Andrew Stuart is the Bren Professor of Computing and Mathematical Sciences at the California Institute of Technology (Caltech), joining in 2016. He previously held faculty positions at the University of Warwick (1999–2016), Stanford University (1992–1999), and Bath University (1989–1992). He earned his PhD from the University of Oxford's Computing Laboratory in 1986. Professor Stuart's research focuses on applied and computational mathematics , particularly Bayesian inverse problems , data assimilation for dynamical systems , and stochastic modeling . His work bridges mathematical theory, algorithm development, and applications in geophysics, materials science, and biological systems. His recent publications emphasize operator learning , machine learning for PDEs , and uncertainty quantification . Key areas include ensemble Kalman methods , Gaussian processes , and neural operators for solving and learning from complex systems. Scientific awards include the Vannevar Bush Faculty Fellowship and election to the Royal Society of Great Britain . He advises graduate students in applied mathematics, computational science, and geophysics, including Edoardo Calvello , Hojjat Kaveh , and Florian Wolf .
Tapio Schneider is the Theodore Y. Wu Professor of Environmental Science and Engineering at the California Institute of Technology. His research focuses on atmospheric dynamics across Earth and other planets, climate modeling innovations, and geophysical turbulence analysis. He contributes to the Climate Modeling Alliance (CliMA) and develops advanced computational tools for climate prediction. Albert-Ludwigs-Universität Freiburg (Vordiplom, 1993) Princeton University (M.Sc. 1997, Ph.D. 2001) University of Washington, Seattle (Visiting Graduate Student, 1994-1995) His research spans climate dynamics , atmospheric turbulence , and AI-enhanced climate modeling , addressing challenges in cloud dynamics, extreme weather patterns, and planetary climate systems. Current work emphasizes hybrid machine learning-physical models and computational acceleration for high-resolution simulations. Recent publications highlight trends in AI integration for climate science, with applications in hydrology , cloud microphysics , ocean circulation , snowpack modeling , and climate tipping points . His team develops open-source tools like ClimateMachine for GPU-accelerated simulations. Scientific Recognition: Fellow, American Geophysical Union (2022) Rosenstiel Award (2019) World Economic Forum Young Scientist (2012) David and Lucile Packard Fellow (2005-2010) Alfred P. Sloan Research Fellow (2004-2006) Tapio leads climate dynamics research at Caltech, directs the Linde Center for Global Environmental Science (2011-2012), and serves as Editor for the Journal of Advances in Modeling Earth Systems . His group collaborates with NASA Jet Propulsion Laboratory (2016-2024) and Google Research (2022-present).
Petros Koumoutsakos is the Herbert S. Winokur, Jr. Professor of Computing in Science and Engineering at Harvard University's School of Engineering and Applied Sciences (SEAS), where he also serves as Area Chair for Applied Mathematics. His research integrates machine learning with computational science to advance understanding of complex systems, including fluid dynamics, turbulence modeling, and biomedical applications. He leads the CSE Lab, focusing on high-performance computing and interdisciplinary collaborations such as a recent study with Citadel Securities and Google Cloud to simulate heart disease in cloud environments. Key research interests include reinforcement learning for turbulence closures, generative models for PDE solutions, and physics-informed AI for biomedical imaging and wildfire prediction. He was awarded the PRACE HPC Excellence Award (2023) for contributions to high-performance computing. His work bridges computational methods with real-world applications, emphasizing interpretability and scalability in multiscale systems. Grants & Collaborations: Leadership in multi-institutional projects, including turbulence modeling via reinforcement learning and cloud-based HPC studies. Labs/Teams: Director of the CSE Lab, advancing AI, computational fluid dynamics, and biomedical simulations.
Erik B. Sudderth is a Professor of Computer Science and Statistics and Chancellor's Fellow at the University of California, Irvine (UCI). He leads the Learning, Inference, & Vision Group and directs multiple research centers, including the UCI Center for Machine Learning and Intelligent Systems and the HPI Research Center in Machine Learning and Data Science. He previously served as an Associate Professor at Brown University. Education: B.S. (summa cum laude) in Electrical Engineering from UC San Diego (1999), M.S. and Ph.D. in EECS from MIT (2002, 2006). His research focuses on statistical methods for scalable machine learning, Bayesian nonparametrics, probabilistic graphical models, and applications in computer vision, AI, and environmental science. Key areas include nonparametric clustering, deep generative models, and particle-based inference algorithms. Research interests span diverse topics: advancing Bayesian nonparametric models for medical time series, scalable variational inference, and AI ethics. Notable contributions include the NET-VISA seismic monitoring system (ISBA Mitchell Prize, 2014), the BNPy toolbox (NSF CAREER Award), and work on diverse particle max-product algorithms for continuous inference. Scientific awards include the NSF CAREER Award, ISBA Mitchell Prize, and recognition as one of "AI's 10 to Watch" (IEEE). He has served as editor for top journals (JMLR, IEEE PAMI) and conference chairs (NeurIPS, CVPR). His work bridges theory and practice, with applications in robotics, climate science, and healthcare. Labs/Teams: UCI Learning, Inference, & Vision Group; UCI Center for Machine Learning; CREATE Technology Center. Grants include NSF funding for visually impaired collaboration tools and soil biogeochemical modeling.
Daniel Sanz-Alonso is an Assistant Professor in the Department of Statistics at the University of Chicago since 2018, affiliated with the Committee on Computational and Applied Mathematics. He previously held a postdoctoral position in Brown University’s Division of Applied Mathematics and contributed to their Data Science Initiative. His research focuses on integrating predictive mathematical models with large datasets, addressing challenges in inverse problems, data assimilation, and scientific machine learning. Education: BSc from University of Valladolid (2012), PhD in Mathematics and Statistics from University of Warwick (2016). Awards include the José Luis Rubio de Francia Prize (2020) for Spanish mathematicians under 32 and an NSF CAREER Award (2023). He has been an Associate Editor of the SIAM/ASA Journal on Uncertainty Quantification since 2025. Funding support comes from the National Science Foundation, National Geospatial-Intelligence Agency, Department of Energy, and BBVA Foundation. His interdisciplinary work bridges data science, machine learning, and partial differential equations, with applications in weather forecasting and geophysical sciences.
Ben Livneh is an Associate Professor at the University of Colorado Boulder , affiliated with both the Civil, Environmental, and Architectural Engineering Department and the Cooperative Institute for Research in Environmental Sciences (CIRES) . As Director of the Western Water Assessment , he bridges academic research with regional climate resilience initiatives. Ph.D. in Civil Engineering (Hydrology), University of Washington (2012) MESc in Civil Engineering, University of Western Ontario (2006) His research explores hydrologic responses to climate and land-cover changes , focusing on snowpack dynamics, wildfire impacts on water quality, sediment transport, and drought predictability. Key projects include simulations of montane snowpack for wolverine habitat preservation and post-fire landslide susceptibility analysis . Recent publications highlight continental-scale hydraulic geometry datasets , climate-energy nexus challenges , and global lake level reconstructions using satellite data. His work has been recognized by the AGU Hydrologic Sciences Early Career Award (2022) and NASA New Investigator Program (2018) . Scientific Awards AGU Hydrologic Sciences Early Career Award (2022) NASA New Investigator Award (2018) Symposium Scholar, DISCCRS VIII (2013) CIRES Visiting Fellowship (2012) Ben leads interdisciplinary collaborations with institutions like the University of Alaska Southeast and NOAA , addressing climate-water-energy-food nexus challenges through advanced modeling and remote sensing techniques.
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.
Richard E. Turner is a Professor of Machine Learning at the University of Cambridge's Department of Engineering and Research Lead for AI for Weather Prediction at the Alan Turing Institute. He serves as Cambridge Lead for the EPSRC Probabilistic AI Hub and previously held roles including Visiting Researcher at Microsoft Research, Co-Director of the AI4ER CDT, and Course Director for the Machine Learning and Machine Intelligence MPhil program. Current research focuses on probabilistic machine learning fundamentals, environmental prediction (weather/climate), and spatio-temporal modeling combining deep learning with Bayesian methods Supervised 26 PhD students (13 graduated) and 7 research assistants/associates Secured over £30M in research funding from EPSRC, Microsoft, Toyota, Google, DeepMind, Amazon, and Improbable Featured in BBC Radio 5 Live's The Naked Scientist, BBC World Service's Click, and Wired Magazine His recent publications demonstrate expertise in diffusion models for PDE simulations, Gaussian Processes for environmental applications, and Bayesian methods for spatio-temporal forecasting. Key trends include climate modeling using ML, neural PDE solvers, and scalable probabilistic inference. Awards : Cambridge Students' Union Teaching Award for Lecturing; supervised Qualcomm Innovation Fellowship winner Collaborations : Microsoft Research (AI4Science), Alan Turing Institute, EPSRC Probabilistic AI Hub Turner leads the Turner Group within Cambridge's Machine Learning Group, focusing on uncertainty-aware ML for scientific applications. Current research assistants work on topics like meta-learning, Bayesian inference, and climate science applications.
Farnoush Banaei-Kashani is an Associate Professor (Tenured) in the Department of Computer Science and Engineering at the University of Colorado Denver. She also holds an Adjunct Associate Professor position in the Department of Mathematical and Statistical Sciences. As the founder and director of the Big Data Management and Mining Lab (BDLab), she leads multiple GAANN Fellowship Programs, including BDSE (Big Data Science and Engineering), DDC (Data-Driven Cybersecurity), and II (Infrastructure Informatics). She directs the 'Data Science in Biomedicine' MS Track and focuses on data-driven decision systems (DDSs), integrating machine learning and big data analytics into healthcare, energy, transportation, and environmental applications. Education: Details not explicitly provided in the text. Her research spans data management cycles for DDSs, addressing challenges like big data volume, velocity, and variety. Key projects include iWatch (crime surveillance), POCM (mobility monitoring), and GeoSIM (urban texture documentation). She teaches courses such as Machine Learning Systems, Big Data Science, and Data Mining. Publications highlight advancements in sea ice classification, federated learning, proteomic networks, and privacy-preserving AI. Her work is funded by NSF, NIH, DOT, and industry partners like Google and IBM. She has advised numerous students and contributes to academic leadership as editor, conference chair (ACM SIGSPATIAL 2018/2019), and program committee member for venues like SIGMOD and KDD.
Leif Eriksson is a Professor at Chalmers University of Technology , specializing in Radar Remote Sensing within the Department of Space, Earth and Environment . His career at Chalmers began in 2004, and he was promoted to Professor in 2022 after serving as Group Leader (2012–2017) and Head of Faculty Assembly (2017–2020). His research focuses on developing advanced methods for environmental monitoring using radar data, particularly synthetic aperture radar (SAR) from satellites and aircraft. Leadership Roles: Group Leader (Radar Remote Sensing), Faculty Assembly Head Key Collaborations: Rymdstyrelsen, EU Horizon, VINNOVA, European Space Agency Research Interests : Dr. Eriksson’s work spans forest biomass estimation , sea ice dynamics , and ocean surface current/wind retrieval . He integrates SAR data with in situ observations and climate models to study: Forest degradation (clear cuts, storm damage) via multi-temporal SAR Sea ice concentration, drift patterns, and thickness in Arctic regions Wind vectors and surface currents using interferometric SAR techniques Applications for maritime navigation safety and polar shipping optimization Article Trends : His recent publications emphasize SAR’s role in transport infrastructure monitoring (e.g., Iron Ore Line degradation), pan-Arctic landfast ice stability , and multi-frequency SAR fusion for enhanced sea ice observations. Collaborative work with teams across Europe and the U.S. highlights interdisciplinary approaches to climate and marine research. Projects & Grants : Dr. Eriksson leads or contributes to projects such as: CAISA (2022–2024): Air-ice-sea data assimilation EONav (2016–2019): Copernicus data for maritime navigation SEDNA (2017–2020): Safe Arctic shipping Forest Biomass Monitoring (2017–2018): Spaceborne SAR applications His work is supported by Rymdstyrelsen, EU Horizon, and industry partners like Trafikverket. Labs & Teams : He is central to the Radar Remote Sensing Group at Chalmers, collaborating with institutions like Lund University and international bodies such as ESA. His research often involves satellite campaigns (e.g., TanDEM-X, Sentinel) and field studies in polar regions.
Rebecca Willett is a Professor of Statistics and Computer Science at the University of Chicago and Faculty Director of AI at the Data Science Institute. She holds the Worah Family Professorship and is a member of the Wallman Society of Fellows. Her research focuses on machine learning, signal processing, and scientific computing, with applications in astronomy, climate science, and biochemistry. She has held visiting roles at institutions including UCLA and INRIA. Key roles include Deputy Directorships at the NSF-Simons Institute for Theory and Mathematics in Biology and the SkAI Institute. Education: PhD in Electrical and Computer Engineering from Rice University (2005), followed by faculty roles at Duke University (2005–2013) and the University of Wisconsin-Madison (2013–2018). Awards include the 2024 SIAM Data Science Career Award, NSF CAREER Award (2007), and AFOSR Young Investigator Award (2010). Research interests span inverse problems, optimization theory, and interdisciplinary applications. Her work bridges high-dimensional statistics and imaging science. Recent articles emphasize neural network theory, climate data assimilation, and biophysical modeling. Awards include SIAM Fellowship, IEEE Fellowship, and teaching excellence awards. She leads initiatives in AI ethics, broadening participation in STEM, and serves on key committees like the National Academies' CATS. Labs/Groups: Machine Learning Group at UChicago, CERES Center for Unstoppable Computing. Grants include NSF, DOE, and collaborations with Argonne National Laboratory.
Beverley J. McKeon is a Professor of Mechanical Engineering at Stanford University, previously holding the Theodore von Kármán Professorship in Aeronautics at Caltech. Her research focuses on fluid mechanics, particularly turbulence, flow control, and boundary layer dynamics. She earned her B.A. and M.Eng. from the University of Cambridge, and her Ph.D. from Princeton University. McKeon's work integrates experimental and theoretical approaches to manipulate wall-bounded flows for drag reduction and performance enhancement. Her research interests include resolvent analysis, high Reynolds number turbulence, and the application of machine learning to fluid dynamics. She has led interdisciplinary projects on morphing surfaces and viscoelastic turbulence. Awarded the Vannevar Bush Faculty Fellowship and PECASE, McKeon has been recognized for her teaching and mentoring. Her honors include Fellowships from the APS and AIAA. She chairs editorial boards for journals like Physical Review Fluids and has served on national committees for theoretical and applied mechanics. Her academic leadership includes roles as Deputy Chair of Caltech’s Division of Engineering and Applied Science and Associate Director of GALCIT. She advises numerous students and collaborates globally on initiatives like the Stories of Women in Fluids.
Prof. Hans van Lint is a Professor of Traffic Simulation and Computing at Delft University of Technology (TU Delft), where he holds the Anthony van Leeuwenhoek Chair since 2013. He is affiliated with the Department of Transport & Planning within the Faculty of Civil Engineering and Geosciences. His research focuses on the intersection of traffic flow theory, data analytics, and traffic simulation, with applications in estimating and predicting traffic states in networks. He has supervised numerous PhD students and contributed to valorization projects translating research into practical solutions. Van Lint earned his MSc in Civil Engineering in 1997 and returned to TU Delft for his PhD, which he completed in 2004 on 'Freeway Travel Time Prediction.' He has held roles including Assistant Professor (until 2009), Associate Professor, and has served as Director of Education for the MSc Transport, Infrastructure and Logistics program from 2010–2016. His research interests include traffic simulation frameworks, data assimilation techniques, and the development of tools for traffic state estimation. He has authored influential papers on topics such as microscopic traffic modeling, congestion pattern analysis, and macroscopic fundamental diagrams. His work emphasizes bridging theoretical models with real-world applications, enhancing traffic management and infrastructure planning. Van Lint teaches courses like 'Transport & Planning' and 'Interdisciplinary Fundamentals,' reflecting his commitment to both research and education. He actively contributes to TU Delft's labs, including the Traffic Dynamics, Modelling and Control Lab, advancing interdisciplinary approaches to mobility challenges.
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
Scientia Professor Gary Froyland is a Professor at the University of New South Wales (UNSW), affiliated with the School of Mathematics & Statistics. He leads the ARC Laureate Centre for Dynamical Systems and Data and holds an Einstein Visiting Fellowship from the Einstein Foundation Berlin. His academic credentials include a BSc (Hons 1, Medal) in Pure and Applied Mathematics from the University of Queensland and a PhD in Mathematics from the University of Western Australia. Professor Froyland's research spans two primary domains: dynamical systems and optimization. In dynamical systems, he investigates the interplay of probability and geometry in nonlinear and chaotic systems, employing tools from ergodic theory, functional analysis, and differential geometry. His work extends to applications in oceanography, atmospheric science, and granular flows. In optimization, he focuses on decision-making in complex systems with uncertain information, developing novel approaches in mathematical programming that have been applied to mining, logistics, and medical treatment planning. His recent publications demonstrate a strong focus on coherent structures in dynamical systems, linear response theory, and applications to geophysical phenomena. The research shows increasing interdisciplinary collaboration, particularly with climate scientists and data analysts, reflecting a trend toward applying advanced mathematical techniques to real-world problems in environmental science and engineering. J.D. Crawford Prize (2025) Elected Member of the Academy of Europe / Academia Europaea (2024) ARC Laureate Fellow (2024-2029) Fellow of the Society for Industrial and Applied Mathematics (SIAM) (2021) Fellow of the Australian Academy of Science (2020) Vice-Chancellor's Award for Teaching Excellence - Postgraduate Research Supervision (2015) Professor Froyland actively supervises PhD and honors students, with current advisees including Kevin Felipe Kühl Oliveira, Nicholas Peters, and Kathrin Völkner. His research is supported by multiple grants, including an ARC Laureate Fellowship (2024-2029) for "Breakthrough mathematics for dynamical systems and data," an Einstein Visiting Fellowship (2022-2026), and several ARC Discovery Projects. His work has practical applications in climate science, mining optimization, and medical treatment planning, particularly in radiotherapy. He leads the ARC Laureate Centre for Dynamical Systems and Data, which brings together researchers to develop new mathematical approaches for analyzing complex dynamical systems. The center focuses on creating methods to identify coherent structures in spatiotemporal data, with applications spanning environmental science, social science, health science, and engineering.
