Institute for Computational and Mathematical Engineering (ICME)

Showing 41-60 of 66 Results

  • Brad Osgood

    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.

  • Julia Palacios

    Julia Palacios

    Assistant Professor of Statistics, of Biomedical Data Science and, by courtesy, of Biology

    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.

  • Arogyaswami Paulraj

    Arogyaswami Paulraj

    Professor (Research) of Electrical Engineering, Emeritus

    BioProfessor Emeritus Arogyaswami Paulraj, Stanford University, is a pioneer of MIMO wireless communications, a technology break through that enables improved wireless performance. MIMO is now incorporated into all new wireless systems.

    Paulraj is the author of over 400 research papers, two textbooks, and a co-inventor in 80 US patents.

    Paulraj has won over a dozen awards, notably the National Inventors Hall of Fame (USPTO), Marconi Prize and Fellowship, 2014 and the IEEE Alexander Graham Bell Medal, 2011. He is a fellow of eight scientific / engineering national academies including the US, China, India, and Sweden. He is a fellow of IEEE and AAAS.

    In 1999, Paulraj founded Iospan Wireless Inc. - which developed and established MIMO-OFDMA wireless as the core 4G technology. Iospan was acquired by Intel Corporation in 2003. In 2004, he co-founded Beceem Communications Inc. The company became the market leader in 4G-WiMAX semiconductor and was acquired by Broadcom Corp. in 2010. In 2014 he founded Rasa Networks to develop Machine Learning tools for WiFi Networks. The company was acquired HPE in 2016.

    During his 30 years in the Indian (Navy) (1961-1991), he founded three national-level laboratories in India and headed one of India’s most successful military R&D projects – APSOH sonar. He received over a dozen awards (many at the national level) in India including the Padma Bhushan, Ati Vishist Seva Medal and the VASVIK Medal.

  • Marco Pavone

    Marco Pavone

    Associate Professor of Aeronautics and Astronautics and, by courtesy, of Electrical Engineering and of Computer Science
    On Partial Leave from 07/01/2022 To 02/21/2023

    BioDr. Marco Pavone is an Assistant Professor of Aeronautics and Astronautics at Stanford University, where he is the Director of the Autonomous Systems Laboratory and Co-Director of the Center for Automotive Research at Stanford. 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 several awards, including a Presidential Early Career Award for Scientists and Engineers from President Barack Obama, an ONR Young Investigator Award, an NSF CAREER Award, and a NASA Early Career Faculty Award. 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 the International Conference on Intelligent Transportation Systems, at the Field and Service Robotics Conference, at the Robotics: Science and Systems Conference, and at NASA symposia.

  • Peter Pinsky

    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.

  • Noah Rosenberg

    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

  • Grant M. Rotskoff

    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

    Amin Saberi

    Professor of Management Science and Engineering

    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.

  • Andreas Santucci

    Andreas Santucci


    Current Research and Scholarly InterestsI am interested in the intersection of Causal Inference and Machine Learning.

  • Michael Saunders

    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.

  • Eric S.G. Shaqfeh

    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.

  • Aaron Sidford

    Aaron Sidford

    Assistant 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.

  • Andrew Spakowitz

    Andrew Spakowitz

    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

  • Jenny Suckale

    Jenny Suckale

    Assistant Professor of Geophysics and, by courtesy, of Civil and Environmental Engineering and Center 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.

  • Hamdi Tchelepi

    Hamdi Tchelepi

    Professor of Energy Science Engineering

    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.

  • Johan Ugander

    Johan Ugander

    Associate Professor of Management Science and Engineering

    BioProfessor 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.