Stanford University
Showing 601-650 of 762 Results
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Maxwell Bradley Strange
Ph.D. Student in Electrical Engineering, admitted Autumn 2018
BioMax is a Ph.D. student in Electrical Engineering advised by Mark Horowitz. His research focuses on developing infrastructure and tools to facilitate agile hardware development as part of the ongoing efforts by the Stanford AHA! Research Center. His research interests also include domain-specific hardware architectures, hardware/software co-design, and embedded systems design. He graduated from the University of Wisconsin in 2017 with a B.S. in Computer Engineering and Computer Science.
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Fouad Tobagi
Professor of Electrical Engineering
BioTobagi works on network control mechanisms for handling multimedia traffic (voice, video and TCP- based applications) and on the performance assessment of networked multimedia applications using user-perceived quality measures. He also investigates the design of wireless networks, including QoS-based media access control and network resource management, as well as network architectures and infrastructures for the support of mobile users, all meeting the requirements of multimedia traffic. He also investigates the design of metropolitan and wide area networks combining optical and electronic networking technologies, including topological design, capacity provisioning, and adaptive routing.
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Alexander Toews
Ph.D. Student in Electrical Engineering, admitted Autumn 2017
Current Research and Scholarly InterestsMagnetic resonance imaging, computational imaging
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Caroline Trippel
Assistant Professor of Computer Science and of Electrical Engineering
BioCaroline Trippel is an Assistant Professor in the Computer Science and Electrical Engineering Departments at Stanford University working in the area of computer architecture. Prior to starting at Stanford, Trippel spent nine months as a Research Scientist at Facebook in the FAIR SysML group. Her work focuses on promoting correctness and security as first-order computer systems design metrics (akin to performance and power). A central theme of her work is leveraging formal methods techniques to design and verify hardware systems in order to ensure that they can provide correctness and security guarantees for the applications they intend to support. Additionally, Trippel has been recently exploring the role of architecture in enabling privacy-preserving machine learning, the role of machine learning in hardware systems optimizations, particularly in the context of neural recommendation, and opportunities for improving datacenter and at-scale machine learning reliability.
Trippel's research has influenced the design of the RISC-V ISA memory consistency model both via her formal analysis of its draft specification and her subsequent participation in the RISC-V Memory Model Task Group. Additionally, her work produced a novel methodology and tool that synthesized two new variants of the now-famous Meltdown and Spectre attacks.
Trippel's research has been recognized with IEEE Top Picks distinctions, the 2020 ACM SIGARCH/IEEE CS TCCA Outstanding Dissertation Award, and the 2020 CGS/ProQuest® Distinguished Dissertation Award in Mathematics, Physical Sciences, & Engineering. She was also awarded an NVIDIA Graduate Fellowship (2017-2018) and selected to attend the 2018 MIT Rising Stars in EECS Workshop. Trippel completed her PhD in Computer Science at Princeton University and her BS in Computer Engineering at Purdue University. -
Madeleine Udell
Assistant Professor of Management Science and Engineering and, by courtesy, of Electrical Engineering
Current Research and Scholarly InterestsProfessor Udell develops new techniques to accelerate and automate data science,
with a focus on large-scale optimization and on data preprocessing,
and with applications in medical informatics, engineering system design, and automated machine learning. -
Benjamin Van Roy
Professor of Electrical Engineering, of Management Science and Engineering
BioBenjamin Van Roy is a Professor at Stanford University, where he has served on the faculty since 1998. His current research focuses on reinforcement learning. Beyond academia, he leads a DeepMind Research team in Mountain View, and has also led research programs at Unica (acquired by IBM), Enuvis (acquired by SiRF), and Morgan Stanley.
He is a Fellow of INFORMS and IEEE and has served on the editorial boards of Machine Learning, Mathematics of Operations Research, for which he co-edited the Learning Theory Area, Operations Research, for which he edited the Financial Engineering Area, and the INFORMS Journal on Optimization. He received the SB in Computer Science and Engineering and the SM and PhD in Electrical Engineering and Computer Science, all from MIT, where his doctoral research was advised by John N. Tstitsiklis. He has been a recipient of the MIT George C. Newton Undergraduate Laboratory Project Award, the MIT Morris J. Levin Memorial Master's Thesis Award, the MIT George M. Sprowls Doctoral Dissertation Award, the National Science Foundation CAREER Award, the Stanford Tau Beta Pi Award for Excellence in Undergraduate Teaching, the Management Science and Engineering Department's Graduate Teaching Award, and the Lanchester Prize. He was the plenary speaker at the 2019 Allerton Conference on Communications, Control, and Computing. He has held visiting positions as the Wolfgang and Helga Gaul Visiting Professor at the University of Karlsruhe, the Chin Sophonpanich Foundation Professor and the InTouch Professor at Chulalongkorn University, a Visiting Professor at the National University of Singapore, and a Visiting Professor at the Chinese University of Hong Kong, Shenzhen. -
Dave Van Veen
Ph.D. Student in Electrical Engineering, admitted Autumn 2021
BioFor more about Dave, see http://davevanveen.com
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Jelena Vuckovic
Jensen Huang Professor of Global Leadership, Professor of Electrical Engineering and, by courtesy, of Applied Physics
Current Research and Scholarly InterestsJelena Vuckovic’s research interests are broadly in the areas of nanophotonics, quantum and nonlinear optics. Her lab develops semiconductor-based photonic chip-scale systems with goals to probe new regimes of light-matter interaction, as well as to enable platforms for future classical and quantum information processing technologies. She also works on transforming conventional photonics with the concept of inverse design, where optimal photonic devices are designed from scratch using computer algorithms with little to no human input. Her current projects include quantum and nonlinear optics, cavity QED, and quantum information processing with color centers in diamond and in silicon carbide, heterogeneously integrated chip-scale photonic systems, and on-chip laser driven particle accelerators.
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Sumaiya Wahid
Ph.D. Student in Electrical Engineering, admitted Autumn 2019
Current Research and Scholarly InterestsPhase change memory
Low temperature processable p-type semiconductors
Oxide semiconductors for upper layer transistors
Flexible Electronics
