Stanford University
Showing 51-100 of 342 Results
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Haoxuan Chen
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2022
BioPersonal website: https://haoxuanstevec00.github.io/
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Srabanti Chowdhury
Associate Professor of Electrical Engineering, Senior Fellow at the Precourt Institute for Energy and Associate Professor, by courtesy, of Materials Science and Engineering
Current Research and Scholarly InterestsWide bandap materials & devices for RF, Power and energy efficient electronics
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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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Isabella Crouch
Masters Student in Computational and Mathematical Engineering, admitted Autumn 2023
Current Research and Scholarly InterestsComputational materials science, material informatics, scientific computing
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Marta D'Elia
Adjunct Professor, Institute for Computational and Mathematical Engineering (ICME)
BioI’m a research/computational scientist working on the design and analysis of models and data-driven algorithms for the simulation of complex, multiscale and multiphysics problems. My background and training have foundations in Numerical Analysis, Scientific Computing, Inverse Problems, Control and Optimization, and Uncertainty Quantification. In the past five years I have focused on Scientific Machine Learning (SciML) and Deep Learning. I am an expert in Nonlocal/Fractional Modeling and Simulation (10 years) with application to Continuum Mechanics, Subsurface Transport, Image Processing, and Turbulence. I have a master's degree in Mathematical Engineering from Politecnico di Milano (2007) and a PhD in Applied Mathematics from Emory University (2011).
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Eric Darve
Professor of Mechanical Engineering
Current Research and Scholarly InterestsThe research interests of Professor Darve span across several domains, including machine learning for engineering, surrogate and reduced order modeling, stochastic inversion, anomaly detection for engineering processes and manufacturing, numerical linear algebra, high-performance and parallel computing, and GPGPU.
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Stefan P. Domino
Adjunct Professor, Institute for Computational and Mathematical Engineering (ICME)
BioDr. Domino’s research interest rests within low-Mach fluid mechanics methods development for complex systems that drive the coupling of mass, momentum, species and energy transport. His core research resides within the intersection of physics elucidation, numerical methods research, V&V techniques exploration, and high performance computing and coding methods for turbulent flow applications. Stefan also supports the teaching of ME469, Computational Methods in Fluid Mechanics, while continuing his primary career at Sandia National Laboratories as a Distinguished Member of the Technical Staff.
Education:
University of Utah
Ph.D. Department of Chemical Engineering, 1999
"Methods towards improved simulations for the oxides of nitrogen in pulverized-coal furnaces"
Professor Philip J. Smith, Advisor
Select Recent Publications:
* Domino, S. P., "On the subject of large-scale pool fires and turbulent boundary layer interactions", Phys. Fluids, 2024. (Featured)
* Domino, S. P., Wenzel, E. A, "A direct numerical simulation study for confined non-isothermal jet impingement at moderate nozzle-to-plate distances: capturing jet-to-ambient density effects", Int. J. Heat Mass Trans, 2023.
* Benjamin, M., Domino, S. P., Iaccarino, G., "Neural networks for large eddy simulations of wall-bounded turbulence: numerical experiments and challenges", Eur. Phys. J. E., 2023.
* Hubbard, J., Cheng, M., Domino, S. P., "Mixing in low-Reynolds number reacting impinging jets in crossflow", J. Fluids Engr., 2023.
* Domino, S. P. “Unstructured finite volume approaches for turbulence,” in Numerical Methods in Turbulence Simulation, edited by R. Moser (Elsevier, 2023), Ch. 7, pp. 285–317.
* Scott, S., Domino, S. P., "A computational examination of large-scale pool fires: variations in crosswind velocity and pool shape", Flow, 2022.
* Domino, S. P., Horne, W., "Development and deployment of a credible unstructured, six-DOF, implicit low-Mach overset simulation tool for wave energy applications", Renew. Energy, 2022.
* Hubbard, J., Hansen, M., Kirsch, J., Hewson, J., Domino, S. P., “Medium scale methanol pool fire model validation”, J. Heat Transfer, 2022.
* Barone, M., Ray, J., Domino, S. P., "Feature selection, clustering, and prototype placement for turbulence datasets", AIAA J., 2021,
* Domino, S. P., Hewson, J., Knaus, R., Hansen, M., "Predicting large-scale pool fire dynamics using an unsteady flamelet- and large-eddy simulation-based model suite", Phys. Fluids, 2021. (Editor's pick)
* Domino, S. P., "A case study on pathogen transport, deposition, evaporation and transmission: linking high-fidelity computational fluid dynamics simulations to probability of infection", Int. J. CFD, 2021.
* Domino, S. P., Pierce, F., Hubbard, J., "A multi-physics computational investigation of droplet pathogen transport emanating from synthetic coughs and breathing", Atom. Sprays, 2021.
* Jofre, L., Domino, S. P., Iaacarino, G., "Eigensensitivity analysis of subgrid-scale stresses in large-eddy simulation of a turbulent axisymmetric jet", Int. J. Heat Fluid Flow, 2019.
* Domino, S. P., Sakievich, P., Barone, M., "An assessment of atypical mesh topologies for low-Mach large-eddy simulation", Comp. Fluids, 2019.
* Domino, S. P., "Design-order, non-conformal low-Mach fluid algorithms using a hybrid CVFEM/DG approach ", J. Comput. Physics, 2018.
* Jofre, L., Domino, S. P., Iaacarino, G., "A Framework for Characterizing Structural Uncertainty in Large-Eddy Simulation Closures", Flow Turb. Combust., 2018.
CV: https://github.com/spdomin/Present/blob/master/cv/dominoCV.pdf -
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
On Partial Leave from 04/01/2024 To 06/30/2024Current 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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Humera Fasihuddin
d.school Systems Architect, d.school
BioHumera co-directs the University Innovation Fellows Program. She trains students to create lasting institutional impact that enhances the innovation and entrepreneurship ecosystem on campus.
Prior to the University Innovation Fellows program, she worked for nonprofit VentureWell and led the creation of numerous programs including the organization’s first foray in advanced venture training workshops, which today account for over half of the 501c(3)’s income. Before that, she created innovation networks between industry and the University of Massachusetts Amherst under an NSF Partnership for Innovation grant.
Humera began her career at the publicly-traded UK firm Rexam, serving as product manager in their precision coated materials subsidiary. Humera holds an M.B.A. from UMass Amherst and a B.S. from Smith College. -
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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Chris Flink
Adjunct Professor, d.school
BioChris Flink is an Adjunct Professor and a versatile leader with experience spanning top creative, educational and cultural institutions. He's a dynamic executive who consistently marries imagination with strategic rigor, brings the best out of interdisciplinary teams, and fosters inclusive, human-centered organizational cultures. He is the former CEO and Executive Director of the Exploratorium (2016-22), senior partner at IDEO (1997-2016), and Fortune 500 corporate board member. At Stanford, he was reappointed as an Adjunct Professor in 2023 to again support strategic leadership of the "d.school" and contribute to its courses, programs and projects. Professor Flink was a founding faculty member of the d.school (Hasso Plattner Institute of Design) and key part of its early leadership team. He was previously appointed as a Consulting Associate Professor in Engineering (1999-2017), a Lecturer in Marketing at the Graduate School of Business (2011-16), and a faculty Resident Fellow (2013-17). Courses taught include: "Advanced Product Design" (ME 216B), "Human Values in Design" (ME 313 with Professor David Kelley), "Brands, Experience & Social Technology" (MKTG 353), "Designing Empathy-based Organizations" (GSBGEN 555), "Social Brands" and "Building Innovative Brands" (MKTG 541 & 552 with Professor Jennifer Aaker). He served as the faculty Resident Fellow for a vibrant innovation-themed undergraduate dorm of more than 130 upperclass students (each year) as they built community and fueled their creative confidence. Professor Flink has also delivered popular guest lectures at Wharton and Columbia business schools, and presented at TEDx as well as the World Economic Forum in Davos, Switzerland. His adventures with Stanford began as an enthusiastic student, earning his BS in Engineering/Product Design in 1994 and his MS in Management from the Graduate School of Business in 2005.
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Emily Fox
Professor of Statistics and of Computer Science
BioEmily Fox is a Professor in the Departments of Statistics and Computer Science at Stanford University. Prior to Stanford, she was the Amazon Professor of Machine Learning in the Paul G. Allen School of Computer Science & Engineering and Department of Statistics at the University of Washington. From 2018-2021, Emily led the Health AI team at Apple, where she was a Distinguished Engineer. Before joining UW, Emily was an Assistant Professor at the Wharton School Department of Statistics at the University of Pennsylvania. She earned her doctorate from Electrical Engineering and Computer Science (EECS) at MIT where her thesis was recognized with EECS' Jin-Au Kong Outstanding Doctoral Thesis Prize and the Leonard J. Savage Award for Best Thesis in Applied Methodology.
Emily has been awarded a CZ Biohub Investigator Award, Presidential Early Career Award for Scientists and Engineers (PECASE), a Sloan Research Fellowship, ONR Young Investigator Award, and NSF CAREER Award. Her research interests are in modeling complex time series arising in health, particularly from health wearables and neuroimaging modalities. -
Oliver Fringer
Professor of Civil and Environmental Engineering and of Oceans
BioFringer's research focuses on the development and application of numerical models and high-performance computational techniques to the study of fundamental processes that influence the dynamics of the coastal ocean, rivers, lakes, and estuaries.
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Margot Gerritsen
Professor of Energy Resources Engineering, Emerita
Current Research and Scholarly InterestsResearch
My work is about understanding and simulating complicated fluid flow problems. My research focuses on the design of highly accurate and efficient parallel computational methods to predict the performance of enhanced oil recovery methods. I'm particularly interested in gas injection and in-situ combustion processes. These recovery methods are extremely challenging to simulate because of the very strong nonlinearities in the governing equations. Outside petroleum engineering, I'm active in coastal ocean simulation with colleagues from the Department of Civil and Environmental Engineering, yacht research and pterosaur flight mechanics with colleagues from the Department of Mechanical and Aeronautical Engineering, and the design of search algorithms in collaboration with the Library of Congress and colleagues from the Institute of Computational and Mathematical Engineering.
Teaching
I teach courses in both energy related topics (reservoir simulation, energy, and the environment) in my department, and mathematics for engineers through the Institute of Computational and Mathematical Engineering (ICME). I also initiated two courses in professional development in our department (presentation skills and teaching assistant training), and a consulting course for graduate students in ICME, which offers expertise in computational methods to the Stanford community and selected industries.
Professional Activities
Senior Associate Dean, School of Earth, Energy and Environmental Sciences, Stanford (from 2015); Director, Institute for Computational and Mathematical Engineering, Stanford (from 2010); Stanford Fellow (2010-2012); Magne Espedal Professor II, Bergen University (2011-2014); Aldo Leopold Fellow (2009); Chair, SIAM Activity group in Geosciences (2007, present, reelected in 2009); Faculty Research Fellow, Clayman Institute (2008); Elected to Council of Society of Industrial and Applied Mathematics (SIAM) (2007); organizing committee, 2008 Gordon Conference on Flow in Porous Media; producer, Smart Energy podcast channel; Director, Stanford Yacht Research; Co-director and founder, Stanford Center of Excellence for Computational Algorithms in Digital Stewardship; Editor, Journal of Small Craft Technology; Associate editor, Transport in Porous Media; Reviewer for various journals and organizations including SPE, DoE, NSF, Journal of Computational Physics, Journal of Scientific Computing, Transport in Porous Media, Computational Geosciences; member, SIAM, SPE, KIVI, AGU, and APS -
Kay Giesecke
Professor of Management Science and Engineering
Current Research and Scholarly InterestsKay is a financial technologist whose research agenda is driven by significant applied problems in areas such as investment management, risk analytics, lending, and regulation, where data streams are increasingly large-scale and dynamical, and where computational demands are critical. He develops and analyzes statistical machine learning methods to make explainable data-driven decisions in these and other areas and efficient numerical algorithms to address the associated computational issues.
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Julia Gillespie
Director of Finance and Operations, Institute for Computational and Mathematical Engineering (ICME)
Current Role at StanfordI am the Director of Finance and Operations for the Institute for Computational Mathematics and Engineering within the School of Engineering.
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Peter Glynn
Thomas W. Ford Professor in the School of Engineering and Professor, by courtesy, of Electrical Engineering
Current Research and Scholarly InterestsStochastic modeling; statistics; simulation; finance
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Ashish Goel
Professor of Management Science and Engineering and, by courtesy, of Computer Science
BioAshish Goel is a Professor of Management Science and Engineering and (by courtesy) Computer Science at Stanford University. He received his PhD in Computer Science from Stanford in 1999, and was an Assistant Professor of Computer Science at the University of Southern California from 1999 to 2002. His research interests lie in the design, analysis, and applications of algorithms.
