Andrew M. Stuart is a Professor at the California Institute of Technology's Division of Engineering and Applied Science. His research bridges computational mathematics, machine learning, and physical modeling, focusing on inverse problems, partial differential equations, and multiscale systems. He has pioneered methodologies integrating Gaussian processes, Kalman inversion, and neural operators for scientific computing. His recent publications highlight innovations in competitive protein dimerization networks, nonlinear Bayesian inference, and operator learning. Articles span applications in materials science, geophysics, and biochemical signal processing, emphasizing data-driven discovery of differential equations and scalable algorithms for high-dimensional problems. Stuart's work addresses challenges in structural error modeling, uncertainty quantification, and graph-based learning, with implications for climate modeling and dynamical systems. Despite extensive contributions, the scraped data does not specify students, awards, or contact details.
Peter K. Kitanidis is a Professor in the Department of Civil and Environmental Engineering and the Institute for Computational and Mathematical Engineering at Stanford University . His research focuses on groundwater flow , hydrologic forecasting , and stochastic inverse modeling , with applications to pollutant remediation and CO₂ storage monitoring . Education : Diploma, National Technical University of Athens (1974) M.S., MIT (1976) Ph.D., MIT (1978) Research Interests : Groundwater modeling and contaminant transport Hydraulic tomography and aquifer characterization Stochastic methods for uncertainty quantification Bioremediation and enhanced in-situ pollutant decay Dilution and mixing processes in heterogeneous media Real-time river flow forecasting Scientific Awards : L.G. Straub Award (1979) W.L. Huber Research Prize (1994) ISI Highly Cited Researcher (2001) AGU Hydrologic Sciences Award (2011) ASCE Pioneers in Groundwater Lecturer (2011) Advising and Grants : Advised 20+ PhD and MS students (1978–2018) Principal investigator on NSF, EPA, and DOE-funded projects Developed software for groundwater data analysis and CO₂ monitoring Contributed to bioremediation protocols and hydraulic tomography algorithms Labs and Teams : Kitanidis Laboratory for groundwater crisis solutions Collaborated with Oak Ridge National Laboratory and Stanford Hydrogeology Group Mentored postdocs (2000–2017) in reactive transport and inverse modeling
Xiaojing (Ruby) Fu is an Assistant Professor of Mechanical and Civil Engineering at the California Institute of Technology and a William H. Hurt Scholar (2024-present). Her research focuses on multiphase fluid mechanics in porous media, integrating theory, computation, experiments, and field observations to address geoscience and engineering challenges. Her educational background includes: B.S. in Engineering from Clarkson University (2011) M.S. from Massachusetts Institute of Technology (2015) Ph.D. from Massachusetts Institute of Technology (2017) Professor Fu's research centers on cryosphere hydrology, subsurface engineering, and phase transitions in porous media. She investigates multiphase flow dynamics in contexts like permafrost thaw, snow metamorphism, and carbon sequestration using phase-field modeling and experimental techniques. Her work bridges fundamental physics with applications in environmental resilience and energy systems, emphasizing predictive capabilities for large-scale phenomena through simplified multiscale theories. Analysis of her 15 most recent publications reveals intense focus on cryosphere processes (snow, permafrost) using advanced phase-field modeling and fiber-optic sensing. Key trends include freezing infiltration patterns, meltwater transport in layered snow, and seismic monitoring of soil moisture. Her work increasingly integrates field validation with computational models for environmental applications like drought monitoring and carbon sequestration. Her scientific recognition includes: William H. Hurt Scholar (2024) Professor Fu actively mentors graduate students, as evidenced by qualified students in her research group. She teaches core courses including Thermal Science (ME 11 abc) and Computational Methods for Flow in Porous Media (ME/CE/Ge/ESE 146), training students in both theoretical foundations and applied techniques for subsurface flow problems. She leads the Fu Research Group on Mechanics and Physics of Porous Media Flow, which develops multiscale theories to predict large-scale environmental and energy system behaviors. The group combines mathematical modeling, laboratory experiments, and field observations to address problems in geologic carbon storage, cryosphere dynamics, and subsurface resource management, with recent emphasis on climate change impacts and monitoring technologies.
Ian Main is a Professor of Seismology and Rock Physics at the University of Edinburgh since 2000, within the School of GeoSciences. Previously, he held roles as Reader (1996–2000) and Lecturer (1989–1996) in similar fields. He earned a BSc in Physics from the University of St Andrews, an MSc in Geophysics from the University of Durham, and a PhD in Seismology from the University of Edinburgh. His research focuses on quantifying natural hazards, catastrophic failure mechanisms (e.g., earthquakes, volcanic eruptions), and fluid-rock interactions. He explores these phenomena through complex systems theory and non-linear dynamics, with applications to subsurface processes and urban disaster resilience. Main has held leadership roles including Director of Research at the School of GeoSciences and membership in national/international bodies such as the Natural Environment Research Council Science Committee and the Royal Society of Edinburgh Research Committee. He contributed to high-profile initiatives like the UKRI GCRF Multi-Hazard Urban Disaster Risk Transitions Hub and the International Commission on Earthquake Forecasting. Notable awards include the Louis Néel Medal (2014) and the Ed Lorenz Lecture (2019). He has been a visiting scholar at institutions like Stanford University and the Centre for Mathematical Research, Barcelona.
Seth Blumsack is a Professor at the Pennsylvania State University in the Department of Energy and Mineral Engineering and serves as Director of the Center for Energy Law and Policy . He holds an Adjunct Research Professor position at the Carnegie Mellon Electricity Industry Center and is affiliated with the Santa Fe Institute as an External Faculty member. His research spans energy economics , power grid reliability , and complex infrastructure networks . Key projects include: Interdependent natural gas and electricity systems analysis Governance of regional transmission organizations Smart grid consumer behavior studies Power grid reliability tools development He has secured funding from the U.S. National Science Foundation , Department of Energy , Environmental Protection Agency , and private industry. His Best paper award at Hawai’i International Conference on System Sciences (2011) and John T. Ryan, Jr. Fellowship (2011-17) highlight his scientific recognition. Publications emphasize electricity market deregulation , energy infrastructure resilience , and consumer response to smart grid technologies . His work has been cited in major media outlets like The New York Times and The Los Angeles Times , and he has consulted for National Renewable Energy Laboratory , U.S. Department of Energy , and other industry stakeholders.
Jan Martin Nordbotten is a full-time Professor at the Department of Mathematics, University of Bergen (UiB), with adjunct positions at Princeton University and NORCE. His research focuses on applied mathematics, particularly in porous media, CO2 storage, fluid dynamics, and interdisciplinary applications in hydrology, biomedicine, and ecology. He completed his PhD at UiB in 2004 and became Norway's third youngest professor in 2007. His work emphasizes numerical methods, multiscale modeling, and experimental validation. Affiliations: UiB (full-time), Princeton (adjunct), NORCE (adjunct) Research Group: Center for Sustainable Subsurface Resources Research interests span mathematical modeling of subsurface processes, including flow in fractured media, geomechanics, and phase-field fracture. Notable contributions include analytical and numerical solutions for CO2 leakage, multiphase flow, and development of tools like DarSIA for image processing in porous media. Publications highlight advancements in mixed-dimensional models, finite element methods, and experimental validation of CO2 storage forecasts. His work bridges theoretical mathematics with practical applications in energy and environmental systems.
Prof. Dr. Oliver Schilling is an Assistant Professor of Hydrogeology at the University of Basel and affiliated with Eawag , the Swiss Federal Institute of Aquatic Science and Technology. He leads research on surface water-groundwater interactions using integrated surface-subsurface hydrological models (ISSHM) and novel tracer techniques including dissolved atmospheric noble gases, environmental DNA, and radioactive tracers. Research Focus : Surface-subsurface hydrology, ecohydrology, groundwater-surface water interactions, tracer hydrogeology, climate change adaptation in water systems Key Projects : Integrated Hydrological Modeling for Operational Forecasting, Sustainable Nitrogen Fertilization, Slow Water Initiatives, Cryosphere-Groundwater Connectivity in Alpine Regions Scientific Contributions include developing HGS-PDAF (modular data assimilation framework), advancing microbial transport algorithms in HydroGeoSphere, and pioneering online flow cytometry for microbial analysis in high-turbidity environments. His work addresses drinking water production via bank filtration along alluvial rivers, critical for 30% of Swiss and 50% of European drinking water supply. Scientific Leadership : Editor, Hydrogeology Journal (since 2025) Associate Editor, Frontiers in Water (since 2021) Coordinator, Swiss Water-Earth Systems (WES) PhD School (2020–2022)
Professor Anne Verhoef is a leading academic at the University of Reading, specializing in environmental and hydrological sciences. Her research focuses on soil-plant systems, climate change impacts, groundwater dynamics, and remote sensing applications in ecological modeling. She collaborates internationally on projects like GEWEX and AMMA, advancing understanding of global hydrological cycles and land-atmosphere interactions. Key Research Areas: Hydrological modeling and prediction Climate change adaptation in semi-arid regions Soil health and pedotransfer functions Evapotranspiration dynamics in tropical ecosystems Remote sensing for biodiversity and water resource assessments Her work integrates field observations, satellite data, and numerical models to address challenges in water resource management, flood mitigation, and sustainable agriculture. She has published widely in top journals such as Reviews of Geophysics , Water Resources Research , and Nature Reviews Earth & Environment . Professor Verhoef contributes to interdisciplinary initiatives, including climate adaptation strategies in transboundary regions and improving land surface models for global Earth system simulations. Her research emphasizes bridging gaps between observational data and predictive frameworks to inform policy and environmental decision-making.
Brandon Schmandt is a Professor in the Department of Earth, Environmental and Planetary Sciences at Rice University, where he leads research using seismology to investigate Earth systems. His work integrates interdisciplinary approaches, data science, and numerical modeling to study tectonic processes, magmatic systems, and environmental interactions. His educational background includes a PhD in Geological Sciences from the University of Oregon (2011) and a BA in Environmental Studies from Warren Wilson College (2006). Dr. Schmandt's research focuses on seismology, tectonics, volcanology, and surface processes , with emphasis on seismic imaging of subsurface structures. His group employs innovative time-series analysis and field projects to resolve geologic history and contemporary Earth dynamics, particularly examining fault zones, magmatic reservoirs, and deep convective processes. Key methodologies include dense seismic arrays and machine learning applications. Analysis of his recent publications (2023-2025) reveals dominant trends in seismic event discrimination (earthquakes vs. explosions), magmatic system imaging (Yellowstone, Cascades), and global mantle structure studies. There is strong emphasis on induced seismicity, machine learning applications, and high-resolution imaging of Earth's discontinuities using dense arrays. His distinguished honors include: Aki Award of the AGU Seismology Section GSA Donath Medal AGU Macelwane Medal Body Dr. Schmandt directs an active research group conducting field projects across diverse settings including the Raton Basin, Yellowstone, Antarctica, and the Caribbean. While specific student advisees and grant details aren't provided in available materials, his group's work involves collaborative data collection, advanced computational modeling, and development of novel seismic analysis techniques applicable to both natural and anthropogenic seismic sources. The research program maintains focus on magmatic systems beneath volcanic regions, induced seismicity mechanisms, and global mantle structure using dense node arrays and interdisciplinary approaches to address fundamental questions in Earth dynamics.
Chaopeng Shen is a Professor in the Department of Civil and Environmental Engineering at Pennsylvania State University. His research bridges hydrology with state-of-the-art deep learning and differentiable modeling techniques, focusing on advancing our understanding of hydrologic cycles and their interactions with ecosystems, energy, and carbon cycles. He leads the Multi-scale Hydrology, Processes and Intelligence group (MHPI) and has developed the Process-based Adaptive Watershed Simulator (PAWS) for large-scale hydrologic modeling. Shen's work emphasizes physics-informed machine learning , where deep learning components are integrated with process-based equations through differentiable modeling. This approach enables training neural networks using big data while respecting physical laws, leading to improved generalizability and robustness. His group has demonstrated advantages of differentiable models in rainfall-runoff prediction, routing, ecosystem modeling, and water quality studies. Notably, his team's deepLDB project addresses landslide prediction using AI and big datasets. Recent publications highlight his contributions to global water modeling (grid-LSTM, differentiable Muskingum-Cunge routing), extreme flood forecasting (probabilistic diffusion models), and hydrologic uncertainty quantification . Shen actively engages in interdisciplinary collaborations through the PRISM Cooperative Institute, which aims to integrate multi-domain data for systemic risk assessment. His group has advised students including Dapeng Feng, Wen-Ping Tsai, Kuai Fang, Xinye Ji, and Tasnuva Mahjabin. Shen's research is supported by the National Science Foundation (NSF), Department of Energy (DoE), USGS, Google.org, and the Gates Foundation. He serves as Editor for the Journal of Geophysical Research - Machine Learning & Computation and Chief Editor for Frontiers in Water: Water & AI. His open-source software tools like PAWS and deepLDB are available through dedicated project websites.
Jiaxuan Li is an Assistant Professor of Geophysics in the Department of Earth and Atmospheric Sciences at the University of Houston's College of Natural Sciences and Mathematics. His research focuses on developing fiber-optic sensing technologies for seismic monitoring across diverse geological environments including volcanic, crustal, and glacial settings. Dr. Li's educational background includes a Ph.D. in Geophysics from the University of Houston (2015-2020) and a B.S. in Geophysics from Peking University (2011-2015). He previously held a postdoctoral position at Caltech Seismolab under Prof. Zhongwen Zhan. His research program centers on distributed acoustic sensing (DAS) applications, with major contributions in volcanic eruption forecasting through minute-scale magma migration imaging, earthquake rupture dynamics via high-frequency fault asperity analysis, and subsurface characterization for carbon sequestration and geothermal energy. Recent work demonstrates DAS capabilities as dense geodetic arrays for real-time volcanic monitoring systems deployed in Iceland through collaborations with the Icelandic Met Office and Reykjavik University. Analysis of Dr. Li's publication record reveals a strong emphasis on operationalizing fiber-optic networks for geophysical monitoring, with significant advancements in eruption early warning systems, earthquake source characterization, and subsurface imaging techniques. His work bridges fundamental seismological research with practical hazard mitigation applications. Dr. Li actively mentors graduate students and recently welcomed postdoc Dr. Tianfan Yan to his research team. His lab operates real-time DAS streaming systems for volcanic eruption monitoring in Iceland, developed through international collaborations involving the University of Houston, Caltech, Ljósleiðarann, and Reykjavik University. Current research directions include expanding DAS applications for carbon sequestration verification and deep geothermal reservoir characterization.
Ehsan Modiri is a researcher at the Department of Hydrosystem Modelling , Helmholtz Centre for Environmental Research (UFZ), Germany. His work focuses on climate change impacts on hydrological systems, drought monitoring, and environmental modeling using advanced computational frameworks. Affiliation: UFZ - Helmholtz Centre for Environmental Research Department: Hydrosystem Modelling Research Themes: Climate Change, Droughts, Hydrological Forecasting, Water Resource Management Research Interests: Modiri specializes in understanding hydrological responses to climate change, particularly in drought dynamics and soil moisture variability. His work bridges observational data with sophisticated modeling frameworks to improve predictability of water balance components under warming scenarios. Scientific Contributions: Recent publications highlight his role in developing high-resolution drought simulations, evaluating hydrological model performance, and analyzing groundwater responses to global warming. He participates in large-scale European hydrological projects and collaborates on climate-hydrology integration initiatives.
Anne Sheehan is Professor of Geological Sciences at the University of Colorado Boulder and a Fellow of the Cooperative Institute for Research in Environmental Sciences (CIRES). Her research integrates seismology, geodesy and electromagnetics to investigate earthquake sources, subduction-zone dynamics, tsunami generation and human-induced seismicity. Education Ph.D., Massachusetts Institute of Technology, 1991 B.S., University of Kansas, 1984 Research Interests Sheehan’s group pursues field and computational studies of earthquake seismology , tsunami processes , active tectonics and geophysical imaging . Major themes include: Deep structure and tectonics of mountain belts (Rockies, Himalaya, Southern Alps) Subduction-zone seismicity, slow-slip events and tsunami hazards Induced seismicity linked to wastewater injection in Colorado Novel seafloor instrumentation (ocean-bottom seismometers, pressure gauges) and satellite-based tsunami detection from ships Advanced computational techniques such as data assimilation, interferometry and machine-learning phase detection Scientific Awards Erasmus Haworth Distinguished Alumni Award, University of Kansas (2009) EarthScope Distinguished Lecturer (2013) College Scholar Award, University of Colorado (2014) New Zealand Geophysics Prize (2016, 2019) President, Seismology Section, American Geophysical Union (2020) Leadership, Grants & Teams Sheehan is Principal Investigator on major NSF experiments such as the Alaska Amphibious Community Seismic Experiment (AACSE) and the Hikurangi Ocean Bottom Investigation of Tremor and Slow Slip (HOBITSS) . She collaborates closely with the hydrogeology group of Prof. Shemin Ge and geodesist Prof. Kristy Tiampo, and mentors graduate students and post-docs in the Sheehan Geophysics Group . The group maintains real-time seismic data access for Colorado at earthquake.colorado.edu and hosts weekly seminars and reading groups.
Mustafa Onur is the McMan Professor and Chair of Petroleum Engineering at The University of Tulsa, where he directs the TU Petroleum Reservoir Exploitation Projects (TUPREP). He holds a Ph.D. and M.S. in Petroleum Engineering from The University of Tulsa and a B.S. from Middle East Technical University. Previously, he held professorships at Istanbul Technical University and Universiti Teknologi Petronas (Malaysia), including a Schlumberger Chair position. Research Focus: Dr. Onur specializes in inverse problem theory, mathematical optimization, and data science applied to reservoir management, geothermal systems, and uncertainty quantification. His work integrates machine learning with traditional reservoir engineering to solve complex problems in energy extraction and carbon sequestration. Publication Trends (2024-2025): His 15 most recent articles emphasize deep learning-based reservoir surrogates, CO₂ storage optimization, geothermal energy extraction, and constrained production optimization. Key innovations include Embed-to-Control frameworks, physics-driven interwell simulators, and stochastic optimization algorithms for uncertainty management in subsurface systems. Awards & Recognition: 2010 SPE Formation Evaluation Award 2014 SPE Distinguished Member 2018 SPE Reservoir Description and Dynamics Award Leadership: As TUPREP director, he leads advanced research in reservoir exploitation, focusing on practical applications of AI and optimization in petroleum and geothermal engineering. He serves as Associate Editor for SPE Journal and Journal of Petroleum Science and Engineering .
Dr. Jonathan Frame is an Assistant Professor of Artificial Intelligence/Machine Learning in Geological Sciences at the University of Alabama (2024–present) and a Faculty Fellow at the Alabama Water Institute (2024–2027). He holds a PhD in Geological Sciences from the University of Alabama (2022), an MS in Civil Engineering from the University of California, Irvine (2011), and a BS in Earth Systems Science, Technology, and Policy from California State University, Monterey Bay (2010). His research focuses on advancing hydrologic modeling through machine learning, including deep learning for streamflow forecasting, geospatial modeling, and flood prediction systems. Notable projects include improving the National Water Model with LSTM networks and developing rapid inundation mapping techniques using satellite data. He has contributed to over 30 peer-reviewed publications and actively participates in conferences like AGU and NeurIPS. His engineering experience spans flood risk mitigation, groundwater analysis, and pipeline transient modeling across California, Texas, and Washington. Research Interests Machine learning integration in hydrological systems Operational flood forecasting and inundation mapping Data-driven approaches for ungauged basins Climate nonstationarity and model adaptability Hydraulic transient analysis in water infrastructure Recent Contributions Frame’s recent work emphasizes NextGen water modeling frameworks, combining physics-based models with AI to enhance predictive accuracy. His 2025 paper on heterogeneous water modeling frameworks and 2024 studies on rapid inundation mapping highlight innovations in integrating satellite observations with hydrologic models. He also explores topics like mass conservation constraints in rainfall-runoff models and evapotranspiration prediction using deep learning. Grants & Projects FEMA partnership for near-real-time flood damage prediction systems NOAA-funded research on AI in environmental sciences NASA snowpack analysis for water resources forecasting Development of the Tarsier environmental modeling framework Labs & Collaborations Frame collaborates with the Alabama Water Institute and contributes to interdisciplinary teams advancing hydrologic AI. His work intersects with climate science, environmental engineering, and computational hydrology to address global water challenges.
