School of Engineering
Showing 2,301-2,350 of 6,464 Results
-
Kuo-Han Hung
Masters Student in Computer Science, admitted Autumn 2025
BioFirst-year Computer Science Master’s student at Stanford, originally from Taipei, Taiwan.
Passionate about robotics and AI, with a focus on developing robot policies that are more generalizable and reliable. Open to research collaborations and work opportunities in embodied AI and machine learning.
Website: https://khhung-906.github.io -
Sydney Hunt
Ph.D. Student in Electrical Engineering, admitted Autumn 2023
BioSydney Hunt (she/her), from Cornwall, New York, is a Knight-Hennessy Scholar pursuing a PhD in electrical engineering with a focus on brain-computer interfaces (BCI) at Stanford School of Engineering. She is advised by Paul Nuyujukian, MD, PhD in the Brain Interfacing Laboratory.
She currently serves as a Trustee on the Duke University Board of Trustees, Knight-Hennessy Scholar Ambassador, and on the Knight-Hennessy Scholar Experience Committee. She graduated with distinction from Duke University with bachelor’s degrees in electrical/computer engineering and computer science (concentration in artificial intelligence and machine learning), and a minor in gender, sexuality, and feminist studies.
An aspiring professor, Sydney passionately commits herself to STEM retention as a founding member of both the nonprofit CS Sidekicks and Duke’s S.P.I.R.E. Fellows Living Learning Community. She conducted and published her BCI research at Caltech (Richard Andersen’s lab) and MIT (Polina Anikeeva’s lab) through the WAVE Fellows and MIT SRP-Bio programs, respectively. She enjoys playing soccer, trying new food, and dad jokes. Sydney is certified in Mental Health First Aid and a recipient of Duke’s Reginaldo Howard Memorial Scholarship. -
Hillard Huntington
Executive Director, Energy Modeling Forum
Researcher, Management Science and Engineering - Energy Modeling Forum
Staff, Management Science and Engineering - Energy Modeling ForumBioHuntington is Executive Director of Stanford University's Energy Modeling Forum, where he conducts studies to improve the usefulness of models for understanding energy and environmental problems. In 2005 the Forum received the prestigious Adelman-Frankel Award from the International Association for Energy Economics for its "unique and innovative contribution to the field of energy economics."
His current research interests are modeling energy security, energy price shocks, energy market impacts of environmental policies, and international natural gas and LNG markets. In 2002 he won the Best Paper Award from the Energy Journal for a paper co-authored with Professor Dermot Gately of New York University.
He is a Senior Fellow and a past-President of the United States Association for Energy Economics and a member of the National Petroleum Council. He was also Vice-President for Publications for the International Association for Energy Economics and a member of the American Statistical Association's Committee on Energy Data. Previously, he served on a joint USA-Russian National Academy of Sciences Panel on energy conservation research and development.
Huntington has testified before the U.S. Senate Committee on Foreign Relations and the California Energy Commission.
Prior to coming to Stanford in 1980, he held positions in the corporate and government sectors with Data Resources Inc., the U.S. Federal Energy Administration, and the Public Utilities Authority in Monrovia, Liberia (as a U.S. Peace Corps Volunteer). -
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. -
Donald Iglehart
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
