Stanford University
Showing 321-340 of 756 Results
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Kalhan Koul
Ph.D. Student in Electrical Engineering, admitted Autumn 2019
BioKalhan Koul is an EE Ph.D. student at Stanford University supervised by Prof. Priyanka Raina. Previously, he was a Digital Design Intern at Micron and Silicon Labs. He received a B.S. in Electrical Engineering Honors and a B.A. in Plan II Honors (Liberal Arts) from The University of Texas in 2018 and his M.S. in Electrical Engineering from Stanford University in 2021. During his PhD he has worked on three chip tapeouts. The first was Chimera, a DNN accelerator utilizing RRAM for low energy inference. The next was Amber, a coarse grained reconfigurable array (CGRA) optimized for image processing and machine learning applications. Finally, Kalhan led the tapeout of Onyx, a CGRA accelerating both dense and sparse kernels on the same fabric. His current research focuses on further improving the efficiency of the CGRA and extending its acceleration to end-to-end machine learning workloads.
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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.
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Christoforos Kozyrakis
Professor of Electrical Engineering and of Computer Science
BioChristos Kozyrakis is 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 leads the MAST research group. He is also the faculty director of the Stanford Platform Lab.
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. -
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. 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.
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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.
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Anand Vikas Lalwani
Ph.D. Student in Electrical Engineering, admitted Autumn 2018
BioAnand is a Graduate Student researcher in XLab (advisor: Debbie Senesky).
Anand's research work includes developing and deploying sensors for environmental and energy industries. Sensors developed include techniques for Hall Effect sensors to measure AC magnetic fields, deployable and low cost ammonia sensor for rivers and lakes, CO2 sensors for down-hole applications.
Anand's interests outside of research include startups and solving problems. Anand is committed to developing technologies that tackle pressing issues and translating work form lab into a startup. -
Monica Lam
Kleiner Perkins, Mayfield, Sequoia Capital Professor in the School of Engineering and Professor, by courtesy, of Electrical Engineering
BioDr. Monica Lam is a Professor in the Computer Science Department at Stanford University, and the Faculty Director of the Stanford Open Virtual Assistant Laboratory. Dr. Monica Lam obtained her BS degree in computer science from University of British Columbia, and her PhD degree in computer science from Carnegie Mellon University in 1987. She joined Stanford in 1988.
Professor Lam's current research is on conversational virtual assistants with an emphasis on privacy protection. Her research uses deep learning to map task-oriented natural language dialogues into formal semantics, represented by a new executable programming language called ThingTalk. Her Almond virtual assistant, trained on open knowledge graphs and IoT API standards, can be easily customized to perform new tasks. She is leading an Open Virtual Assistant Initiative to create the largest, open, crowdsourced language semantics model to promote open access in all languages. Her decentralized Almond virtual assistant that supports fine-grain sharing with privacy has received Popular Science's Best of What's New Award in Security in 2019.
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.
