School of Engineering
Showing 51-100 of 803 Results
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Halleh Balch
Assistant Professor of Oceans and, by courtesy, of Electrical Engineering
BioHalleh B. Balch is an experimental physicist and HHMI Hanna H. Gray Faculty Fellow at Stanford University. Her research broadly focuses on advancing imaging, spectroscopy, and nanophotonics with a focus on applications in oceanography and water sustainability. Halleh received her PhD in physics from the University of California Berkeley and her undergraduate degree from Swarthmore College in physics and literature. Halleh joined Stanford as an Assistant Professor in the Doerr School of Sustainability in August 2025.
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Nicholas Bambos
Richard W. Weiland Professor in the School of Engineering and Professor of Electrical Engineering
BioNick Bambos is R. Weiland Professor in the School of Engineering at Stanford University, having a joint appointment in the Department of Electrical Engineering and the Department of Management Science & Engineering. He has been the Fortinet Founders Department Chair of the Management Science & Engineering Department (2016 – 20).
He heads the Computer Systems Performance Engineering Lab (Perf-Lab) at Stanford, comprised of doctoral students and industry visitors engaged in various research projects, and was the Director (1999 – 2005) of the Stanford Networking Research Center (a research project of about $30M). He has published over 300 peer-reviewed research publications and graduated over 40 doctoral students (including two post-doctoral ones), who have moved on to leadership positions in academia, the Silicon Valley industries and technology startups, finance and venture capital, etc.
His research interests are in architecture and high-performance engineering of computer systems and networks, as well as data analytics with an emphasis on medical and health-care analytics. His research contributions span the areas of networking and the Internet, cloud computing and data centers, multimedia streaming, computer security, digital health, etc. His methodological interests and contributions span the areas of network control, online task scheduling, routing and distributed processing, machine learning and artificial intelligence, etc.
He received his Ph.D. (1989) in Electrical Engineering & Computer Sciences from the University of California at Berkeley. Before joining Stanford in 1996, he served as assistant professor (1989 – 95) and tenured associate professor (1995 – 96) of Electrical Engineering at the University of California at Los Angeles (UCLA).
He has received several best research paper awards and has been the Cisco Systems Faculty Development Chair and the David Morgenthaler Faculty Scholar at Stanford. He has won the IBM Faculty Award, as well as the National Young Investigator Award and the Research Initiation Award from the National Science Foundation. He has been a Berkeley U.C. Regents Fellow, an E. C. Anthony Fellow, and a D. & S. Gale Fellow.
He has served on various editorial boards of research journals, scientific boards of research labs, international technical and scientific committees, and technical review panels for networking and computing technologies. He has also served on corporate technical boards, as consultant and co-founder of technology start-up companies, and as expert witness in high-profile patent litigation and other legal cases involving information technologies. -
Mohsen Bayati
Carl and Marilynn Thoma Professor in the Graduate School of Business and Professor, by courtesy, of Electrical Engineering and of Radiation Oncology (Radiation Therapy)
Current Research and Scholarly Interests1) Healthcare management: I am interested in improving healthcare delivery using data-driven modeling and decision-making.
2) Network models and message-passing algorithms: I work on graphical modeling ideas motivated from statistical physics and their applications in statistical inference.
3) Personalized decision-making: I work on machine learning and statistical challenges of personalized decision-making. The problems that I have worked on are primarily motivated by healthcare applications. -
Stacey Bent
Jagdeep & Roshni Singh Professor in the School of Eng, Professor of Energy Science and Eng, Senior Fellow at Precourt & Prof, by courtesy, of Electrical Eng, Materials Sci Eng & Chemistry
BioThe research in the Bent laboratory is focused on understanding and controlling surface and interfacial chemistry and applying this knowledge to a range of problems in semiconductor processing, micro- and nano-electronics, nanotechnology, and sustainable and renewable energy. Much of the research aims to develop a molecular-level understanding in these systems, and hence the group uses of a variety of molecular probes. Systems currently under study in the group include functionalization of semiconductor surfaces, mechanisms and control of atomic layer deposition, molecular layer deposition, nanoscale materials for light absorption, interface engineering in photovoltaics, catalyst and electrocatalyst deposition.
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Rohan Tan Bhowmik
Undergraduate, Electrical Engineering
BioI am an undergraduate student at Stanford University studying Computer Science and Electrical Engineering with an emphasis on artificial intelligence. I am constantly seeking to learn and develop new machine-learning techniques and build applications based on them, especially in the areas of health, environment, and human-computer interaction. I’m especially interested in brain-inspired computing for energy-efficient systems.
As a software engineering intern at AMD AI Group since June 2024, I’ve gained expertise in machine learning compilers and optimized model performance across diverse hardware architectures. I unified AI/ML model implementations for high-performance computing on CPUs, GPUs, and AI accelerators. I also developed masked and causal attention modules on Torch-MLIR and IREE, enabling models like LLaMa and Stable Diffusion on the AMD stack.
My other recent projects include the development of 1) a wildfire prediction method by analyzing trends in environmental, meteorological, and geological data with an aim to mitigate the impact of California’s devastating wildfire seasons, 2) a respiratory disease exacerbation prediction system based on a novel spatio-temporal artificial intelligence algorithm and local environmental sensor network, 3) a machine learning technique for automating patient facial condition assessment and surgery planning, 4) blood alcohol level estimation using infrared imaging and deep neural networks, and 5) a novel image recognition framework utilizing a quantum optical convolutional neural network.
I have published papers based on my research in peer-reviewed journals, including the Journal of Environmental Management, IEEE Access, Electronics, and Facial Plastic Surgery & Aesthetic Medicine. I have won top national awards in the USA Physics, Astronomy & Astrophysics, Junior Math, Computing, and Biology Olympiads and was named Regeneron STS Top 300 Finalist in 2023.
Outside of academics, I play clarinet, tennis, and volunteer with organizations to help sensory-deficient individuals, including the Baker Institute for Children with Hearing Loss, Starkey Hearing Foundation, and VocaliD. -
Kwabena Boahen
Professor of Bioengineering and of Electrical Engineering
Current Research and Scholarly InterestsBoahen's group analyzes neural behavior computationally to elucidate principles of neural design at the cellular, circuit, and systems levels; and synthesizes neuromorphic electronic systems that scale energy-use with size as efficiently as the brain does. This interdisciplinary research program bridges neurobiology and medicine with electronics and computer science, bringing together these seemingly disparate fields.
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Ivo Bolsens
Adjunct Professor
BioDirector of System X and instructor for EE310
Ivo retired from AMD as Senior Vice-President Corporate Research and Advanced Development. He managed advanced hardware and software technology development, including future architectures and software stacks to enable emerging opportunities in the fields of AI and embedded computing. His team was also driving the university partnerships to create a thriving, global ecosystem for AMD technology in academia.
He joined AMD in 2022, as part of the Xilinx acquisition. At Xilinx, he served as the Chief Technology Officer in charge of corporate research. He joined Xilinx in 2001 from the Interuniversity Microelectronics Centre (IMEC), an international research center based in Belgium. At IMEC he was vice president leading the R&D of digital signal processing hardware and software. During his tenure at IMEC, he spun-out several successful startups in the field of SOC design tools and wireless systems.
He serves on the advisory boards of IMEC, the Engineering Departments of San Jose State University and Santa Clara University, and the Department of Electrical Engineering and Computer Sciences at UC Berkeley.
He received his Master’s degree and PhD degree (EE) from the KU Leuven university in Belgium. -
Dan Boneh
Cryptography Professor and Professor of Electrical Engineering
BioProfessor Boneh heads the applied cryptography group and co-direct the computer security lab. Professor Boneh's research focuses on applications of cryptography to computer security. His work includes cryptosystems with novel properties, web security, security for mobile devices, and cryptanalysis. He is the author of over a hundred publications in the field and is a Packard and Alfred P. Sloan fellow. He is a recipient of the 2014 ACM prize and the 2013 Godel prize. In 2011 Dr. Boneh received the Ishii award for industry education innovation. Professor Boneh received his Ph.D from Princeton University and joined Stanford in 1997.
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Stephen Boyd
Samsung Professor in the School of Engineering
BioStephen P. Boyd is the Samsung Professor of Engineering, and Professor of Electrical Engineering in the Information Systems Laboratory at Stanford University, and a member of the Institute for Computational and Mathematical Engineering. His current research focus is on convex optimization applications in control, signal processing, machine learning, and finance.
Professor Boyd received an AB degree in Mathematics, summa cum laude, from Harvard University in 1980, and a PhD in EECS from U. C. Berkeley in 1985. In 1985 he joined Stanford's Electrical Engineering Department. He has held visiting Professor positions at Katholieke University (Leuven), McGill University (Montreal), Ecole Polytechnique Federale (Lausanne), Tsinghua University (Beijing), Universite Paul Sabatier (Toulouse), Royal Institute of Technology (Stockholm), Kyoto University, Harbin Institute of Technology, NYU, MIT, UC Berkeley, CUHK-Shenzhen, and IMT Lucca. He holds honorary doctorates from Royal Institute of Technology (KTH), Stockholm, and Catholic University of Louvain (UCL).
Professor Boyd is the author of many research articles and four books: Introduction to Applied Linear Algebra: Vectors, Matrices, and Least-Squares (with Lieven Vandenberghe, 2018), Convex Optimization (with Lieven Vandenberghe, 2004), Linear Matrix Inequalities in System and Control Theory (with El Ghaoui, Feron, and Balakrishnan, 1994), and Linear Controller Design: Limits of Performance (with Craig Barratt, 1991). His group has produced many open source tools, including CVX (with Michael Grant), CVXPY (with Steven Diamond) and Convex.jl (with Madeleine Udell and others), widely used parser-solvers for convex optimization.
He has received many awards and honors for his research in control systems engineering and optimization, including an ONR Young Investigator Award, a Presidential Young Investigator Award, and the AACC Donald P. Eckman Award. In 2013, he received the IEEE Control Systems Award, given for outstanding contributions to control systems engineering, science, or technology. In 2012, Michael Grant and he were given the Mathematical Optimization Society's Beale-Orchard-Hays Award, for excellence in computational mathematical programming. In 2023, he was given the AACC Richard E. Bellman Control Heritage Award, the highest recognition of professional achievement for U.S. control systems engineers and scientists. In 2025, he was awarded the IFAC Nathaniel B. Nichols Medal.
He is a Fellow of the IEEE, SIAM, INFORMS, IFAC, and ACA, a Distinguished Lecturer of the IEEE Control Systems Society, a member of the US National Academy of Engineering, a foreign member of the Chinese Academy of Engineering, and a foreign member of the National Academy of Engineering of Korea. He has been invited to deliver more than 100 plenary and keynote lectures at major conferences in control, optimization, signal processing, and machine learning.
He has developed and taught many undergraduate and graduate courses, including Signals & Systems, Linear Dynamical Systems, Convex Optimization, and a recent undergraduate course on Matrix Methods. His graduate convex optimization course attracts around 300 students from more than 20 departments. In 1991 he received an ASSU Graduate Teaching Award, and in 1994 he received the Perrin Award for Outstanding Undergraduate Teaching in the School of Engineering. In 2003, he received the AACC Ragazzini Education award, for contributions to control education. In 2016 he received the Walter J. Gores award, the highest award for teaching at Stanford University. In 2017 he received the IEEE James H. Mulligan, Jr. Education Medal, for a career of outstanding contributions to education in the fields of interest of IEEE, with citation "For inspirational education of students and researchers in the theory and application of optimization." -
Emmanuel Candes
Barnum-Simons Chair of Math and Statistics, and Professor of Statistics and, by courtesy, of Electrical Engineering
BioEmmanuel Candès is the Barnum-Simons Chair in Mathematics and Statistics, a professor of electrical engineering (by courtesy) and a member of the Institute of Computational and Mathematical Engineering at Stanford University. Earlier, Candès was the Ronald and Maxine Linde Professor of Applied and Computational Mathematics at the California Institute of Technology. His research interests are in computational harmonic analysis, statistics, information theory, signal processing and mathematical optimization with applications to the imaging sciences, scientific computing and inverse problems. He received his Ph.D. in statistics from Stanford University in 1998.
Candès has received several awards including the Alan T. Waterman Award from NSF, which is the highest honor bestowed by the National Science Foundation, and which recognizes the achievements of early-career scientists. He has given over 60 plenary lectures at major international conferences, not only in mathematics and statistics but in many other areas as well including biomedical imaging and solid-state physics. He was elected to the National Academy of Sciences and to the American Academy of Arts and Sciences in 2014. -
Hugo Chen
Ph.D. Student in Electrical Engineering, admitted Autumn 2022
BioHugo "Jiun-Yu" Chen is currently pursuing his Ph.D. degree in the Department of Electrical Engineering at Stanford University. He earned his M.S. in Photonics and Optoelectronics from National Taiwan University in 2019 and his B.S. in Materials Science and Engineering from National Dong Hwa University in 2017.
Prior to joining Stanford, Hugo worked as an R&D engineer at Taiwan Semiconductor Manufacturing Company (TSMC) in the High Power Program and Analog Power/RF Specialty Technology from 2019 to 2022. His research experience includes investigating GaN high electron mobility transistors (HEMTs) for high power converter applications, oxide-based thin-film transistors (TFTs) for CMOS inverter applications, and III-V quantum dots molecular beam epitaxy (MBE) material growth.
As the first author, Hugo has published two peer-reviewed journal articles, six conference papers, and one US/KR/TW/CN/DE patent. He is currently advised by Professors H.-S. Philip Wong and Kwabena Boahen, and his research focuses on developing ferroelectric field-effect transistors (FeFETs) for dendritic-centric learning.
In his leisure time, Hugo enjoys biking, playing badminton, and watching dramas. -
Po-Han Chen
Ph.D. Student in Electrical Engineering, admitted Winter 2021
BioPo-Han Chen is an EE Ph.D. student at Stanford University supervised by Prof. Priyanka Raina. He received his B.S. in Electrical Engineering and Computer Science (EECS) and M.S. in Electrical Engineering from National Tsing Hua University (Taiwan) in 2016 and 2018 respectively. Before joining Stanford, he was a digital circuit designer at MediaTek where he worked on developing hardware architectures of image processing pipeline. He is interested in designing hardware accelerators. Most of his previous works were related to computational photography algorithms such as digital refocusing. Currently, He is focusing on analyzing and designing architecture of CGRAs to create high-performance, energy-efficient, and reconfigurable computing platforms.
