School of Engineering
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Director, Institute for Computational and Mathematical Engineering and Professor of Mechanical EngineeringOn Leave from 10/01/2022 To 06/30/2023
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.
I consider the classical boundaries between disciplines outdated and counterproductive in seeking innovative solutions to real-world problems.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.
Professor of Engineering-Economic Systems & Operations Research, Emeritus
BioDonald L. Iglehart is a John von Neumann Theory Prize recipient who has made fundamental contributions to performance analysis, optimization, and simulation of stochastic systems. Iglehart received his Bachelor’s degree in Engineering Physics from Cornell in 1956, his Master’s degree in Mathematical Statistics from Stanford University in 1959, and his PhD in the same subject from Stanford in 1961. His dissertation was supervised by Herbert E. Scarf and Samuel Karlin, and the topic was on dynamic programming and stationary analysis of inventory problems. He taught at Cornell University from 1961 to 1967 and came to Stanford in 1967, where he has been emeritus since 1999. In1976, he spent a very productive year as an Overseas Fellow at Churchill College at Cambridge University. In his capacity as a PhD advisor, he has had many notable students, including Peter Glynn, Peter Haas, Phil Heidelberger, Doug Kennedy, and Ward Whitt.
Iglehart was jointly awarded the John von Neumann Theory Prize in 2002 with Cyrus Derman, the same year he was named an inaugural Fellow of the Institute for Operations Research and the Management Sciences. He was recognized for having pioneered and developed diffusion limits and approximations for heavily congested stochastic systems. His ideas provided tractable limiting processes and readily computable approximations for complex queueing and other stochastic systems for which closed-form solutions have proved intractable. Iglehart’s original research and contributions have heavily influenced queueing theory in the years since their publication, and his papers have been cited in hundreds of publications. Some of his other work has focused on inventory and distribution problems.
Iglehart was also honored by the INFORMS Simulation Society in 2012 with its highest honor, the Lifetime Professional Achievement Award (LPAA). His foundational work in that field recognized and exploited the underlying stochastic structure of simulation as a means of producing enhanced simulation methodologies. For example, he introduced and led the development of the regenerative method for stochastic simulation output analysis, inspiring a flood of significant contributions to simulation methodology. In the late 1980s, Iglehart and Glynn incorporated such techniques as importance sampling into stochastic simulations. The LPAA also noted his ability to clearly organize and articulate deep theory in his presentations and writing, and recognized his education of Ph.D. students who have had, individually and cumulatively, a profound impact on simulation education and research. The citation for his award states that "It is no exaggeration to say that Don Iglehart’s contributions made simulation a respectable research discipline in some circles of the operations research community."
In addition to being an INFORMS Fellow, Iglehart was elected in 1999 to the National Academy of Engineering, having been selected for his contributions to queueing theory, simulation methodology, inventory control, and diffusion approximations. He was also honored in 1971 through his induction as a Fellow of the Institute of Mathematical Statistics.
Historical Academic Appointments:
1961-67 School of Operations Research and Industrial Engineering, Cornell University
1967-96 Department of Operations Research, Stanford University
1996-99 Department of Engineering-Economic Systems and Operations Research, Stanford University
Professor of Mechanical Engineering and of Photon Science
BioLarge-eddy simulation and modeling of turbulent reacting flows, non-premixed flame, aeroacoustics and combustion generated noise, turbulence and fluid dynamics, numerical methods and high-order schemes.
Soh Young In
Current Research and Scholarly InterestsMy research encompasses engineering, economics and public policy. It focuses on clean energy finance and entrepreneurship. My current research projects (1) investigate clean investment performance in the capital market; (2) analyze networks between investors and entrepreneurs; and (3) aim to create an innovative investment vehicle for clean technology startups. My ultimate aim is to catalyze private capital in clean energy so that the world can transition more rapidly to a low-carbon economy.
Professor of Electrical Engineering, Emeritus
BioThrough measurements in space and at multiple remote sites in Antarctica, Alaska, and the continental United States, Professor Inan studies the Earth's ionosphere and upper atmosphere. Of particular interest are ionospheric effects of lightning discharges and the recently discovered phenomena of electrical discharges and luminous glows at high altitudes above thunderstorms. He also studies physical processes in the Earth's near-space environment, including space weather effects on navigation and communication signals, electrodynamic coupling of the ionosphere to the magnetosphere, wave-induced precipitation of particles out of the radiation belts, and cyclotron resonant interactions between electromagnetic waves and energetic electrons. He is also involved in the development of ultra-low-power and miniaturized radio receivers for use in remote polar regions and on micro-satellites.
Adjunct Professor, Institute for Computational and Mathematical Engineering (ICME)
Postdoctoral Scholar, Biomedical Data Sciences
BioDr. Alexander Ioannidis (Ph.D., M.Phil) earned his Ph.D. from Stanford University in Computational and Mathematical Engineering, where he teaches machine learning and data science as an Adjunct Professor in the School of Engineering. He also has an M.S. in Management Science and Engineering (Optimization) from Stanford. Prior to Stanford, he worked in superconducting computing logic and quantum computing at Northrop Grumman. He graduated summa cum laude from Harvard University in Chemistry and Physics and earned an M.Phil from the Department of Applied Math and Theoretical Physics in Computational Biology, and Diploma in Greek, from the University of Cambridge. As a current research fellow in the Stanford School of Medicine, Department of Biomedical Data Science his work focuses on the design of algorithms and application of computational methods for problems in genomics, clinical data science, and precision health with a particular focus on underrepresented populations in Oceania and Latin America.