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Doss-Gollin Research Group

Our infrastructure was designed for a world that no longer exists. Today, these systems face converging pressures from climate extremes, aging assets, and demographic shifts.

Our research vision is to advance climate risk analysis and adaptive planning for complex, interdependent infrastructure systems.

To achieve this, we integrate Earth science, risk analysis, and probabilistic machine learning to research three interrelated questions. First, how can we understand and simulate nonstationary hydroclimatic hazards and quantify relevant uncertainties given imperfect physical models and short observational records? Second, how do these hazards create risks to critical infrastructure systems and the communities that rely on them? Third, what adaptation strategies can robustly meet multiple, potentially competing, performance objectives, given deep uncertainty in future conditions? We build open-source tools and actionable insights to tackle critical challenges in urban flood resilience, energy reliability, and catastrophe modeling.

Dr. James Doss-Gollin is an Assistant Professor in the Department of Civil and Environmental Engineering at Rice University.

Read his full bio →

Date Title
2025-12-30 Doss-Gollin Lab at AGU 2025
2025-09-23 New Paper on Diffusion for Precipitation Downscaling Published in IEEE TGRS
2025-09-02 CERCAT Funding for Tropical Cyclone Hazard Assessment Project
2025-08-26 New Paper on Bayesian Spatiotemporal Analysis of Extreme Rainfall in Environmental Research: Climate
2025-07-08 Updated Thoughts on the Guadalupe Flood
2025-06-18 Call for Abstracts: AGU Fall Meeting 2025 Sessions
2025-06-01 James on Telemundo Special Bajo la Amenaza del Golfo
2025-04-30 Congratulations to Yuchen Lu for Passing Her Thesis Proposal!
2025-04-02 Join Us at the Texas Climate Conference 2025
2025-03-03 Successful Nature-Based Solutions Workshop at Rice University
2025-02-05 Bayesian Model Reveals Rising Extreme Rainfall on Gulf Coast
2025-02-02 NSF Grant to Study Flood Resilience in Rural Texas
2024-12-05 Doss-Gollin Lab at AGU 2024
2024-08-23 Announcing AI for Climate Risk and Resilience at Rice
2024-08-22 Welcome Dongwook!
2024-06-09 Pluvial Flood Emulation with Hydraulics-informed Message Passing accepted to ICML 2024
2024-05-14 How extreme was the recent San Jacinto River rainfall?
2024-03-27 Video for World Water Day / ASU Water Institute Kickoff
2024-03-27 James gives seminar at UNC Chapel Hill
2024-03-18 Paper on solar and wind potential during temperature extremes published in Environmental Research Letters
2024-01-01 Welcome Jonah!
2023-12-11 Catch us at AGU 2023!
2023-12-10 New preprint on transparency in peer-reviewed climate change research
2023-11-11 James presents at STAHY 2023
2023-11-01 Paper on trends in heating and cooling demand published in Communications Earth & Environment
2023-10-13 James Presents at SSPEED Center Conference
2023-08-16 Published: review paper on climate risks to electricity systems
2023-08-14 New student advising booklet for undergraduate environmental education at Rice
2023-07-12 Published: theorizing modular, adaptive, and decentralized water systems
2023-04-23 New paper on mesoscale modeling of distributed water systems
2023-01-03 Paper bridging DMDU and probabilistic frameworks published in Earth’s Future
2022-11-08 Undergraduate Research Opportunity: AI for Sustainability and Resilience to Natural Disasters
2022-02-08 Yuchen Lu wins 2022 H.W. Reeves Endowed Scholarship
2021-12-08 Talk at Risk KAN Compound Events Group
2021-09-01 Houston Chronicle Op-Ed: The risks of climate change are great - so are the rewards of solving it
2021-08-27 NSF Award from EAGER SAI: ‘Participatory Design for Water Quality Monitoring of Highly Decentralized Water Infrastructure Systems’
2021-06-24 Call for Abstracts: Statistical-Dynamical Models for Hydroclimatic Risk Management and Adaptation
2021-05-18 How Unprecedented Was the February 2021 Texas Cold Snap? published in Environmental Research Letters
2020-11-10 2020 Annual Meeting of the Society for Decision Making under Deep Uncertainty
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    Rice University
    Department of Civil & Environmental Engineering
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