Stanford Doerr School of Sustainability
Showing 1,221-1,230 of 1,462 Results
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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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Olav Solgaard
Audrey S. Hancock Professor in the School of Engineering
BioThe Solgaard group focus on design and fabrication of nano-photonics and micro-optical systems. We combine photonic crystals, optical meta-materials, silicon photonics, and MEMS, to create efficient and reliable systems for communication, sensing, imaging, and optical manipulation.
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George Somero
David and Lucile Packard Professor in Marine Science, Emeritus
Current Research and Scholarly InterestsWe examine two aspects of organism-environment interactions: How does stress from physical (e.g., temperature) and chemical (oxygen levels, pH) factors perturb organisms and how do organisms respond, adaptively, to cope with this stress? We examine evolutionary adaptation and phenotypic acclimatization using a wide variety of marine animals, including Antarctic fishes and invertebrates from intertidal habitats on the coastlines of temperate and tropical seas.
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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.
