Dr. Leanne Archer is a Researcher in the School of Geographical Sciences at the University of Bristol, specializing in hydrology and climate change impacts. Her work focuses on flood risk assessment in Small Island Developing States, extreme rainfall events, and the application of convection-permitting climate models. She collaborates with experts like Prof. Paul Bates and Dr. Jonty Rougier on interdisciplinary projects addressing tropical cyclone hazards and climate adaptation strategies. Her research interests include analyzing flood exposure in vulnerable regions, soil moisture dynamics in urban flooding, and improving global flood forecasts for humanitarian operations. Archer’s publications emphasize the implications of climate change on rainfall-driven disasters, particularly in Puerto Rico and East Africa, using high-resolution climate projections to evaluate future risks under 1.5° C and 2° C warming scenarios. Recent work explores innovations like the Surface Water Ocean Topography Mission for flood modeling and evaluates the suitability of TanDEM-X data for inundation studies in island nations. Her contributions bridge environmental science with policy, aiming to enhance disaster preparedness and resilience in climate-sensitive regions.
Ann B. Lee is a Professor and Co-Director of the PhD Program in Statistics at Carnegie Mellon University , with a joint appointment in the Department of Statistics & Data Science and the Machine Learning Department. Prior to joining CMU, she held positions as a J.W. Gibbs Assistant Professor at Yale University and a visiting research associate at Brown University. PhD in Physics, Brown University MSc/BSc in Engineering Physics, Chalmers University of Technology, Sweden Her research focuses on statistical methodology for complex data in the physical sciences , emphasizing trustworthy inference, uncertainty quantification, and integration of classical statistics with machine learning. Recent work includes likelihood-free inference, calibrated forecasting, and diagnostics for generative models. The STAMPS research group , which she co-founded in 2018, hosts weekly meetings and public webinars. In Fall 2024, STAMPS will transition into a CMU Research Center. Recent publications span likelihood-free inference , climate modeling , and astronomy . Notable collaborations include applications to hurricane intensity guidance , galaxy redshift estimation , and cosmological parameter biases . She mentors PhD students and has advised multiple award-winning researchers, including ASA Best Student Paper Award winners. Her teaching includes advanced courses on probability, regression, and AI for climate sciences.
Sebastian Schemm is a Heisenberg Fellow at the Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, a position regarded as equivalent to a non-permanent Associate Professor. He leads research within the Atmosphere-Ocean Dynamics group and previously held an ERC Starting Grant-funded Assistant Professorship (without tenure track) at ETH Zurich. Education and Career Path PhD (2013) and MSc (2010), ETH Zurich, Switzerland Postdoctoral researcher, University of Bergen, Norway (2014–2017) Postdoctoral researcher, Laboratoire de Météorologie Dynamique, ENS Paris (2017–2018) Assistant Professor (ERC Starting Grant), ETH Zurich (2020–2024) Heisenberg Fellow, DAMTP, University of Cambridge (2025–present) Research Focus Schemm’s work centres on atmospheric and climate dynamics, spanning turbulence to planetary scales. Core themes include the physics of extratropical cyclone life cycles, jet-stream and storm-track dynamics, Rossby waves and teleconnection patterns, high-resolution atmospheric modelling, and the integration of machine-learning techniques for parameter estimation, data assimilation, and kilometre-scale global simulations. He also contributes to large-scale initiatives such as ECMWF’s WeatherGenerator. Scientific Awards and Editorial Service DFG Heisenberg Fellowship (2025) ERC Starting Grant (2020–2024) European Meteorological Society Young Researcher Medal (2019) Co-Editor, Weather and Climate Dynamics (EGU) Co-Editor, Quarterly Journal of the Royal Meteorological Society PhD Supervision & Funding He currently supervises PhD students at both Cambridge and ETH Zurich, with funding streams including the Cambridge CREATES Doctoral Training Partnership and Swiss/EU grants. Ongoing students explore reinforcement-learning parameterisations, jet-stream–storm-track relationships, mid-latitude eddy energetics, machine-learning ensemble forecasting, and Bayesian parameter estimation in LES. Active Projects EU Horizon project WeatherGenerator (led by ECMWF) PASC HiRAD-Gen : High-Resolution Atmospheric Downscaling Using Generative Models
Jim Hall is a Professor of Climate and Environmental Risk at the University of Oxford's School of Geography and the Environment, and serves as Director of Research there. He is also a Visiting Fellow at Linacre College and holds leadership roles including Chair of the Science Advisory Committee at IIASA, and Expert Advisor to the UK's National Infrastructure Commission. His work focuses on systemic risk analysis, infrastructure resilience, and policy implications of climate change adaptation. Prof Hall has pioneered methodologies like the National Infrastructure Systems Model (NISMOD) and chairs the Data and Analytics Facility for National Infrastructure (DAFNI). His research spans flood risk management, energy systems decarbonization, and transboundary water resource conflicts in regions such as the Eastern Nile Basin and the Caribbean. Key research areas include robust decision making under uncertainty, info-gap theory applications, and integrated assessments of human-environmental systems. He has contributed to major international assessments, including the IPCC's Fourth Assessment Report, and developed frameworks for multi-hazard stress testing of infrastructure networks. Scientific Awards: George Stephenson Medal (2001), Prince Sultan Prize for Water (2018), Royal Academy of Engineering Fellowship (2010) His advising and grants work includes mentoring a DPhil student Erin Canning and leading projects like MARIUS and ENHANCE. He has also developed innovative modeling tools for coastal erosion prediction and probabilistic assessments of global shipping fuel transitions. Prof Hall’s research groups actively engage in interdisciplinary projects, including the Oxford Martin Programme on Resource Stewardship and the UK Infrastructure Transitions Research Consortium. His work emphasizes bridging scientific analysis with actionable policy solutions for climate adaptation.
Peter Huybers is a Professor of Earth and Planetary Sciences and Environmental Science and Engineering at Harvard University , where he investigates the climate system and its societal implications, including interactions between volcanism and glaciation , extreme temperature predictability , and climate change impacts on food production . Research interests span climate change attribution , paleoclimate reconstruction , drought dynamics , crop yield modeling , and earth system feedbacks . His work often integrates art-historical analysis with climate science, as seen in studies of 19th-century air pollution through Turner and Monet paintings . Scientific awards include funding from Harvard Data Science Initiative (2023) for projects on climate change and food supply volatility Amazon Web Services (2023) grant His 20+ peer-reviewed articles since 2020 focus on climate proxies , hydrological modeling , solar forcing , and agricultural-climate interactions , with recent work in Nature , PNAS , and Science Advances . Advising : Mentored 10+ PhD students including Parker Liautaud , Duo Chan , and Marena Lin , while leading research teams with current members like Greta Berendes and Caro Park . Former staff include Jon Proctor and Lucas Vargas Zeppetello , the latter now at UC Berkeley (2024).
Dr. Dominik Büeler is a Researcher at ETH Zurich's Institute for Atmospheric and Climate Science and staff member of the Center for Climate Systems Modeling (C2SM). His work bridges atmospheric dynamics with practical climate services, focusing on subseasonal prediction systems and their societal applications in Europe. Research Focus: Büeler's work centers on subseasonal-to-seasonal prediction, with emphasis on weather regime dynamics, extratropical cyclone behavior, and stratosphere-troposphere interactions. His research integrates large ensemble modeling, forecast verification, and climate impact assessment, particularly for European weather extremes. Recent projects examine heatwave mortality prediction, energy meteorology applications, and the role of moist processes in atmospheric blocking. Analysis of his publication record since 2021 reveals consistent advancement in subseasonal forecasting methodology, with growing emphasis on societal applications including public health (heat-related mortality) and energy sectors. His work increasingly connects fundamental atmospheric processes with operational forecasting systems, leveraging collaborations through the Subseasonal-to-Seasonal Prediction Project. Affiliations: Center for Climate Systems Modeling (C2SM) - Core Research Staff ETH Zurich Institute for Atmospheric and Climate Science MeteoSwiss Collaborator (Energy Meteorology) Büeler contributes to multidisciplinary teams developing climate services, with recent work supporting Swiss operational forecasting systems. His research group within C2SM focuses on improving subseasonal predictability through advanced diagnostics of model biases and atmospheric processes.
Jonathan L. Rogers is a Professor in the Accounting Department at Leeds School of Business, University of Colorado Boulder. He maintains his office in Koelbel Building, room 433, and can be contacted at jonathan.rogers@colorado.edu or by phone at 303-735-6620. Dr. Rogers received dual bachelor's degrees from the University of Texas in 1996: one in Business Administration with a focus in finance, and another in Economics with a minor in accounting. He earned his PhD in Accounting from the Wharton School of the University of Pennsylvania in 2005. He is also a certified management accountant and certified in financial management, though both certifications are currently inactive. Dr. Rogers' research focuses on voluntary disclosure, market microstructure, multinational firms, insider trading, and stock return volatility . His work has been published in all three top accounting journals (Journal of Accounting Research, Journal of Accounting and Economics, The Accounting Review) and has received significant attention from major media outlets including The Wall Street Journal, The New York Times, Financial Times, Fortune, Reuters, Bloomberg TV, and CNBC. His research has also been cited by members of Congress. His recent publications span a diverse range of topics from accounting and finance to meteorology and healthcare, reflecting interdisciplinary collaborations. Major themes in his work include financial disclosure practices, market microstructure, insider trading, and the dissemination of financial information. His research on SEC dissemination in high-frequency trading environments has been particularly influential in both academic and regulatory circles. 2015 EKS&H Faculty Fellowship 2015 RAST Conference Best Paper Award 2011 Fama-Miller Center Research Grant 2010 William Ladany Faculty Scholar 2009 Ernest R. Wish Award 2009 Initiative on Global Markets Research Grant 2003 Deloitte Foundation Doctoral Fellowship European Accounting Association's 2003 Doctoral Colloquium Fellowship 2001 Geewax, Terker & Company Prize for Investment Research Dr. Rogers serves on the editorial board of the Journal of Accounting Research and works as an ad hoc reviewer for the Journal of Finance, the Accounting Review, the Journal of Accounting and Economics, the Review of Accounting Studies, Contemporary Accounting Research, American Accounting Association Midyear, and the Annual and FARS section meetings. His research has been supported by numerous grants including those from the Fama-Miller Center, Initiative on Global Markets, and the Deloitte Foundation. While specific information about his laboratory or research team is not provided in the available text, his extensive publication record and editorial roles suggest he likely collaborates with multiple researchers and potentially supervises graduate students in accounting research.
David M. Higdon is a Professor and Department Head of the Department of Statistics at Virginia Tech within the College of Science. He specializes in Bayesian statistical modeling of environmental and physical systems, focusing on integrating physical observations with computer simulations for prediction and inference. Previously, he spent 14 years at Los Alamos National Laboratory as a scientist and group leader in the Statistical Sciences Group. Education: Ph.D. in Statistics, University of Washington, 1994 M.A. in Mathematics, University of California San Diego, 1989 B.A. in Mathematics, University of California San Diego, 1987 Research Interests: Higdon’s work spans space-time modeling , inverse problems in hydrology and imaging , statistical modeling in ecology and environmental science , and multiscale models . He develops methods for parallel processing in posterior exploration , statistical computing , and Monte Carlo simulations . His research addresses critical challenges in uncertainty quantification (UQ), including climate modeling, nuclear density functional theory, and geophysical imaging. Publications Trends: His recent articles emphasize Bayesian methodologies applied to complex systems, such as climate forecasting, materials science, and cosmology. A recurring theme is the development of emulators and surrogate models to handle computationally intensive simulations. Awards: Fellow of the American Statistical Association Advising & Grants: While no specific advisees are listed, Higdon has contributed to interdisciplinary collaborations in UQ and statistical modeling. His work has been supported by grants from agencies such as the National Science Foundation and Department of Energy. Labs/Teams: He leads the Statistics Department’s efforts in UQ and computational statistics, fostering collaborations across engineering, environmental science, and physics.
Phu Nguyen is an Associate Adjunct Professor in the Department of Civil and Environmental Engineering at the Samueli School of Engineering, University of California, Irvine. His academic background includes a Ph.D. in Civil Engineering from UC Irvine (2014), an M.S. in Engineering Science from the University of Melbourne (2008), and a B.Sc. in Civil Engineering from Bach Khoa University HCMC Vietnam (2003). Education Ph.D., University of California, Irvine, 2014 M.S., University of Melbourne, 2008 B.Sc., Bach Khoa University, 2003 Nguyen’s research focuses on flood modeling and forecasting, with a strong emphasis on satellite-based precipitation estimation. He has developed systems like CONNECT for analyzing large-scale rainfall systems, RainSphere for integrated satellite data tools, iRain for real-time rainfall observations, and DataPortal for on-demand data processing. His work leverages machine learning, GIS, and remote sensing technologies to improve hydrologic and disaster management applications. Recent publications highlight his integration of deep learning and satellite infrared/microwave data to enhance precipitation estimation accuracy. Key contributions include bias correction frameworks, tropical cyclone detection models, and evaluations of climate data records like PERSIANN-CCS-CDR. Nguyen is actively affiliated with the Center for Hydrometeorology & Remote Sensing (CHRS) at UC Irvine, where he contributes to advancing satellite precipitation methodologies and their hydroclimatic applications.
Professor Joaquim Pinto is a leading climate scientist at the Karlsruhe Institute of Technology (KIT), where he serves as Head of the Working Group "Regional Climate and Weather Hazards" and as Spokesperson of the collegial institute management team. He holds the prestigious AXA Research Fund Chair position at the Institute of Meteorology and Climate Research - Troposphere Research (IMK-TRO). His academic background includes a Licenciate in Geophysical Sciences - Meteorology from the University of Lisbon (1990-1996), PhD studies at the University of Cologne (1998-2002), and academic positions at the University of Cologne (2002-2016) and University of Reading (2013-2016) before joining KIT in 2016. He became a Privatdozent (lecturer) at the University of Cologne in 2011 and earned his habilitation with research on extreme European wind storms. Professor Pinto's research focuses on mid-latitude meteorology and climatology, with special emphasis on extreme weather events , climate variability in Europe across multiple time scales, regional climate modeling and downscaling methods , and the diagnostic modeling and quantification of risks associated with extreme events affecting Europe. His work bridges fundamental climate science with practical applications for risk assessment and management. His extensive publication record demonstrates expertise in analyzing European windstorms, heatwaves, and compound extreme events. Recent work examines the impacts of climate change on wind energy potential, extreme precipitation events, and the complex interactions between atmospheric circulation patterns and regional climate extremes. His research often employs high-resolution climate modeling, statistical-dynamical downscaling approaches, and interdisciplinary collaborations to address pressing climate challenges. AXA Research Fund Chair in Regional Climate and Weather Hazards Professor Pinto teaches graduate and undergraduate courses including "Climate Modelling and Dynamics with ICON," "IPCC Assessment Report," "Climatology," "Energy Meteorology," "Methods of Data Analysis," and "Regional Climate and Weather Hazards." His teaching reflects his research expertise in climate modeling, extreme events, and regional climate change impacts.
Dr. Patrick S. Market is a Professor of Atmospheric Science and currently serves as the Director of the School of Natural Resources at the University of Missouri. He also acts as Interim Co-Director of the Missouri Water Center. His research focuses on synoptic and mesoscale dynamics, particularly winter weather, heavy rainfall, flash flooding, and severe local storms. He has contributed to advancements in precipitation efficiency studies and operational forecasting techniques. His work explores the role of artificial intelligence in weather prediction and communication, emphasizing the continued importance of human expertise in an automated forecast process. Dr. Market has secured grants for data stream maintenance and digital equity planning, and he has led educational initiatives integrating research into synoptic meteorology classrooms. Notable collaborations include projects with the National Weather Service and studies on the Ozark Plateau's topographical influence on weather systems.
Professor Liz Stephens is a faculty member at the University of Reading's Department of Meteorology, specializing in flood forecasting, climate variability, and disaster risk management. Her work focuses on improving hydrological and meteorological models to enhance flood preparedness and climate adaptation strategies globally. Research Interests: Probabilistic flood forecasting Climate impacts on extreme events Enhancing forecast communication for decision-makers Applications in data-scarce regions like Kenya and Uganda Key Projects: Global Flood Awareness System (GloFAS) World Weather Attribution studies Probabilistic forecast evaluation frameworks Her research bridges academic analysis with practical implementation, collaborating with international organizations like ECMWF and humanitarian agencies to translate scientific insights into actionable disaster preparedness measures.
Hyemi Kim is an Adjunct Professor at the School of Marine and Atmospheric Sciences (SoMAS), Stony Brook University. Her research focuses on climate variability across subseasonal to decadal timescales, including topics like the Madden-Julian Oscillation (MJO), tropical-extratropical interactions, and extreme weather events such as atmospheric rivers and tropical cyclones. Education: Ph.D., 2008, School of Earth and Environmental Sciences, Seoul National University, South Korea Research Interests: Hyemi Kim's work spans four primary areas: (1) Climate prediction from subseasonal to decadal scales, (2) Tropical-extratropical interactions, (3) Extreme events (atmospheric rivers, storm tracks, tropical cyclones), and (4) Machine learning applications for subseasonal-to-seasonal (S2S) prediction. Publication Trends: Her research output emphasizes the MJO, its interactions with other climate modes (QBO, ENSO), and implications for extreme weather. Recent works analyze atmospheric rivers, storm tracks, and tropical cyclone activity, often linking these to large-scale climate variability. Publications frequently employ climate models (e.g., CESM1, SubX, NMME) to assess predictability and improve forecasting frameworks. Labs & Teams: She collaborates with institutions like the National Center for Atmospheric Research (NCAR) and contributes to multi-model experiments such as the Subseasonal Experiment (SubX) and North American Multi-Model Ensemble (NMME).
Tom Beucler is a Conditional Pre-Tenure Assistant Professor in Geo-Environmental Data Science at the University of Lausanne’s Institute for Earth Surface Dynamics (IDYST). He holds a Master’s degree in Science and Mechanics from École Polytechnique (2014) and a PhD in Atmospheric Science from MIT (2019). Postdoctoral research at Columbia University and UC Irvine focused on machine learning applications in climate science under Professors Pierre Gentine and Michael Pritchard. Research Interests: Climate informatics, atmospheric physics, fluid dynamics, tropical meteorology, and integrating machine learning into climate models for extreme weather prediction and hydrological cycle modeling. Collaborations: Works with environmental scientists and computer engineers to improve climate models using neural networks and causal discovery methods. Initiatives: Organizes weekly brainstorming sessions to promote machine learning adoption in environmental sciences. Publications span climate-invariant machine learning, data-driven parameterizations, and hybrid AI-climate modeling frameworks like ClimSim. His work emphasizes causal consistency and generalizability across climate conditions.
Dr. Jinan Allan is an Assistant Professor at Clemson University's College of Behavioral, Social and Health Sciences, Department of Psychology. She holds a PhD in Cognitive Psychology from the University of Oklahoma (2021), an MS from the same university (2018), and a BS in Mathematics-Statistics from Michigan Technological University (2015). She completed a postdoctoral fellowship at the Max Planck Institute for Human Development. Her research focuses on cognitive abilities, statistical numeracy, and their impact on decision-making in contexts like medical scenarios, cybersecurity, and extreme weather risks. She is affiliated with the Media Forensics Hub and teaches courses on judgment and decision-making. Education: PhD Cognitive Psychology, University of Oklahoma, 2021 MS Cognitive Psychology, University of Oklahoma, 2018 BS Mathematics-Statistics, Michigan Technological University, 2015 Research emphasizes behavioral science and psychometric methods to develop decision-making metrics. Key themes include risk communication for tornado/hurricane warnings, climate change judgments, and cybersecurity decision-making. Her work bridges cognitive psychology with real-world applications in disaster preparedness and public health. Recent publications analyze extreme weather response patterns, numeracy's role in science comprehension, and probabilistic risk communication strategies. She is currently reviewing graduate student applications for 2025-2026. Affiliations include the Media Forensics Hub at Clemson University, indicating interdisciplinary collaboration in digital security and information integrity.
