Institute for Computational and Mathematical Engineering (ICME)
Showing 61-80 of 168 Results
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Gianluca Iaccarino
Robert Bosch Chair of the Department of Mechanical Engineering and Joseph L. and Roberta M. Rodgers Professor
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I consider the classical boundaries between disciplines outdated and counterproductive in seeking innovative solutions to real-world problems.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 Ioannidis
Assistant Professor (Research) of Genetics and of Biomedical Data Science
Adjunct Professor, Institute for Computational and Mathematical Engineering (ICME)BioDr. Ioannidis earned his Ph.D. from Stanford University in Computational and Mathematical Engineering together with an M.S. in Management Science and Engineering (Optimization). He graduated summa cum laude from Harvard University in Chemistry and Physics and earned an M.Phil at the University of Cambridge from the Department of Applied Math and Theoretical Physics in Computational Biology. His research focuses on the design of algorithms and application of computational methods for problems in precision health, genomics, clinical data science, and AI in healthcare.
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Doug James
LeRa Professor and Professor, 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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Jikai Jin
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2023
BioI am currently a Ph.D. student of the The Institute for Computational and Mathematical Engineering (ICME) at Stanford university. Prior to joining Stanford, I obtained my bachelor degree in computational mathematics at the School of Mathematical Sciences, Peking University, fortunately having Prof. Liwei Wang as my research advisor. My research is highly interdisciplinary across machine learning, statistics, operations research. While primarily focusing on theoretical aspects, the ultimate goal of my research is to develop state-of-the-art solutions for important real-world problems.
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Ramesh Johari
Professor of Management Science and Engineering, Senior Fellow at the Stanford Institute for Human-Centered Artificial Intelligence (HAI) 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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Riley Juenemann
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2021
BioThird-year Computational and Mathematical Engineering (ICME) PhD Candidate @ Stanford University passionate about research at the intersection of mathematics, computing, and biology.
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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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Ellen Kuhl
Catherine Holman Johnson Director of Stanford Bio-X, Walter B Reinhold Professor in the School of Engineering, Professor of Mechanical Engineering and, by courtesy, of Bioengineering
Current Research and Scholarly Interestscomputaitonal simulation of brain development, cortical folding, computational simulation of cardiac disease, heart failure, left ventricular remodeling, electrophysiology, excitation-contraction coupling, computer-guided surgical planning, patient-specific simulation
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Ching-Yao Lai
Assistant Professor of Geophysics
BioMy group attacks fundamental questions in fluid dynamics and geophysics by integrating mathematical and machine-learned models with observational data. We use our findings to address challenges facing the world, such as advancing our scientific knowledge of ice dynamics under climate change. The length scale of the systems we are interested in varies broadly from a few microns to thousands of kilometers, because the governing physical principles are often universal across a range of length and time scales. We use mathematical models, simulations, and machine learning to study the complex interactions between fluids and elasticity and their interfacial dynamics, such as multiphase flows, flows in deformable structures, and cracks. We extend our findings to tackle emerging topics in climate science and geophysics, such as understand the missing physics that governs the flow of ice sheets in a warming climate. We welcome collaborations across disciplinary lines, from geophysics, engineering, physics, applied math to computer science, since we believe combining expertise and methodologies across fields is crucial for new discoveries.
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Sanjay Lall
Professor of Electrical Engineering
BioSanjay Lall is Professor of Electrical Engineering in the Information Systems Laboratory and Professor of Aeronautics and Astronautics at Stanford University. He received a B.A. degree in Mathematics with first-class honors in 1990 and a Ph.D. degree in Engineering in 1995, both from the University of Cambridge, England. His research group focuses on algorithms for control, optimization, and machine learning. Before joining Stanford he was a Research Fellow at the California Institute of Technology in the Department of Control and Dynamical Systems, and prior to that he was a NATO Research Fellow at Massachusetts Institute of Technology, in the Laboratory for Information and Decision Systems. He was also a visiting scholar at Lund Institute of Technology in the Department of Automatic Control. He has significant industrial experience applying advanced algorithms to problems including satellite systems, advanced audio systems, Formula 1 racing, the America's cup, cloud services monitoring, and integrated circuit diagnostic systems, in addition to several startup companies. Professor Lall has served as Associate Editor for the journal Automatica, on the steering and program committees of several international conferences, and as a reviewer for the National Science Foundation, DARPA, and the Air Force Office of Scientific Research. He is the author of over 130 peer-refereed publications.
