Stanford Doerr School of Sustainability
Showing 1-10 of 13 Results
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Sarah Dawn Saltzer
Managing Director of SCCS, Energy Science & Engineering
Current Role at StanfordManaging Director Stanford Center for Carbon Storage
Managing Director Stanford Carbon Initiative -
Celine Scheidt
Sr Res Engineer
BioCéline Scheidt has worked extensively in uncertainty modeling, sensitivity analysis, geostatistics and in the use of distance-based methods in reservoir modeling. She obtained her PhD at Strasbourg University and the IFP (France) in applied mathematics, with a focus on the use of experimental design and geostatistical methods to model response surfaces.
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Katrin Sievert
Graduate, Energy Science & Engineering
BioKatrin Sievert is a research associate and PhD candidate in the Climate Finance and Policy Group of Bjarne Steffen at ETH Zurich and a visiting researcher in the Environmental Assessment and Optimisation Group of Adam Brandt at the Doerr School of Sustainability and the Precourt Institute for Energy at Stanford University.
Her research focuses on the techno-economics of carbon dioxide removal (CDR), carbon capture and storage (CCS), and synthetic fuels. -
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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Suihong Song
Postdoctoral Scholar, Energy Resources Engineering
BioSuihong 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.
