School of Engineering
Showing 21-40 of 151 Results
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John M. Cioffi
Hitachi America Professor in the School of Engineering, Emeritus
BioJohn M. Cioffi teaches Stanford's graduate electrical engineering course sequence in digital communications, part-time as recalled emeritus presently, from 1986 to the present. Cioffi's research interests are in the theory of transmitting the highest possible data rates on a number of different communications channels, many of which efforts spun out of Stanford through he and/or his many former PhD students to companies, most notably including the basic designed globally used 500 million DSL connections. Cioffi also oversaw the prototype developments for the worlds first cable modem and digital-audio broadcast systems. Cioffi pioneering the use of remote management algorithms to improve (over the internet or cloud) both wireline (DSL) and wireless (Wi-Fi) physical-layer transmission performance, an area often known as Dynamic Spectrum Management or Dynamic Line Management. Cioffi is co-inventor on basic patents for vectored DSL transmission and optimized MIMO wireless transmission. In his early career, Cioffi developed the worlds first full-duplex voiceband data modem while at Bell Laboratories, and the worlds first adaptively equalized disk read channel while at IBM. His courses and research projects over the years center on the area of multiuser transmission methods.
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Daniel Norbert Congreve
Assistant Professor of Electrical Engineering
BioDan is an Assistant Professor in the Department of Electrical Engineering at Stanford University. Prior to Stanford, Dan received his B.S. and M.S. from Iowa State in 2011, working with Vik Dalal studying defect densities of nano-crystalline and amorphous silicon. He then received his PhD from MIT in Electrical Engineering in 2015, studying under Marc Baldo. His thesis work focused on photonic energy conversion using singlet fission and triplet fusion as downconverting and upconverting processes, respectively. He spent a year as a postdoc with Will Tisdale in Chemical Engineering at MIT studying perovskite nanoplatelets. He joined the Rowland Institute in 2016 as a Rowland Fellow before starting at Stanford in 2020. Dan is a Moore Inventor Fellow, Sloan Research Fellow, Intel Rising Star, and co-founder of Quadratic3D, a startup looking to commercialize 3D printing technologies. His current research interests focus on engineering nanomaterials to solve challenging problems.
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Eric Darve
Director, Institute for Computational and Mathematical Engineering (ICME) and Professor of Mechanical Engineering
Current Research and Scholarly InterestsThe research interests of Professor Darve span across several domains, including machine learning for science and engineering, large-language models, transformer models, surrogate and reduced order modeling, stochastic inversing, anomaly detection, numerical linear algebra, high-performance, parallel, and GPU computing.
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Reinhold Dauskardt
Ruth G. and William K. Bowes Professor in the School of Engineering
BioDauskardt and his group have worked extensively on integrating new materials into emerging technologies including thin-film structures for nanoscience and energy technologies, high-performance composite and laminates for aerospace, and on biomaterials and soft tissues in bioengineering. His group has pioneered methods for characterizing adhesion and cohesion of thin films used extensively in device technologies. His research on wound healing has concentrated on establishing a biomechanics framework to quantify the mechanical stresses and biologic responses in healing wounds and define how the mechanical environment affects scar formation. Experimental studies are complimented with a range of multiscale computational capabilities. His research includes interaction with researchers nationally and internationally in academia, industry, and clinical practice.
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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
Cheriton Family Professor and Professor, by courtesy, of Structural Biology and of Molecular & Cellular Physiology
Current Research and Scholarly InterestsMy lab’s research focuses on computational biology, with an emphasis on 3D molecular structure. We combine two approaches: (1) Bottom-up: given the basic physics governing atomic interactions, use simulations to predict molecular behavior; (2) Top-down: given experimental data, use machine learning to predict molecular structures and properties. We collaborate closely with experimentalists and apply our methods to the discovery of safer, more effective drugs.
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Eric Dunham
Professor of Geophysics
Current Research and Scholarly InterestsPhysics of natural hazards, specifically earthquakes, tsunamis, and volcanoes. Computational geophysics.
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Jonathan Fan
Associate Professor of Electrical Engineering
Current Research and Scholarly InterestsOptical engineering plays a major role in imaging, communications, energy harvesting, and quantum technologies. We are exploring the next frontier of optical engineering on three fronts. The first is new materials development in the growth of crystalline plasmonic materials and assembly of nanomaterials. The second is novel methods for nanofabrication. The third is new inverse design concepts based on optimization and machine learning.
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Charbel Farhat
Vivian Church Hoff Professor of Aircraft Structures and Professor of Aeronautics and Astronautics
Current Research and Scholarly InterestsCharbel Farhat and his Research Group (FRG) develop mathematical models, advanced computational algorithms, and high-performance software for the design, analysis, and digital twinning 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 reliable autonomous carrier landing in rough seas; dissipation of vertical landing energies through structural flexibility; nonlinear aeroelasticity of N+3 aircraft with High Aspect Ratio (HAR) wings; pulsation and flutter of a parachute; pendulum motion in main parachute clusters; coupled fluid-structure interaction (FSI) in supersonic inflatable aerodynamic decelerators for Mars landing; flight dynamics of hypersonic systems and their trajectories; and advanced digital twinning. Current theoretical and computational emphases in research are on high-performance, multi-scale modeling for the high-fidelity analysis of multi-component, multi-physics problems; discrete-event-free embedded boundary methods for CFD and FSI; efficient Bayesian optimization using physics-based surrogate models; modeling and quantifying model-form uncertainty; probabilistic, physics-based machine learning; mechanics-informed artificial neural networks for data-driven constitutive modeling; and efficient nonlinear projection-based model order reduction for time-critical applications such as design, active control, and digital twinning.
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Kayvon Fatahalian
Associate Professor of Computer Science
BioKayvon Fatahalian is an Associate Professor in the Computer Science Department at Stanford University. Kayvon's research focuses on the design of systems for real-time graphics, high-efficiency simulation engines for applications in entertainment and AI, and platforms for the analysis of images and videos at scale.
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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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Grace Gao
Associate Professor of Aeronautics and Astronautics and, by courtesy, of Electrical Engineering
BioGrace Gao is an associate professor in the Department of Aeronautics and Astronautics at Stanford University, with a courtesy appointment in the Electrical Engineering Department. She leads the Navigation and Autonomous Vehicles Laboratory (NAV Lab) and serves as co-director of the Stanford AI Safety Center and co-lead of the Stanford SystemX Robotics area. Her research focuses on robust and secure perception, localization, and navigation, with applications in crewed and uncrewed aerial vehicles, autonomous cars, humanoid robots, and space robotics.
Prof. Gao has won numerous awards, including the NSF CAREER Award, the Institute of Navigation Early Achievement Award, the RTCA William E. Jackson Award, and the Inspiring Early Academic Career Award from Stanford University. In addition to her research achievements, she has received significant recognition for her teaching and advising, including the AIAA Stanford Chapter Excellence in Advising Award and the Excellence in Teaching Award. -
Xiaojing Gao
Assistant Professor of Chemical Engineering
Current Research and Scholarly InterestsHow do we design biological systems as “smart medicine” that sense patients’ states, process the information, and respond accordingly? To realize this vision, we will tackle fundamental challenges across different levels of complexity, such as (1) protein components that minimize their crosstalk with human cells and immunogenicity, (2) biomolecular circuits that function robustly in different cells and are easy to deliver, (3) multicellular consortia that communicate through scalable channels, and (4) therapeutic modules that interface with physiological inputs/outputs. Our engineering targets include biomolecules, molecular circuits, viruses, and cells, and our approach combines quantitative experimental analysis with computational simulation. The molecular tools we build will be applied to diverse fields such as neurobiology and cancer therapy.
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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.
