Stanford Doerr School of Sustainability
Showing 81-90 of 113 Results
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Ashley Prow Fleischer
Postdoctoral Scholar, Earth and Planetary Sciences
BioI earned a PhD in Earth Science with a focus in paleoclimatology from Syracuse University, where my research focused on reconstructing past environmental change and biotic responses using microfossils and geochemical proxies. My work integrates stratigraphy, paleobiology, geochemistry, and climate modeling to better understand Earth's climate dynamics during the intervals of rapid change, like the Late Devonian and end Triassic mass extinctions.
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Chenfei Qu
Postdoctoral Scholar, Environmental Social Sciences
BioChenfei Qu’s research focuses on climate change economics, including emissions trading systems, carbon pricing, air pollution, and integrated assessment of climate and energy policies, with an emphasis on general equilibrium modeling for policy analysis in developing countries. She holds a Ph.D. in Management Science and Technology and a Bachelor’s degree in Environmental Management, both from Tsinghua University. Chenfei Qu was a visiting scholar at the ZEW–Leibniz Centre for European Economic Research in 2024. Her work has been published in journals such as Climate Change Economics, Advances in Climate Change Research, and Environmental Science & Technology.
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Lisa Rennels
Postdoctoral Scholar, Environmental Social Sciences
Biopersonal website (more frequently updated): lisarennels.com
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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.
