Stanford Doerr School of Sustainability
Showing 51-100 of 499 Results
-
Hilary Brumberg
Ph.D. Student in Environment and Resources, admitted Autumn 2024
BioHilary Brumberg (she/her) is a PhD student in the Emmett Interdisciplinary Program in Environment and Resources (E-IPER) at Stanford. She is an interdisciplinary environmental scientist, conservation practitioner, and National Science Foundation Graduate Research Fellow (NSF GRFP) with extensive experience researching and implementing Natural Climate Solutions (NCS) across the tropics. She studies socioeconomic, financial, political, and ecological dimensions of NCS implementation. Hilary spent four years managing community-based restoration projects while living at a research station deep in the Costa Rican rainforest, originally as a Princeton in Latin America Fellow. She has consulted for diverse international conservation organizations, including The Nature Conservancy (TNC), World Wildlife Fund (WWF), and the Governors' Forest and Climate Task Force. Her work has been featured in National Geographic, Living on Earth on NPR, Mongabay Latam, and NASA DEVELOP. Hilary holds an M.S. in Environmental Studies with a Data Science Statistics Certificate from the University of Colorado Boulder as a USDA NNF Fellow, as well as a B.A. in Earth Science and Spanish from Wesleyan University.
-
Stephanie Caddell
Ph.D. Student in Oceans, admitted Autumn 2024
Graduate Student Coordinator, Stanford Doerr School of Sustainability - Dean's OfficeBioStephanie Caddell graduated with a bachelor's degree in environmental science from the University of North Carolina at Chapel Hill with minors in marine science and environmental justice. While at UNC, she researched marine microbiology, fisheries dynamics, and marine ecosystem dynamics in Ecuador and the Galapagos. Additionally, she has researched bycatch mitigation efforts in the North Atlantic for sea turtle species with the National Oceanographic and Atmospheric Administration. Now working on her PhD in the Oceans Department under the mentorship of Dr. Larry Crowder and Dr. Nicole Ardoin, Stephanie studies how relationships with marine resources shape stewardship. Her research sits at the intersection of marine social science and policy, examining how and why people care for local ecosystems and how governance can support—or hinder—this care. Her work aims to inform marine management policies grounded in local realities, ensuring coastal and island communities are equitably engaged at every stage of decision-making.
-
Catherine (Hay) Callas
Ph.D. Student in Energy Resources Engineering, admitted Spring 2020
BioCatherine Callas is a Ph.D. candidate in the Benson Lab in Energy Resources Engineering. She is an ExxonMobil Emerging Energy Fellow, and her research is focused on offshore carbon capture and sequestration in the Gulf Coast. She obtained her M.S. degree in the Atmosphere and Energy program within Civil and Environmental Engineering from Stanford University and a B.S. degree in Chemical Engineering from Brown University. Before attending Stanford, she worked as a Financial Analyst within the Fixed Income group at Goldman Sachs in New York City for three years. She was a Schneider Fellow at the Natural Resources Defense Council in San Francisco where she analyzed the impact of the 2017 Northern California wildfires and 2018 Camp Fire on retail rates within PG&E’s service territory.
-
Eeshan Chaturvedi
Ph.D. Student in Environment and Resources, admitted Autumn 2022
BioEeshan is currently pursuing his Ph.D. in Climate Governance, and its correlations with policy, law, and earth systems. He holds an LLM in Environmental Law and Policy from Stanford Law School and has since worked with various domestic and international organizations on legal and management issues. In academia, he has held positions of Assistant Dean and Professor of Environmental Governance and continues to engage with the various stakeholders in the space.
He enjoys discussions on neuroscience, astrophysics, and geo-politics in his free time. -
Zhenlin Chen
Ph.D. Student in Energy Science and Engineering, admitted Summer 2023
BioZhenlin (Richard) Chen is a Ph.D. candidate at Stanford's Adam Brandt lab, focuses on greenhouse gas emissions from oil and gas. His work primarily revolves around evaluating ground sensor technologies for methane detection and quantification ability. His methodological approach blends engineering principles, field data collection, and applied statistics. Chen is exploring AI-driven frameworks, particularly large language models, to refine energy data extraction and enhance the OPGEE model through private data fine-tuning and reinforcement learning. His emphasis remains on domain-specific tasks, aiming for efficiency in terms of latency and cost. He pursued his undergraduate studies in environmental science at Cornell University and holds a master's in Atmosphere and Energy Engineering from Stanford.
-
Pedro Cintra
Ph.D. Student in Environmental Social Sciences, admitted Autumn 2025
BioTrained as a physicist until my MSc, in which I worked with neutrino detection of core collapse supernovae, I recently switched fields to apply mathematical and computational models to ecological and social systems :)
On the ecological side, I like working with individual based models for cooperation and foraging strategies from an evolutionary perspective. On the social side, I am currently interested in the evolution of cultural values on groups of humans and polarization of opinions on networks of contacts. -
Dylan Marshall Crain
Ph.D. Student in Energy Resources Engineering, admitted Autumn 2022
Current Research and Scholarly InterestsMy current research revolves around optimizing the monitoring design of Carbon Capture and Sequestration (CCS) projects in such a way that the posterior (after data assimilation) predictions are as close to reality as can be hoped for.
In CCS projects within the U.S., it is important to have monitoring plan, which can consist of wells with pressure, saturation, salinity, et cetera sensors, seismic lines, or gravimetric above-ground measurements, before any injection has begun into the subsurface. This is due to the permitting requirements that must be satisfied before operations are begun.
Due to this constraint, any monitoring optimization (at least initially) needs to be determined using only a prior (highly uncertain) understanding of the subsurface. This makes the optimization much more challenging. We utilize a prior optimization scheme from a previous student which allows us to optimize a monitoring plan using only prior information to get the minimized, expected uncertainty reduction in the posterior models for a given quantity of interest. This scheme is limited by some Gaussian assumptions. We optimize it using a genetic algorithm.
From this point, with the monitoring plan established, the information gathered from the optimized monitoring scheme (using only monitoring wells at the moment) is used to history match (data assimilate) our understanding of the subsurface. The results can be used to predict the CO2 plume flow and behavior into the future.
This work was initially developed to assist a project in Illinois that is currently seeking Class VI injection well permits in the self-same state in order to begin injecting CO2 produced from two companies paying for the work from the Illinois Geological Survey.
