. -
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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Yu Gu
Masters Student in Computational and Mathematical Engineering, admitted Autumn 2018
BioYu is a first year master's student in the Institute for Computational and Mathematical Engineering (ICME) at Stanford University. He received his bachelor's degree in Financial Engineering at Wuhan University, China. His research interests lie in quantitative finance, machine learning algorithms and stochastic modeling.
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Leonidas Guibas
Paul Pigott Professor in the School 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 USA Professor in the School of Engineering and Professor of Electrical Engineering
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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Ryan Alexander Humble
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2018
Current Research and Scholarly InterestsFast sparse linear system solvers,High-performance computing, Numerical methods for physical systems
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Gianluca Iaccarino
Professor of Mechanical Engineering and Director, Institute for Computational and Mathematical 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. -
Alexander Infanger
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2016
BioI am a second year PhD student at the Institute for Computational and Mathematical Engineering. I currently work on mean field models of (randomly) interacting agents with professor Peter Glynn.
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Arun Jambulapati
Ph.D. Student in Computational and Mathematical Engineering, admitted Spring 2016
Current Research and Scholarly InterestsI am interested in discrete mathematics and graph theory, especially in applications of combinatorics to Big Data.
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Doug James
Professor of Computer Science and, by courtesy, of Music
Current Research and Scholarly InterestsComputer graphics & animation, physics-based sound synthesis, computational physics, haptics, reduced-order modeling
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Antony Jameson
Professor (Research) of Aeronautics and Astronautics, Emeritus
BioProfessor Jameson's research focuses on the numerical solution of partial differential equations with applications to subsonic, transonic, and supersonic flow past complex configurations, as well as aerodynamic shape optimization.
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Ramesh Johari
Professor of Management Science and Engineering and, by courtesy, of Electrical Engineering
BioJohari is broadly interested in the design, economic analysis, and operation of online platforms, as well as statistical and machine learning techniques used by these platforms (such as search, recommendation, matching, and pricing algorithms).
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Indraneel Gireendra Kasmalkar
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2015
Current Research and Scholarly InterestsWe are currently limited in our understanding of what drives fast ice flow in Antarctica. I study water systems at the interface of the ice and the underlying bed and their coupled dynamics with ice and sediment. My goal is to understand how subglacial meltwater facilitates fast flow.
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Ramtin Keramati
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2015
Current Research and Scholarly InterestsReinforcement Learning, Deep Learning, Human in the Loop Reinforcement Learning
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Peter K. Kitanidis
Professor of Civil and Environmental Engineering
BioKitanidis develops methods for the solution of interpolation and inverse problems utilizing observations and mathematical models of flow and transport. He studies dilution and mixing of soluble substances in heterogeneous geologic formations, issues of scale in mass transport in heterogeneous porous media, and techniques to speed up the decay of pollutants in situ. He also develops methods for hydrologic forecasting and the optimization of sampling and control strategies.
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Allison Koenecke
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2016
BioI am a PhD candidate in the Institute for Computational and Mathematical Engineering (ICME). Prior to joining the Stanford community, I worked at NERA Economic Consulting in New York, where I specialized in data work with applications to antitrust litigation and mergers. I am originally from the DC area and received my Bachelor's in Mathematics with Computer Science from MIT. Previous internships include data science roles at Facebook, Google, and Microsoft.
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Matan Leibovich
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2015
Current Research and Scholarly InterestsI am interested in investigating and modeling PDEs in complex and stochastic environment for various applications such as imaging and wave propagation, and their interface with statistical learning and optimization.
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Sanjiva Lele
Professor of Aeronautics and Astronautics and of Mechanical Engineering
BioProfessor Lele's research combines numerical simulations with modeling to study fundamental unsteady flow phemonema, turbulence, flow instabilities, and flow-generated sound. Recent projects include shock-turbulent boundary layer interactions, supersonic jet noise, wind turbine aeroacoustics, wind farm modeling, aircraft contrails, multi-material mixing and multi-phase flows involving cavitation. He is also interested in developing high-fidelity computational methods for engineering applications.
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Adrian Lew
Associate Professor of Mechanical Engineering
BioProf. Lew's interests lie in the broad area of computational solid mechanics. He is concerned with the fundamental design and mathematical analysis of material models and numerical algorithms.
Currently the group is focused on the design of algorithms to simulate hydraulic fracturing. To this end we work on algorithms for time-integration embedded or immersed boundary methods.
