Electrical Engineering
Showing 351-400 of 792 Results
-
Gregory Kovacs
Professor of Electrical Engineering, Emeritus
Current Research and Scholarly InterestsHis present research areas include instruments for biomedical and biological applications including space flight, solid-state sensors and actuators, cell-based sensors for toxin detection and pharmaceutical screening, microfluidics, electronic interfaces to tissue, and biotechnology, all with emphasis on solving practical problems.
-
Christoforos Kozyrakis
Leonard Bosack and Sandy K. Lerner Professor of Engineering and Professor of Computer Science
BioChristos Kozyrakis is the Leonard Bosack and Sandy K. Lerner Professor of Engineering and a Professor of Electrical Engineering and Computer Science at Stanford University. His primary research areas are computer architecture and computer systems. His current work focuses on cloud computing, systems for machine learning, and machine learning for systems.
Christos holds a BS degree from the University of Crete and a PhD degree from the University of California at Berkeley. He is a fellow of the ACM and the IEEE. He has received the ACM SIGARCH Maurice Wilkes Award, the ISCA Influential Paper Award, the NSF Career Award, the Okawa Foundation Research Grant, and faculty awards by IBM, Microsoft, and Google. -
Siddharth Krishnan
Assistant Professor of Electrical Engineering, and by courtesy, of Bioengineering and of Materials Science and Engineering
BioSiddharth is an Assistant Professor of Electrical Engineering and a Terman Faculty Fellow at Stanford University. Prior to this, he was a K99-funded Research Scientist in the groups of Prof. Daniel Anderson and Prof. Robert Langer at the Koch Institute for Integrative Cancer Research at MIT and at Boston Children's Hospital. He received BS and MS degrees from Washington University in St. Louis, and his PhD from the University of Illinois at Urbana-Champaign from Prof. John Rogers' group. His work has focused on the development of bioelectronic devices for sensing and therapeutics. He has published over 20 scientific papers, is an inventor several granted and pending patents and is co-founded of Rhaeos Inc., a company focused on translating his graduate work on wireless wearable diagnostic tools for neurological surgery. His work has been recognized through several awards, including a postdoctoral fellowship from the Juvenile Diabetes Research Foundation, the 2019 Illinois Innovation Prize, a graduate student medal from the Materials Research Society and being named on MIT Technology Review’s Global Innovators Under 35 list.
-
Renesmee Kuo
Ph.D. Student in Electrical Engineering, admitted Autumn 2022
BioRenesmee Kuo is an Electrical Engineering PhD candidate at Stanford University supported by NSF GRFP and Stanford Lieberman fellowship. Her research interests lie at the intersection of engineering and medicine. She focuses on validation of preclinical PET imaging tracers and their translation into the clinic for applications in neuroinflammatory diseases (e.g., MS, AD) and cancer (e.g., brain metastasis) in Prof. Michelle James' lab. She graduated from UC Berkeley with a BS in Bioengineering. At Berkeley, she worked in Prof. Steve Conolly's lab on Magnetic Particle Imaging (MPI), focusing on tracking CAR-T cells in immunotherapy using high-resolution MPI tracers. She also explored commercially-available high-resolution MPI tracers for early diagnosis of pulmonary embolisms and cardiovascular disease in preclinical settings.
-
Sanjay Lall
Professor of Electrical Engineering
BioSanjay Lall is Professor of Electrical Engineering in the Information Systems Laboratory and Professor of Aeronautics and Astronautics at Stanford University. He received a B.A. degree in Mathematics with first-class honors in 1990 and a Ph.D. degree in Engineering in 1995, both from the University of Cambridge, England. His research group focuses on algorithms for control, optimization, and machine learning. Before joining Stanford he was a Research Fellow at the California Institute of Technology in the Department of Control and Dynamical Systems, and prior to that he was a NATO Research Fellow at Massachusetts Institute of Technology, in the Laboratory for Information and Decision Systems. He was also a visiting scholar at Lund Institute of Technology in the Department of Automatic Control. He has significant industrial experience applying advanced algorithms to problems including satellite systems, advanced audio systems, Formula 1 racing, the America's cup, cloud services monitoring, and integrated circuit diagnostic systems, in addition to several startup companies. Professor Lall has served as Associate Editor for the journal Automatica, on the steering and program committees of several international conferences, and as a reviewer for the National Science Foundation, DARPA, and the Air Force Office of Scientific Research. He is the author of over 130 peer-refereed publications.
-
Monica Lam
Kleiner Perkins, Mayfield, Sequoia Capital Professor in the School of Engineering, Professor of Computer Science and, by courtesy, of Electrical Engineering
BioProfessor Lam's current research interest is to create effective and reliable AI assistants to accelerate the discovery of knowledge. Her OVAL lab has created numerous open-source LLM-based tools used by consumers, historians, and journalists in their work; currently, she is focusing on research assistants that can discover new insights for biomedicine and other technical areas.
Professor Lam's team has created the first quantifiably factual and engaging conversational agent, which has won the Best Research of the Year Award from Wikimedia Foundation; pioneered deep research agent called STORM that has been used by about a million users; developed the best-performing agent for retrieving knowledge from hybrid sources, including databases, knowledge graphs, and free-text, currently deployed at Wikimedia; created an agent framework that produces fluent task-oriented agents that do not hallucinate.
Prof. Lam is also an expert in compilers for high-performance machines. Her pioneering work of affine partitioning provides a unifying theory to the field of loop transformations for parallelism and locality. Her software pipelining algorithm is used in commercial systems for instruction level parallelism. Her research team created the first, widely adopted research compiler, SUIF. She is a co-author of the classic compiler textbook, popularly known as the “dragon book”. She was on the founding team of Tensilica, now a part of Cadence.
Dr. Lam is a Member of the National Academy of Engineering and an Association of Computing Machinery (ACM) Fellow. -
Hannah Lee
Ph.D. Student in Electrical Engineering, admitted Autumn 2023
Masters Student in Electrical Engineering, admitted Winter 2026Current Research and Scholarly InterestsComputational Neuroscience, Statistical Modeling for Neuroscience
-
Jin Hyung Lee
Associate Professor of Neurology and Neurological Sciences (Neurology Research), of Neurosurgery and of Bioengineering and, by courtesy, of Electrical Engineering
Current Research and Scholarly InterestsIn vivo visualization and control of neural circuits
-
Thomas Lee
Professor of Electrical Engineering
BioProfessor Lee's principal areas of professional interest include analog circuitry of all types, ranging from low-level DC instrumentation to high-speed RF communications systems. His present research focus is on CMOS RF integrated circuit design, and on extending operation into the terahertz realm.
-
Craig Levin
Professor of Radiology (Molecular Imaging Program at Stanford/Nuclear Medicine) and, by courtesy, of Physics, of Electrical Engineering and of Bioengineering
Current Research and Scholarly InterestsMolecular Imaging Instrumentation
Laboratory
Our research interests involve the development of novel instrumentation and software algorithms for in vivo imaging of cellular and molecular signatures of disease in humans and small laboratory animal subjects. -
Philip Levis
Professor of Computer Science and of Electrical Engineering
BioProfessor Levis' research focuses on the design and implementation of efficient software systems for embedded wireless sensor networks; embedded network sensor architecture and design; systems programming and software engineering.
