Institute for Computational and Mathematical Engineering (ICME)
Showing 101-150 of 163 Results
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Walter Murray
Professor (Research) of Management Science and Engineering, Emeritus
BioProfessor Murray's research interests include numerical optimization, numerical linear algebra, sparse matrix methods, optimization software and applications of optimization. He has authored two books (Practical Optimization and Optimization and Numerical Linear Algebra) and over eighty papers. In addition to his University work he has extensive consulting experience with industry, government, and commerce.
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Sanjiv Narayan
Professor of Medicine (Cardiovascular Medicine)
Current Research and Scholarly InterestsDr. Narayan directs the Computational Arrhythmia Research Laboratory, whose goal is to define the mechanisms underlying complex human heart rhythm disorders, to develop bioengineering-focused solutions to improve therapy that will be tested in clinical trials. The laboratory has been funded continuously since 2001 by the National Institutes of Health, AHA and ACC, and interlinks a disease-focused group of clinicians, computational physicists, bioengineers and trialists.
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Brad Osgood
Professor of Electrical Engineering and, by courtesy, in Education
BioOsgood is a mathematician by training and applies techniques from analysis and geometry to various engineering problems. He is interested in problems in imaging, pattern recognition, and signal processing.
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Julia Palacios
Associate Professor of Statistics and of Biomedical Data Science
BioDr. Palacios seek to provide statistically rigorous answers to concrete, data driven questions in evolutionary genetics and public health . My research involves probabilistic modeling of evolutionary forces and the development of computationally tractable methods that are applicable to big data problems. Past and current research relies heavily on the theory of stochastic processes, Bayesian nonparametrics and recent developments in machine learning and statistical theory for big data.
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Arogyaswami Paulraj
Professor (Research) of Electrical Engineering, Emeritus
BioProf. Arogyaswami Paulraj is an Emeritus Professor at Stanford University. Paulraj's legacy is deeply intertwined with the evolution of wireless communication. His groundbreaking work on MIMO (multiple input, multiple output) technology laid the foundation for today's ubiquitous 4G/5G networks and high-speed Wi-Fi.
Paulraj's journey began in the Indian Navy, where he served from 1965 to 1991. During this time, he led the development of the APSOH anti-submarine sonar system and established three key R&D labs for the Indian government. His contributions earned him the prestigious Padma Bhushan award, India's third highest civilian honor.
Following his naval career, Paulraj joined Stanford University as a postdoctoral researcher. His research focus shifted to wireless communication, where he made groundbreaking contributions to MIMO technology. MIMO enables data transmission using multiple antennas, significantly increasing network capacity and data rates.
Paulraj's innovation has been recognized with numerous accolades, including the 2024 Royal Acad. Eng. Prince Philip Medal, the 2023 IET Faraday Medal, the 2014 Marconi Prize, and the 2011 IEEE Alexander Graham Bell Medal. He is also a fellow of nine national academies in engineering, sciences, and the arts, and an inductee of the US Patent Office’s National Inventors Hall of Fame.
Currently, Paulraj continues to contribute to technological advancement. He chairs several committees for the Government of India, focusing on the Indian Semiconductor Mission and Core ICT initiatives. His dedication to research and development continues to shape the future of wireless communication. -
Marco Pavone
Associate Professor of Aeronautics and Astronautics, Senior Fellow at the Precourt Institute for Energy and Associate Professor, by courtesy, of Electrical Engineering & of Computer Science
BioDr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University, where he directs the Autonomous Systems Laboratory and the Center for Automotive Research at Stanford. He is also a Distinguished Research Scientist at NVIDIA where he leads autonomous vehicle research. Before joining Stanford, he was a Research Technologist within the Robotics Section at the NASA Jet Propulsion Laboratory. He received a Ph.D. degree in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His main research interests are in the development of methodologies for the analysis, design, and control of autonomous systems, with an emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He is a recipient of a number of awards, including a Presidential Early Career Award for Scientists and Engineers from President Barack Obama, an Office of Naval Research Young Investigator Award, a National Science Foundation Early Career (CAREER) Award, a NASA Early Career Faculty Award, and an Early-Career Spotlight Award from the Robotics Science and Systems Foundation. He was identified by the American Society for Engineering Education (ASEE) as one of America's 20 most highly promising investigators under the age of 40. His work has been recognized with best paper nominations or awards at a number of venues, including the European Conference on Computer Vision, the IEEE International Conference on Robotics and Automation, the European Control Conference, the IEEE International Conference on Intelligent Transportation Systems, the Field and Service Robotics Conference, the Robotics: Science and Systems Conference, and the INFORMS Annual Meeting.
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Markus Pelger
Associate Professor of Management Science and Engineering
Current Research and Scholarly InterestsHis research focuses on understanding and managing financial risk. He develops mathematical financial models and statistical methods, analyzes financial data and engineers computational techniques. His research is divided into three streams: machine learning solutions to big-data problems in empirical asset pricing, statistical theory for high-dimensional data and stochastic financial modeling.
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Peter Pinsky
Professor of Mechanical Engineering, Emeritus
BioPinsky works in the theory and practice of computational mechanics with a particular interest in multiphysics problems in biomechanics. His work uses the close coupling of techniques for molecular, statistical and continuum mechanics with biology, chemistry and clinical science. Areas of current interest include the mechanics of human vision (ocular mechanics) and the mechanics of hearing. Topics in the mechanics of vision include the mechanics of transparency, which investigates the mechanisms by which corneal tissue self-organizes at the molecular scale using collagen-proteoglycan-ion interactions to explain the mechanical resilience and almost perfect transparency of the tissue and to provide a theoretical framework for engineered corneal tissue replacement. At the macroscopic scale, advanced imaging data is used to create detailed models of the 3-D organization of collagen fibrils and the results used to predict outcomes of clinical techniques for improving vision as well as how diseased tissue mechanically degrades. Theories for mass transport and reaction are being developed to model metabolic processes and swelling in tissue. Current topics in the hearing research arena include multiscale modeling of hair-cell mechanics in the inner ear including physical mechanisms for the activation of mechanically-gated ion channels. Supporting research addresses the mechanics of lipid bilayer cell membranes and their interaction with the cytoskeleton. Recent past research topics include computational acoustics for exterior, multifrequency and inverse problems; and multiscale modeling of transdermal drug delivery. Professor Pinsky currently serves as Chair of the Mechanics and Computation Group within the Department of Mechanical Engineering at Stanford.
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Adrienne Propp
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2021
BioI am a fourth year PhD student in ICME (the Institute for Computational and Mathematical Engineering). Prior to Stanford, I was working as a technical analyst at the RAND Corporation where I spent most of my time designing microsimulations and other models to investigate topics in healthcare, education, disaster relief, and international relations.
My research interests lie at the intersection of mathematics, data, and modeling, which has led me to a focus on scientific machine learning (SciML). Specifically, I am working on developing new graph-based surrogate modeling methods for low-data regimes. I am grateful to be advised by Daniel Tartakovsky, During my PhD, I have also collaborated with Jenny Suckale to model volcanic lava fountaining, and Susan Athey and Sanath Kumar Krishnamurthy to design improved algorithms for contextual bandits.
Past research projects have ranged from computational models of the heart to inverse modeling to predict satellite performance. -
Ashwin Rao
Adjunct Professor, Institute for Computational and Mathematical Engineering (ICME)
BioMy current research and teaching is in Machine Learning (specifically RL) with applications to Financial Markets and Retail businesses. My academic origins are in Algorithms Theory and Abstract Algebra. More details on my background are here: https://www.linkedin.com/in/ashwin2rao/
My Stanford Home Page: https://stanford.edu/~ashlearn
CME 241 ("RL for Finance"), which I teach each Winter quarter: http://cme241.stanford.edu -
Noah Rosenberg
Stanford Professor of Population Genetics and Society
Current Research and Scholarly InterestsHuman evolutionary genetics, mathematical models in evolution and genetics, mathematical phylogenetics, statistical and computational genetics, theoretical population genetics
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Grant M. Rotskoff
Assistant Professor of Chemistry
BioGrant Rotskoff studies the nonequilibrium dynamics of living matter with a particular focus on self-organization from the molecular to the cellular scale. His work involves developing theoretical and computational tools that can probe and predict the properties of physical systems driven away from equilibrium. Recently, he has focused on characterizing and designing physically accurate machine learning techniques for biophysical modeling. Prior to his current position, Grant was a James S. McDonnell Fellow working at the Courant Institute of Mathematical Sciences at New York University. He completed his Ph.D. at the University of California, Berkeley in the Biophysics graduate group supported by an NSF Graduate Research Fellowship. His thesis, which was advised by Phillip Geissler and Gavin Crooks, developed theoretical tools for understanding nonequilibrium control of the small, fluctuating systems, such as those encountered in molecular biophysics. He also worked on coarsegrained models of the hydrophobic effect and self-assembly. Grant received an S.B. in Mathematics from the University of Chicago, where he became interested in biophysics as an undergraduate while working on free energy methods for large-scale molecular dynamics simulations.
Research Summary
My research focuses on theoretical and computational approaches to "mesoscale" biophysics. Many of the cellular phenomena that we consider the hallmarks of living systems occur at the scale of hundreds or thousands of proteins. Processes like the self-assembly of organelle-sized structures, the dynamics of cell division, and the transduction of signals from the environment to the machinery of the cell are not macroscopic phenomena—they are the result of a fluctuating, nonequilibrium dynamics. Experimentally probing mesoscale systems remains extremely difficult, though it is continuing to benefit from advances in cryo-electron microscopy and super-resolution imaging, among many other techniques. Predictive and explanatory models that resolve the essential physics at these intermediate scales have the power to both aid and enrich the understanding we are presently deriving from these experimental developments.
Major parts of my research include:
1. Dynamics of mesoscale biophysical assembly and response.— Biophysical processes involve chemical gradients and time-dependent external signals. These inherently nonequilibrium stimuli drive supermolecular organization within the cell. We develop models of active assembly processes and protein-membrane interactions as a foundation for the broad goal of characterizing the properties of nonequilibrium biomaterials.
2. Machine learning and dimensionality reduction for physical models.— Machine learning techniques are rapidly becoming a central statistical tool in all domains of scientific research. We apply machine learning techniques to sampling problems that arise in computational chemistry and develop approaches for systematically coarse-graining physical models. Recently, we have also been exploring reinforcement learning in the context of nonequilibrium control problems.
3. Methods for nonequilibrium simulation, optimization, and control.— We lack well-established theoretical frameworks for describing nonequilibrium states, even seemingly simple situations in which there are chemical or thermal gradients. Additionally, there are limited tools for predicting the response of nonequilibrium systems to external perturbations, even when the perturbations are small. Both of these problems pose key technical challenges for a theory of active biomaterials. We work on optimal control, nonequilibrium statistical mechanics, and simulation methodology, with a particular interest in developing techniques for importance sampling configurations from nonequilibrium ensembles. -
Amin Saberi
Professor of Management Science and Engineering and, by courtesy, of Computer Science
BioAmin Saberi is Professor of Management Science and Engineering at Stanford University. He received his B.Sc. from Sharif University of Technology and his Ph.D. from Georgia Institute of Technology in Computer Science. His research interests include algorithms, design and analysis of social networks, and applications. He is a recipient of the Terman Fellowship, Alfred Sloan Fellowship and several best paper awards.
Amin was the founding CEO and chairman of NovoEd Inc., a social learning environment designed in his research lab and used by universities such as Stanford as well as non-profit and for-profit institutions for offering courses to hundreds of thousands of learners around the world. -
Julia Salzman
Associate Professor of Biomedical Data Science, of Biochemistry and, by courtesy, of Statistics and of Biology
Current Research and Scholarly Interestsstatistical computational biology focusing on splicing, cancer and microbes
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Michael Saunders
Professor (Research) of Management Science and Engineering, Emeritus
BioSaunders develops mathematical methods for solving large-scale constrained optimization problems and large systems of equations. He also implements such methods as general-purpose software to allow their use in many areas of engineering, science, and business. He is co-developer of the large-scale optimizers MINOS, SNOPT, SQOPT, PDCO, the dense QP and NLP solvers LSSOL, QPOPT, NPSOL, and the linear equation solvers SYMMLQ, MINRES, MINRES-QLP, LSQR, LSMR, LSLQ, LNLQ, LSRN, LUSOL.
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Eric S.G. Shaqfeh
Lester Levi Carter Professor and Professor of Mechanical Engineering
Current Research and Scholarly InterestsI have over 25 years experience in theoretical and computational research related to complex fluids following my PhD in 1986. This includes work in suspension mechanics of rigid partlcles (rods), solution mechanics of polymers and most recently suspensions of vesicles, capsules and mixtures of these with rigid particles. My research group is internationally known for pioneering work in all these areas.
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Aaron Sidford
Associate Professor of Management Science and Engineering and of Computer Science
Current Research and Scholarly InterestsMy research interests lie broadly in the optimization, the theory of computation, and the design and analysis of algorithms. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures.
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Andrew Spakowitz
Senior Associate Dean for Research and Faculty Affairs, Professor of Chemical Engineering, of Materials Science and Engineering and, by courtesy, of Applied Physics
Current Research and Scholarly InterestsTheory and computation of biological processes and complex materials
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Adrien Specht
Ph.D. Student in Computational and Mathematical Engineering, admitted Spring 2024
BioI'm a PhD student in the Institute for Computational and Mathematical Engineering (ICME) at Stanford University, mentored by Prof. Mignot. My research is at the intersection of artificial intelligence and sleep medicine, focusing on developing predictive models for circadian rhythms and sleep debt from proteomics data. I adopt a problem-oriented approach, selecting methods based on the data and research questions at hand. My techniques range from linear regression to sophisticated deep learning frameworks, aiming to extract maximal insights from the data. I also explore the use of unsupervised and semi-supervised learning, and am interested in the applications of multimodal and foundation models in biology.
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Jenny Suckale
Associate Professor of Geophysics and, Senior Fellow, by courtesy, at the Woods Institute for the Environment
BioMy research group studies disasters to reduce the risk they pose. We approach this challenge by developing customized mathematical models that can be tested against observational data and are informed by community needs through a scientific co-production process. We intentionally work on extremes across different natural systems rather than focusing on one specific natural system to identify both commonalities in the physical processes driving extremes and in the best practices for mitigating risk at the community level. Our current research priorities include volcanic eruptions, ice-sheet instability, permafrost disintegration, induced seismicity and flood-risk mitigation. I was recently awarded the Presidential Early Career Awards for Scientists and Engineers, the highest honor bestowed by the United States Government on science and engineering professionals in the early stages of their independent research careers and the CAREER award from the National Science Foundation.
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Daniel Tartakovsky
Professor of Energy Science Engineering
Current Research and Scholarly InterestsEnvironmental fluid mechanics, Applied and computational mathematics, Biomedical modeling.
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Hamdi Tchelepi
Max Steineke Professor and Senior Fellow at the Precourt Institute for Energy
Current Research and Scholarly InterestsCurrent research activities: (1) model and simulate unstable miscible and immiscible fluid flow in heterogeneous porous media, (2) develop multiscale numerical solution algorithms for coupled mechanics and multiphase fluid flow in large-scale subsurface formations, and (3) develop stochastic solution methods that quantify the uncertainty associated with predictions of fluid-structure dynamics in porous media.
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Madeleine Udell
Assistant Professor of Management Science and Engineering and, by courtesy, of Electrical Engineering
Current Research and Scholarly InterestsProfessor Udell develops new techniques to accelerate and automate data science,
with a focus on large-scale optimization and on data preprocessing,
and with applications in medical informatics, engineering system design, and automated machine learning. -
Johan Ugander
Associate Professor of Management Science and Engineering
On Partial Leave from 10/01/2024 To 06/30/2025BioProfessor Ugander's research develops algorithmic and statistical frameworks for analyzing social networks, social systems, and other large-scale data-rich contexts. He is particularly interested in the challenges of causal inference and experimentation in these complex domains. His work commonly falls at the intersections of graph theory, machine learning, statistics, optimization, and algorithm design.
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Benjamin Van Roy
Professor of Electrical Engineering, of Management Science and Engineering and, by courtesy, of Computer Science
On Partial Leave from 10/01/2024 To 06/30/2025BioBenjamin Van Roy is a Professor at Stanford University, where he has served on the faculty since 1998. His current research focuses on reinforcement learning. Beyond academia, he leads a DeepMind Research team in Mountain View, and has also led research programs at Unica (acquired by IBM), Enuvis (acquired by SiRF), and Morgan Stanley.
He is a Fellow of INFORMS and IEEE and has served on the editorial boards of Machine Learning, Mathematics of Operations Research, for which he co-edited the Learning Theory Area, Operations Research, for which he edited the Financial Engineering Area, and the INFORMS Journal on Optimization. He received the SB in Computer Science and Engineering and the SM and PhD in Electrical Engineering and Computer Science, all from MIT, where his doctoral research was advised by John N. Tstitsiklis. He has been a recipient of the MIT George C. Newton Undergraduate Laboratory Project Award, the MIT Morris J. Levin Memorial Master's Thesis Award, the MIT George M. Sprowls Doctoral Dissertation Award, the National Science Foundation CAREER Award, the Stanford Tau Beta Pi Award for Excellence in Undergraduate Teaching, the Management Science and Engineering Department's Graduate Teaching Award, and the Lanchester Prize. He was the plenary speaker at the 2019 Allerton Conference on Communications, Control, and Computing. He has held visiting positions as the Wolfgang and Helga Gaul Visiting Professor at the University of Karlsruhe, the Chin Sophonpanich Foundation Professor and the InTouch Professor at Chulalongkorn University, a Visiting Professor at the National University of Singapore, and a Visiting Professor at the Chinese University of Hong Kong, Shenzhen. -
Andras Vasy
Robert Grimmett Professor of Mathematics
Current Research and Scholarly InterestsMy research concentrates on topics in two broad areas of applications of microlocal analysis in which, partly with collaborators, I introduced new ideas in recent years: non-elliptic linear and non-linear partial differential equations (PDE), typically concerning wave propagation or other related phenomena, and inverse problems for X-ray type transforms along geodesics and related problems for determining the metric tensor from boundary measurements.
