Stanford Doerr School of Sustainability
Showing 81-100 of 139 Results
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Harshit Singh
Research Assistant, Woods Research Natural Capital Project
Staff, Woods Research Natural Capital ProjectBioHarshit Singh is an AI Researcher and Engineer working across generative AI, agentic systems, and environmental modeling. He is currently working on the Natural Capital Project at Stanford, where he develops LLM-driven workflows for the InVEST ecosystem to enhance automation, data integration, and sustainable development research. He is also building HarshanAI, an emotionally intelligent voice-AI companion.
Previously, he worked at Amazon Web Services, contributing to Bedrock Flows and AgentCore for large-scale generative AI systems and at the MIT-IBM Watson AI Lab, leading DiffuseKronA as first author and advancing parameter-efficient methods for personalized diffusion models. He has also supported climate and energy research at the Center for Global Sustainability, University of Maryland through the development of G-MAST, a global methane abatement solutions database. His work emphasizes practical innovation, scalable AI systems, and applying machine learning to real-world societal and sustainability challenges. -
Alyson Singleton
Ph.D. Student in Environment and Resources, admitted Autumn 2021
BioAly is a PhD student in the Emmett Interdisciplinary Program in Environment & Resources, investigating the impact of large-scale global change on infectious disease transmission and broader health dynamics. Based on the concepts of One Health and Planetary Health, she focuses on the design and evaluation of win-win solutions that can synergistically benefit human and environmental health. As we anticipate widening disease disparities under increasing climate and land-use change, her research aims to identify opportunities to prevent and mitigate these compounding harms. She approaches these topics by integrating novel computational methods, field-data collection, and epidemiologic techniques.
Prior to coming to Stanford, Aly was a Data Science Fellow at the Centers for Disease Control and Prevention where she developed analytic tools for outbreak detection and triage of multiple pathogens and supported the CDC’s Novel Coronavirus (COVID-19) Response. She also worked at the People, Place & Health Collective at the Brown University School of Public Health while earning her undergraduate (BS, Applied Mathematics) and master's degrees (MA, Biostatistics). -
Norman Sleep
Professor of Geophysics, Emeritus
Current Research and Scholarly InterestsPhysics of large-scale processes in the Earth
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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
Postdoctoral Scholar, Energy Science and 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.
