School of Engineering
Showing 341-360 of 761 Results
-
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. -
Jin Hyung Lee
Associate Professor of Neurology (Neurology Research Faculty), 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.
-
Axel Levy
Ph.D. Student in Electrical Engineering, admitted Autumn 2020
BioAxel is a PhD candidate in Electrical Engineering at Stanford University. He is jointly supervised by Pr. Mike Dunne (LCLS, SLAC) and Pr. Gordon Wetzstein. His research focuses on solving inverse problems that arise in scientific imaging, that is to say getting as much information as possible about hidden physical quantities from noisy or sparsely sampled measurements.
