School of Engineering


Showing 151-160 of 160 Results

  • Bruce A. Wooley

    Bruce A. Wooley

    The Robert L. and Audrey S. Hancock Professor in the School of Engineering, Emeritus

    BioBruce Wooley is the Robert L. and Audrey S. Hancock Professor of Engineering, Emeritus, in the Department of Electrical Engineering at Stanford University. He received a Ph.D. degree in Electrical Engineering from the University of California, Berkeley in 1970, and from 1970 to 1984 he was a member of the research staff at Bell Laboratories in Holmdel, NJ. He joined the faculty at Stanford in 1984. At Stanford he has served as the Chair of the Department of Electrical Engineering, the Senior Associate Dean of Engineering and the Director of the Integrated Circuits Laboratory. His research is in the field of integrated circuit design, where his interests include low-power mixed-signal circuit design, oversampling analog-to-digital and digital-to-analog conversion, circuit design techniques for video and image data acquisition, high-speed embedded memory, high-performance packaging and testing, and circuits for wireless and wireline communications.
    Prof. Wooley is a Fellow of the IEEE and a past President of the IEEE Solid-State Circuits Society. He has served as the Editor of the IEEE Journal of Solid-State Circuits and as the Chairman of both the International Solid-State Circuits Conference (ISSCC) and the Symposium on VLSI Circuits. Awards he has received include the University Medal from the University of California, Berkeley, the IEEE Journal of Solid-State Circuits Best Paper Award, the Outstanding Alumnus Award from the EECS Department at the University of California, Berkeley, and the IEEE Donald O. Pederson Award in Solid-State Circuits.

  • Lei Xing

    Lei Xing

    Jacob Haimson Professor and Professor, by courtesy, of Electrical Engineering

    Current Research and Scholarly Interestsartificial intelligence in medicine, Image-guided intervention, molecular imaging, biologically conformable radiation threapy (BCRT), treatment plan optimization, optimization, application of molecular imaging to radiation oncology.

  • Kuang Xu

    Kuang Xu

    Associate Professor of Operations, Information and Technology at the Graduate School of Business and, by courtesy, of Electrical Engineering

    BioKuang Xu was born in Suzhou, China. He received the B.S. degree in Electrical Engineering (2009) from the University of Illinois at Urbana-Champaign, Urbana, Illinois, USA, and the Ph.D. degree in Electrical Engineering and Computer Science (2014) from the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. He was a postdoctoral fellow at the Microsoft Research-Inria Joint Center in Paris, France (2014-2015).

    His research primarily focuses on understanding fundamental properties and design principles of large-scale stochastic systems using tools from probability theory and optimization, with applications in queueing networks, healthcare, privacy and machine learning. He received First Place in the INFORMS George E. Nicholson Student Paper Competition (2011), the Best Paper Award, as well as the Kenneth C. Sevcik Outstanding Student Paper Award at ACM SIGMETRICS (2013), and the ACM SIGMETRICS Rising Star Research Award (2020). He currently serves as an Associate Editor for Operations Research.

  • Yoshihisa Yamamoto

    Yoshihisa Yamamoto

    Professor of Electrical Engineering and of Applied Physics, Emeritus

    Current Research and Scholarly InterestsExperimental Quantum Optics, Semiconductor Physics, Quantum Information

  • Yinyu Ye

    Yinyu Ye

    Kwoh-Ting Li Professor in the School of Engineering and Professor, by courtesy, of Electrical Engineering

    BioYinyu Ye is currently the Kwoh-Ting Li Professor in the School of Engineering at the Department of Management Science and Engineering and Institute of Computational and Mathematical Engineering and the Director of the MS&E Industrial Affiliates Program, Stanford University. He received the B.S. degree in System Engineering from the Huazhong University of Science and Technology, China, and the M.S. and Ph.D. degrees in Engineering-Economic Systems and Operations Research from Stanford University. Ye's research interests lie in the areas of optimization, complexity theory, algorithm design and analysis, and applications of mathematical programming, operations research and system engineering. He is also interested in developing optimization software for various real-world applications. Current research topics include Liner Programming Algorithms, Markov Decision Processes, Computational Game/Market Equilibrium, Metric Distance Geometry, Dynamic Resource Allocation, and Stochastic and Robust Decision Making, etc. He is an INFORMS (The Institute for Operations Research and The Management Science) Fellow, and has received several research awards including the winner of the 2014 SIAG/Optimization Prize awarded every three years to the author(s) of the most outstanding paper, the inaugural 2012 ISMP Tseng Lectureship Prize for outstanding contribution to continuous optimization, the 2009 John von Neumann Theory Prize for fundamental sustained contributions to theory in Operations Research and the Management Sciences, the inaugural 2006 Farkas prize on Optimization, and the 2009 IBM Faculty Award. He has supervised numerous doctoral students at Stanford who received received the 2015 and 2013 Second Prize of INFORMS Nicholson Student Paper Competition, the 2013 INFORMS Computing Society Prize, the 2008 Nicholson Prize, and the 2006 and 2010 INFORMS Optimization Prizes for Young Researchers. Ye teaches courses on Optimization, Network and Integer Programming, Semidefinite Programming, etc. He has written extensively on Interior-Point Methods, Approximation Algorithms, Conic Optimization, and their applications; and served as a consultant or technical board member to a variety of industries, including MOSEK.

  • Serena Yeung

    Serena Yeung

    Assistant Professor of Biomedical Data Science and, by courtesy, of Computer Science and of Electrical Engineering

    BioDr. Serena Yeung is an Assistant Professor of Biomedical Data Science and, by courtesy, of Computer Science and of Electrical Engineering at Stanford University. Her research focus is on developing artificial intelligence and machine learning algorithms to enable new capabilities in biomedicine and healthcare. She has extensive expertise in deep learning and computer vision, and has developed computer vision algorithms for analyzing diverse types of visual data ranging from video capture of human behavior, to medical images and cell microscopy images.

    Dr. Yeung leads the Medical AI and Computer Vision Lab at Stanford. She is affiliated with the Stanford Artificial Intelligence Laboratory, the Clinical Excellence Research Center, the Center for Artificial Intelligence in Medicine & Imaging, the Center for Human-Centered Artificial Intelligence, and Bio-X. She also serves on the NIH Advisory Committee to the Director Working Group on Artificial Intelligence.

  • Matei Zaharia

    Matei Zaharia

    Assistant Professor of Computer Science

    BioHomepage: https://cs.stanford.edu/~matei/

  • Howard Zebker

    Howard Zebker

    Professor of Electrical Engineering and of Geophysics

    Current Research and Scholarly InterestsResearch
    My students and I study the surfaces of Earth and planets using radar remote sensing methods. Our specialization is interferometric radar, or InSAR. InSAR is a technique to measure mm-scale surface deformation at fine resolution over wide areas, and much of our work follows from applying this technique to the study of earthquakes, volcanoes, and human-induced subsidence. We also address global environmental problems by tracking the movement of ice in the polar regions. whose ice mass balance affects sea level rise and global climate. We participate in NASA space missions such as Cassini, in which we now are examining the largest moon of Saturn, Titan, to try and deduce its composition and evolution. Our work includes experimental observation and modeling the measurements to best understand processes affecting the Earth and solar system. We use data acquired by spaceborne satellites and by large, ground-based radar telescopes to support our research.

    Teaching
    I teach courses related to remote sensing methods and applications, and how these methods can be used to study the world around us. At the undergraduate level, these include introductory remote sensing uses of the full electromagnetic spectrum to characterize Earth and planetary surfaces and atmospheres, and methods of digital image processing. I also teach a freshman and sophomore seminar course on natural hazards. At the graduate level, the courses are more specialized, including the math and physics of two-dimensional imaging systems, plus detailed ourses on imaging radar systems for geophysical applications.

    Professional Activities
    InSAR Review Board, NASA Jet Propulsion Laboratory (2006-present); editorial board, IEEE Proceedings (2005-present); NRC Earth Science and Applications from Space Panel on Solid Earth Hazards, Resources, and Dynamics (2005-present); Chair, Western North America InSAR (WInSAR) Consortium (2004-06); organizing committee, NASA/NSF/USGS InSAR working group; International Union of Radioscience (URSI) Board of Experts for Medal Evaluations (2004-05); National Astronomy and Ionospheric Center, Arecibo Observatory, Visiting Committee, (2002-04; chair, 2003-04); NASA Alaska SAR Facility users working group (2000-present); associate editor, IEEE Transactions on Geoscience and Remote Sensing (1998-present); fellow, IEEE (1998)

  • James Zou

    James Zou

    Assistant Professor of Biomedical Data Science and, by courtesy, of Computer Science and of Electrical Engineering

    Current Research and Scholarly InterestsMy group works on both foundations of statistical machine learning and applications in biomedicine and healthcare. We develop new technologies that make ML more accountable to humans, more reliable/robust and reveals core scientific insights.

    We want our ML to be impactful and beneficial, and as such, we are deeply motivated by transformative applications in biotech and health. We collaborate with and advise many academic and industry groups.