School of Engineering
Showing 1,351-1,400 of 6,464 Results
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Angelo Dragone
Associate Professor of Photon Science and, by courtesy, of Electrical Engineering
BioAngelo Dragone is an Associate Professor of Photon Science and Electrical Engineering (by courtesy). He has over 20 years of experience in the research and development of Instrumentation for Scientific experiments. He received his Ph.D. in Microelectronics from the Polytechnic University of Bari, Italy, for his research on mixed-signal readout architecture for radiation detectors, conducted at Brookhaven National Laboratory. He worked in the Instrumentation Division at Brookhaven National Laboratory from 2004, before joining SLAC National Accelerator Laboratory in 2008. Over the past 15 years, he has been designing radiation detectors, with a focus on innovative architectural solutions for state-of-the-art scientific instruments and sensor interfaces. These solutions have applications in photon science, particle physics, medical imaging, and national security. At SLAC, he focused his research on designing high frame rate, large dynamic range X-ray detectors for the Linac Coherent Light Source SLAC X-ray Free-electron Laser facility. Since 2012, he has held a management position as head of the Integrated Circuits Department within the Instrumentation Division of the Technology Innovation Directorate (TID) at SLAC. During the past three years, Dr. Dragone has been working on the strategic R&D planning for the SLAC X-ray detectors Initiative and leads, as Program Director, TID Detector R&D, and the applied Microelectronics program. Recently, he has been appointed as Deputy Associate Lab Director for TID strategy. His current research interests are on ultra-fast X-ray detector architectures for X-ray Free-Electron Lasers applications and developing efficient, scalable systems with "smart" real-time processing capabilities. More broadly, he is interested in understanding the fundamental performance limits of radiation detection systems.
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Persis S. Drell
Provost, Emerita, James and Anna Marie Spilker Professor, Professor of Materials Science and Engineering and of Physics
BioPersis Drell is the James and Anna Marie Spilker Professor in the School of Engineering, a professor of materials science and engineering, and a professor of physics. From Feb 1, 2017 to Sept. 30, 2023, Drell was the provost of Stanford University.
Prior to her appointment as provost in February 2017, she was dean of the Stanford School of Engineering from 2014 to 2017 and director of U.S. Department of Energy SLAC National Acceleratory Laboratory from 2007 to 2012.
She earned her bachelor’s degree in mathematics and physics from Wellesley College and her PhD in atomic physics from UC Berkeley. Before joining the faculty at Stanford in 2002, she was a faculty member in the physics department at Cornell University for 14 years. -
Leora Dresselhaus-Marais
Assistant Professor of Materials Science and Engineering, of Photon Science and, by courtesy, of Mechanical Engineering
Current Research and Scholarly InterestsMy group develops new methods to update old processes in metals manufacturing
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Ron Dror
Cheriton Family Professor and Professor, by courtesy, of Structural Biology and of Molecular & Cellular Physiology
Current Research and Scholarly InterestsMy lab’s research focuses on computational biology, with an emphasis on 3D molecular structure. We combine two approaches: (1) Bottom-up: given the basic physics governing atomic interactions, use simulations to predict molecular behavior; (2) Top-down: given experimental data, use machine learning to predict molecular structures and properties. We collaborate closely with experimentalists and apply our methods to the discovery of safer, more effective drugs.
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Shaul Druckmann
Associate Professor of Neurobiology, of Psychiatry and Behavioral Sciences and, by courtesy, of Electrical Engineering
Current Research and Scholarly InterestsOur research goal is to understand how dynamics in neuronal circuits relate and constrain the representation of information and computations upon it. We adopt three synergistic strategies: First, we analyze neural circuit population recordings to better understand the relation between neural dynamics and behavior, Second, we theoretically explore the types of dynamics that could be associated with particular network computations. Third, we analyze the structural properties of neural circuits.
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Junting Duan
Ph.D. Student in Management Science and Engineering, admitted Autumn 2020
BioJunting Duan is a PhD candidate in the Department of Management Science and Engineering (MS&E) at Stanford University. Prior to joining Stanford, she received her B.S. in Mathematics and Applied Mathematics from Peking University in 2020.
Junting's research interests lie broadly in data-driven decision-making, focusing on statistical inference and machine learning, with applications to causal inference and finance. Her research develops new methodologies with rigorous statistical foundations that enable reliable decision-making with complex and imperfect data, and lies at the intersection of (1) statistical learning for high-dimensional data; (2) causal inference; and (3) machine learning for finance and risk management. Her work has been recognized through publications and revisions at top journals including Management Science and the Journal of Econometrics, as well as invitations to present at major conferences such as the American Economic Association Annual Meeting, the NBER-NSF Time-Series Conference, the NBER Forecasting & Empirical Methods Conference, and the INFORMS Annual Meeting.
Visit her personal website for more details: https://juntingduan.com. -
John Duchi
Associate Professor of Statistics, of Electrical Engineering and, by courtesy, of Computer Science
Current Research and Scholarly InterestsMy work spans statistical learning, optimization, information theory, and computation, with a few driving goals: 1. To discover statistical learning procedures that optimally trade between real-world resources while maintaining statistical efficiency. 2. To build efficient large-scale optimization methods that move beyond bespoke solutions to methods that robustly work. 3. To develop tools to assess and guarantee the validity of---and confidence we should have in---machine-learned systems.
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Eric Dunham
Professor of Geophysics
Current Research and Scholarly InterestsPhysics of natural hazards, specifically earthquakes, tsunamis, and volcanoes. Computational geophysics.
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Alexander Dunn
Professor of Chemical Engineering
Current Research and Scholarly InterestsMy lab is deeply interested in uncovering the physical principles that underlie the construction of complex, multicellular animal life.
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Zakir Durumeric
Assistant Professor of Computer Science
BioI am an Assistant Professor of Computer Science. My research brings a large-scale, empirical approach to the study of Internet security, trust, and safety. I am interested in how to protect people against attacks on the Internet ranging from cybercrime and harassment to censorship and disinformation. I am broadly an empiricist: I build systems to measure complex networked ecosystems at scale, which I use to understand real-world behavior, uncover weaknesses and attacks, architect more resilient defenses, and guide public policy.
