School of Engineering
Showing 751-789 of 789 Results
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Bill Yen
Ph.D. Student in Electrical Engineering, admitted Autumn 2023
Student Assistant, Electrical EngineeringBioBill Yen is a Ph.D. candidate in the Department of Electrical Engineering at Stanford University working in the area of low-power Internet of Things (IoT) systems. He is an interdisciplinary maker and environmental scientist passionate about solving issues related to food, water, and energy using smart technologies.
Yen's experience in industry (General Motors, CNH Industrial) and academic research (Northwestern - soil-powered computing, Stanford - low-power wireless communication) cultivated his interest in designing self-powered computing devices that boost system efficiency while lowering the environmental impact of existing processes. His work has been featured by The Independent, Fast Company, MIT Technology Review China, Hackster.io, and more. He is also a recipient of the Stanford Graduate Fellowship in Science & Engineering. -
Serena Yeung-Levy
Assistant Professor of Biomedical Data Science and, by courtesy, of Electrical Engineering and of Computer Science
BioDr. Serena Yeung-Levy 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-Levy leads the Medical AI and Computer Vision Lab at Stanford. She is affiliated with the Stanford Artificial Intelligence Laboratory, the Clinical Excellence Research Center, and the Center for Artificial Intelligence in Medicine & Imaging. She is also a Chan Zuckerberg Biohub Investigator and has served on the NIH Advisory Committee to the Director Working Group on Artificial Intelligence. -
Jeffrey Yu
Ph.D. Student in Electrical Engineering, admitted Autumn 2023
BioI am a first year EE Ph.D. student majoring at Stanford University. I received my M.S. degree in Electrical Engineering from Stanford University in 2023 and my B.S. degree in Computer Engineering from UC San Diego in 2021. I am interested in DNN quantization and digital accelerator design.
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Chik Patrick Yue
Affiliate, Electrical Engineering - Integrated Circuits Laboratory
BioProf. Yue received the Master and PhD degrees in EE from Stanford in 1994 and 1998, respectively. He has been a professor for over two decades, and taught IC design classes and conduct research at HKUST (2010-now), UC Santa Barbara (2006-11), Carnegie Mellon (2003-06), Tsinghua (visiting 2016), and Stanford (visiting 1998 & 2025-now). He has supervised over 10 post-docs, 30 PhD and 10 MPhil students. He has published over 250 technical papers, holds over 25 patents and accumulated over 10000 citations. Together with his students and teachers, he has been awarded the IEEE VLSI Circuit Symposium Test of Time Award (2024), the IEEE Circuits and Systems Society Outstanding Young Author Award (2017), and the Guanghua Engineering Science and Technology Youth Award by the Chinese Academy of Engineering (2016), and the ISSCC Best Student Paper Award (2003). Over the years, he has cofounded a number of startups in both Silicon Valley and Hong Kong including Atheros Communications (1998), Jetcomm Technologies (2014), LiPHY Communications (2018), and High5 Semiconductor (2024) to commercialize technologies from academic to industry.
Prof. Yue is a Fellow of the IEEE and Optica, and a member of the ACM. He has a diverse research interests spanning from optical wireline and mmWave wireless SoC and SiP design, neural implant microsystems, 3D computer vision models, and power network management system. -
Sajung Yun, PhD, MBA
Visiting Scholar, Center for East Asian Studies
Affiliate, US-Asia Technology Management CenterBioDr. Sajung Yun is a multifaceted scholar and entrepreneur whose work bridges the disciplines of genomics, biomedical sciences, and artificial intelligence. At Stanford, his current research focuses on AI's self-recognition, self-protection, and self-perpetuation mechanisms and their implications in relation to Artificial General Intelligence and Super-Specialized Generalist Intelligence in medicine. He also serves as Adjunct Professor of Bioinformatics at Johns Hopkins University where he teaches bioinformatics courses over the last ten years.
Dr. Yun earned his Ph.D. in Biomedical Sciences from the John A. Burns School of Medicine and his MBA with concentrations in Healthcare Management and Entrepreneurship from Johns Hopkins University, blending rigorous scientific training with strategic leadership in medical innovation. He also attended M.D. program and completed 121 credits at John A. Burns School of Medicine. His academic appointments also include a concurrent role as Adjunct Professor in Biomedical Engineering at Ulsan National Institute of Science and Technology (UNIST), where he continues to contribute to global collaborations in AI-driven bioinformatics and healthcare system optimization.
As the Founder and CEO of Predictive AI, Dr. Yun leads a digital health company specializing in AI-based personalized preventive medicine platforms. Under his leadership, the company has been recognized for excellence in innovation, receiving distinctions such as the 2023 and 2022 4th Industrial Revolution Awards in AI and Biohealth, and the 2024 Venture Business Association President’s Award at the 6th Korea SME & Startup Awards. In recognition of his contributions to global innovation and leadership, Dr. Yun was named a 2025 Forbes Global CEO Delegate. In 2026, he lead his company to win Honoree Award in CES.
Dr. Yun’s professional career began as a Research Fellow at the U.S. National Institutes of Health (NIH), where he investigated advanced gene editing and genetic surgical methods. His research portfolio spans topics including next-generation sequencing data analysis, MRI volumetric analysis, and AI applications in biomedical imaging. His numerous publications and work continues to contribute to the evolving landscape of digital healthcare, emphasizing the convergence of data science, clinical insight, and artificial intelligence for human health advancement. -
Howard Zebker
Kwoh Ting Li Professor in the School of Engineering and Professor 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) -
Rachel (Yinghao) Zhang
Managing Director of Industry Partnerships, Stanford SystemX Alliance, Electrical Engineering
BioRachel is the Managing Director of Industry Partnerships at SystemX.
As an innovative business leader, Rachel has launched and expanded businesses across the U.S., Asia, and global markets for tech companies including Alibaba, Ant Group, and Microchip.
During her tenure as Senior Advisor at Ant Group, she also incubated a philanthropic initiative to cultivate 10,000 technology leaders over a decade, aiming to bridge the digital divide and drive economic growth in emerging markets.
Rachel is passionate about harnessing technology for good, driving innovation, and bridging industry collaboration to create a broader meaningful impact.
Rachel is a Stanford GSB alumna. -
Zhe Zhang
Postdoctoral Scholar, Electrical Engineering
BioEPFL Ph.D. in Photonics 2024
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Orr Zohar
Ph.D. Student in Electrical Engineering, admitted Autumn 2021
BioOrr Zohar is a PhD candidate in Electrical Engineering at Stanford University and a Knight-Hennessy Scholar. He builds large-scale multimodal foundation models - spanning data curation, pretraining, and post-training - with a focus on video understanding, long-horizon reasoning, and robust transfer under real-world distribution shift. His work includes open-source model and dataset efforts and methods for evaluation and alignment of multimodal systems, with an emphasis on turning research into deployment-ready learning systems.
Before Stanford, he earned a BSc in Chemical Engineering (summa cum laude) and an MSc in Electrical Engineering from the Technion–Israel Institute of Technology, and worked as a machine learning and algorithms engineer at proteanTecs. Earlier research experiences include applied sensing and medical-imaging work. -
James Zou
Associate 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.
