Stanford University
Showing 2,001-2,020 of 2,653 Results
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Genevieve Smith
Postdoctoral Scholar, Comparative Literature
BioI am a Postdoctoral Fellow at Stanford University in the Clayman Institute for Gender Research. I completed my doctoral degree at the University of Oxford, where I studied societal impacts of artificial intelligence in low- and middle-income countries, focusing on gender. As a social scientist with a disciplinary background of science and technology studies (STS) and devleopment studies, I examine the impacts of AI on inequality and society, as well as explore more equitable and responsible paradigms for AI technologies globally. I founded the Responsible AI Initiative at the Berkeley Artificial Intelligence Research Lab and teach on responsible AI. I am a research affiliate at the Minderoo Centre for Technology & Democracy at Cambridge University and at the Technology & Management Centre for Development at University of Oxford. Prior, I served as the Responsible AI Fellow at the United States Agency for International Development and as Interim Co-Director of the UC Berkeley AI Policy Hub.
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Lane D. Smith
Postdoctoral Scholar, Energy Science and Engineering
BioLane D. Smith is a postdoctoral scholar working with the Climate and Energy Policy Program at Stanford University. His research interests include energy policy, electricity rate design, energy affordability, and macro-energy systems (with a particular focus on the electric grid). Lane holds a Ph.D. and M.S. in Electrical Engineering from the University of Washington (2024 and 2019, respectively) and a B.S. in Electrical Engineering from the University of Denver (2018).
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Richelle Smith
Postdoctoral Scholar, Electrical Engineering
BioRichelle L. Smith is a Postdoctoral Scholar at Stanford University with Professor Tom Lee. She received the B.S. and M.S. degrees in Electrical Engineering-Electrophysics from the University of Southern California (USC) in 2017, and the M.S. and Ph.D. degrees in Electrical Engineering from Stanford University in 2019 and 2024, respectively.
Her research interests include analog/mixed-signal circuit design and energy-efficient systems, with a focus on phase-domain communications and computing. Recent projects encompass oscillatory computing for combinatorial optimization, quantum computing emulation with oscillator circuits, brain-inspired/neuromorphic circuit design, as well as wireline transceivers and phase-domain/edge modulation signaling.
She has acted as a technical consultant to Rambus Labs. She has held internship positions at Linear Technology, Rambus Labs, Stanford Brains in Silicon Lab, TDK-InvenSense, and Silicon Laboratories. She holds 5 U.S. patents. Dr. Smith serves as a Reviewer for IEEE Transactions on Circuits and Systems—Part I: Regular Papers, IEEE Transactions on Circuits and Systems—Part II: Express Briefs, and IEEE Journal on Emerging and Selected Topics in Circuits and Systems.
Selected Awards:
• SPOTLIGHT paper, IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS), 2024
• IEEE Solid-State Circuits Society (SSCS) Predoctoral Achievement Award, 2022–2023
• ARCS Foundation Northern California Fellowship (William K. Bowes, Jr. Foundation Scholar), 2022–2024
• Cadence Women in Technology Scholarship, 2021
• Analog Devices Outstanding Student Designer Award, 2019
• Stanford Graduate Fellowship (Sang Samuel Wang Scholar), 2017–2022
• NSF Graduate Research Fellowship, 2017–2022
• USC Discovery Scholar Prize, 2017
• Astronaut Scholarship, 2016
• Barry Goldwater Scholarship, 2016
• Tau Beta Pi Forge No. 42 Scholarship, 2015
• Rambus Innovator of the Future Scholarship, 2013
• USC Trustee Full Tuition Scholarship, 2013–2017 -
Charmaine Fay Carcallas Soco
Postdoctoral Scholar, Stem Cell Transplantation
BioCommunity Engagement Liaison serving the Stanford University Postdoctoral Association (SURPAS)
Co-chair of JEDI-SURPAS
https://surpas.stanford.edu/about/the-surpas-leadership-team/ -
Suihong Song
Physical Science Research Scientist, Energy Science & Engineering
Postdoctoral Scholar, Energy Science and EngineeringBioSuihong Song collaborates with Professor Tapan Mukerji at the Stanford Center for Earth Resources Forecast (SCERF) as a postdoctoral scholar. His research is centered on integrating machine learning with geosciences, specifically focusing on machine learning-based reservoir characterization and geomodelling, Physics-informed Neural Networks (PINNs) and neural operators as well as their applications in porous flow simulations, neural networks-based surrogate and inversion, decision-making under uncertainty, and machine learning-based geological interpretation of well logs and seismic data. These research endeavors have practical applications in managing underground water resources, oil and gas exploration, geological storage of CO2, and the evaluation of hydrothermal and natural hydrogen, among others.Song proposed GANSim, an abbreviation for Generative Adversarial Networks-based reservoir simulation, which presents a reservoir geomodelling workflow. This innovative approach has been successfully implemented in various 3D field reservoirs by international oil companies, including ExxonMobil.
