Stanford University
Showing 28,581-28,590 of 34,930 Results
-
Peiyang Song
Affiliate, Psychology
BioPeiyang Song is a rising senior studying Computer Science at California Institute of Technology (Caltech), advised by Prof. Steven Low, with a minor in Robotics advised by Prof. Günter Niemeyer. He is a researcher in Berkeley AI Research (BAIR) Lab, advised by Prof. Dawn Song and Dr. Jingxuan He. He also works in Stanford AI Lab (SAIL), advised by Prof. Noah Goodman and Dr. Gabriel Poesia in the Computation & Cognition Lab (CoCoLab). His current research interest is mainly in LLM reasoning, especially neuro-symbolic AI for formal math and verifiable code generation. In the past, he also published on neuro-symbolic methods for energy-efficient ML systems and neural machine translation.
-
Shuran Song
Assistant Professor of Electrical Engineering and, by courtesy, of Computer Science
BioShuran Song is an Assistant Professor of Electrical Engineering at Stanford University. Before joining Stanford, she was faculty at Columbia University. Shuran received her Ph.D. in Computer Science at Princeton University, BEng. at HKUST. Her research interests lie at the intersection of computer vision and robotics. Song’s research has been recognized through several awards, including the Best Paper Awards at RSS’22 and T-RO’20, Best System Paper Awards at CoRL’21, RSS’19, and finalists at RSS, ICRA, CVPR, and IROS. She is also a recipient of the NSF Career Award, Sloan Foundation fellowship as well as research awards from Microsoft, Toyota Research, Google, Amazon, and JP Morgan.
To learn more about Shuran’s work, please visit: https://shurans.github.io/ -
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.
