California Institute of Technology (Caltech)United States
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.
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.
Massachusetts Institute of TechnologyUnited States
Themistoklis Sapsis is a Professor in the Department of Mechanical Engineering at the Massachusetts Institute of Technology (MIT), where he also holds an affiliation with the MIT Institute for Data, Systems, and Society. He earned his Ph.D. in Mechanical Engineering from MIT in 2011 and previously served as an Assistant Research Scientist at NYU’s Courant Institute of Mathematical Sciences. His research focuses on developing analytical, computational, and data-driven methods to predict and quantify extreme events in high-dimensional nonlinear systems, such as turbulent fluid flows and mechanical systems. Key areas include probabilistic modeling of climate extremes, machine learning for climate simulation corrections, and uncertainty quantification in complex dynamical systems. Recent work emphasizes applications in ocean engineering (e.g., vortex-induced vibrations, wave energy systems) and environmental science (e.g., spatially resolved climate extremes, bias correction in Earth system models). His methodologies combine stochastic emulators, Bayesian experimental design, and neural networks to address challenges in data sparsity and model fidelity. Notable contributions include frameworks for correcting coarse-scale climate simulations using machine learning, real-time ocean temperature reconstruction from satellite data, and data-driven modeling of hydrodynamic interactions in marine risers. His research bridges theoretical developments with practical applications in energy systems, structural monitoring, and autonomous systems. Prof. Sapsis collaborates with interdisciplinary teams and has contributed to initiatives such as FIRSTLING-DIGIMAR (a marine riser digital twin) and multi-fidelity frameworks for autonomous seakeeping. His work is supported by grants focused on advancing machine learning in scientific modeling and extreme event prediction.
Richard Nickl is a Professor of Mathematical Statistics at the University of Cambridge, affiliated with the Department of Pure Mathematics and Mathematical Statistics (DPMMS) and the Statistical Laboratory. His research focuses on high-dimensional inference, Bayesian nonparametrics, statistics for partial differential equations, and inverse problems. He has held significant grants, including an ERC Advanced Grant (2024–2029) and an EPSRC Programme Grant (2022–2027). His work bridges statistics, probability, and analysis, with contributions to theoretical foundations and computational methods in non-linear inverse problems. Key research interests include Bayesian posterior consistency, statistical inference for diffusions, and polynomial-time algorithms for high-dimensional posteriors. Notable publications include foundational monographs such as Mathematical foundations of infinite-dimensional statistical models (2016), which earned a PROSE Award, and recent advancements in Bayesian nonparametric inference for McKean-Vlasov models (2025). His group organizes workshops, such as the 2024 Statistical Aspects of Non-Linear Inverse Problems conference. Awards: 2017 PROSE Award in Mathematics. Grants: ERC Advanced Grant, EPSRC Programme Grant. Lab/Team: Research Group in Mathematical Statistics at DPMMS, focusing on inverse problems and Bayesian methodology.
Abhijit Sarkar is a Professor in the Department of Civil and Environmental Engineering at Carleton University, Ottawa. His work centers on computational dynamics and probabilistic modeling, with office MC 3076 in the Minto Centre for Advanced Studies in Engineering and contact details including phone (613) 520-2600 x6320 and email abhijit_sarkar@carleton.ca . Education: D.Phil. from University of Oxford M.Sc. from Indian Institute of Science (IISc) B.E. from Calcutta University Professional Engineer (P.Eng.) designation His research drives innovation in uncertainty quantification for complex engineering systems. Core interests include dynamics of nonlinear structures, probabilistic mechanics for stochastic finite element methods, and Bayesian inference frameworks for parameter estimation. He pioneers scalable high-performance computing solvers for large-scale systems and sparse learning algorithms to address overfitting in statistical modeling. Recent publications (2022-2024) reveal three dominant trends: (1) Bayesian model calibration for stochastic compartmental systems applied to epidemiology and aerospace, (2) domain decomposition techniques for scalable uncertainty quantification in stochastic PDEs, and (3) sparse learning methods for nonlinear aerodynamic encoding. Key applications span wind turbine vibration analysis, flutter margin prediction, MEMS resonator optimization, and geospatial pandemic modeling. Scientific awards: No awards, fellowships, or medals listed in the source material Graduate supervision includes 6 current students (Ajay Kumar, John Clarabut, Nastaran Dabiran, Sakhi Mittal, Michael Pantano, Brandon Robinson) and 18 graduated students across 17 years (2006-2023). His research leverages high-performance computing for projects in structural dynamics, aeroelasticity, and computational epidemiology, frequently co-supervised with Dominique Poirel and Chris Pettit. Notable grants focus on wind tunnel validation for nonlinear systems and pandemic spread modeling. Based in the Minto Centre for Advanced Studies in Engineering, his computational mechanics group develops algorithms for stochastic dynamics using Carleton University's high-performance computing infrastructure. Collaborations span aerospace engineering (flutter analysis), civil infrastructure (seismic wave propagation), and public health (Covid-19 modeling).
California Institute of Technology (Caltech)United States
Tim Colonius is the Frank and Ora Lee Marble Professor of Mechanical Engineering and Medical Engineering and holds the Cecil and Sally Drinkward Leadership Chair at the California Institute of Technology. He has been affiliated with Caltech since 1994 and currently serves as Executive Officer for Mechanical and Civil Engineering . Colonius earned his B.S. from the University of Michigan (Ann Arbor), and both his M.S. and Ph.D. from Stanford University. Research Interests: His work focuses on fluid dynamics (global instabilities, cavitation, aerodynamic sound), flow control (closed-loop control, reduced-order modeling), and biomedical applications (shock waves, lithotripsy, ultrasound). He also develops advanced numerical methods for interface capturing, immersed-boundary techniques, and high-order accuracy. Scientific Contributions: Recent publications highlight his research in multiphase flows, vortex ring collisions, turbulent jet analysis, GPU-accelerated simulations, and biomedical applications. His group uses computational and data-driven approaches to study turbulence, instabilities, and flow optimization. Scientific Awards: AIAA Aeroacoustics Award Fellow of the Acoustical Society of America Fellow of the American Physical Society (APS) NSF and DoD research grants
Virginia Polytechnic Institute and State UniversityUnited States
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.
Dr. Masoumeh Dashti is an Associate Professor in Mathematics at the University of Sussex, UK, affiliated with the School of Mathematical and Physical Sciences. She holds a PhD in Mathematics from the University of Warwick (2008) and prior degrees in Mechanical Engineering from Sharif University of Technology and Tehran Polytechnic. Her research focuses on Partial Differential Equations, Inverse Problems, Bayesian Inference, and their applications in fluid dynamics and epidemiology. Key research interests include: Bayesian approaches to inverse problems, sparsity-promoting estimators, uncertainty quantification, and mathematical modeling of epidemics on networks. She has contributed to foundational work on Besov priors and MAP estimator consistency in nonparametric Bayesian frameworks. Her publications span topics like network inference from epidemic data, contraction rates of posterior distributions, and fluid-structure interaction problems. She has secured grants including 'Two-dimensional stochastically perturbed shallow water equations' (2019-2023) and 'Confronting High Dimensional Network Models With Data' (2018-2022). Currently, she serves as an Associate Editor for SIAM-ASA Journal on Uncertainty Quantification and AIMS Foundations of Data Science . Teaching expertise includes Functional Analysis, Partial Differential Equations, and Calculus of Several Variables at both undergraduate and postgraduate levels.
Professor Dörthe Tetzlaff is a leading figure in ecohydrology, serving as Head of Department (A1) Ecohydrology at the Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB) and as Full Professor of Ecohydrology at Humboldt-Universität zu Berlin, Faculty of Mathematics and Natural Sciences, Geography Department. She also holds an Honorary Professorship at the University of Aberdeen, Scotland. Her work bridges theoretical hydrology with practical applications for water security and environmental management. Professor Tetzlaff's research focuses on ecohydrological processes, particularly using stable water isotopes to understand water cycling, catchment functioning, and landscape connectivity. Her work spans from pristine natural systems to complex urban environments, with particular emphasis on drought resilience, water security, and nature-based solutions. She has pioneered tracer-aided modeling approaches to quantify water storage, fluxes, and ages across the soil-plant-atmosphere continuum. Her publication record shows a clear trend toward increasingly sophisticated integration of multi-tracer approaches with ecohydrological modeling, with recent work focusing on urban systems, drought impacts, and nature-based solutions. The publications demonstrate expertise across hydrology, biogeochemistry, and ecological applications, with strong methodological development in isotope hydrology and modeling techniques. Polubarinova-Kochina Hydrologic Sciences Mid-Career Award of the American Geophysical Union AGU (2024) Water Resource Prize of the Rüdiger Kurt Bode Foundation (2024) Member of the Berlin-Brandenburg Academy of Sciences and Humanities (2023) Fellow of The European Academy of Sciences (2022) Fellow of the Geological Society of America GSA (2020) Professor Tetzlaff actively supervises numerous PhD students and postdoctoral researchers, with her group focusing on cutting-edge ecohydrological research. She leads multiple significant research projects including WETSCAPES2.0, ECCO, ISO-SCALE, and BiNatUr, which address critical questions about water security, climate change impacts, and nature-based solutions in urban environments. Her work is supported by substantial funding from various national and international sources. She leads the Ecohydrology research group at both IGB and Humboldt-Universität zu Berlin, which employs an integrative approach combining field measurements, isotope techniques, and advanced modeling to understand water dynamics across diverse landscapes. The group collaborates extensively with international partners across Europe, North America, and Asia, contributing to global understanding of hydrological processes.
Massachusetts Institute of TechnologyUnited States
Youssef M. Marzouk is the Breene M. Kerr (1951) Professor of Aeronautics and Astronautics at MIT and co-director of the MIT Center for Computational Science and Engineering (CCSE). He is affiliated with the MIT Schwarzman College of Computing, the Statistics and Data Science Center, and the Aerospace Computational Design Laboratory. His research focuses on computational science and engineering, with an emphasis on uncertainty quantification, Bayesian modeling, data assimilation, and machine learning applied to physical systems. He holds a Ph.D. in Mechanical Engineering from MIT (2004), preceded by S.M. (1999) and S.B. (1997) degrees in Aeronautics and Astronautics from the same institution. Marzouk’s work bridges computational mathematics, statistical inference, and fluid dynamics, addressing challenges in energy systems and environmental modeling. He has received numerous awards, including the 2018 AIAA Associate Fellowship and the 2012 MIT Class of 1942 Career Development Chair. His teaching spans computational mathematics, fluid dynamics, and uncertainty quantification. Key collaborations involve the MIT CCSE and external institutions, with funding from DOE and NSF. He advises students on topics like stochastic modeling and inverse problems, and his research lab explores advanced computational methods for high-dimensional systems.
Dr. Karim Sabra is a Professor at the George W. Woodruff School of Mechanical Engineering , Georgia Institute of Technology, specializing in Acoustics and Dynamics . He holds a Ph.D. from the University of Michigan (2003) and joined Georgia Tech in 2007 as an Assistant Professor. His research integrates theoretical and experimental approaches to study wave propagation in diverse fields including structural health monitoring, biomechanics, and ocean acoustics. Key areas of focus include passive imaging techniques using ambient noise and diffuse wave fields, with applications in non-invasive monitoring of mechanical systems and seismoacoustic environments. Education: Ph.D., University of Michigan, 2003 M.S., University of Michigan, 2000 M.Sc., École Nationale Supérieure de Techniques Avancées (France), 2000 Research Interests: Dr. Sabra’s work spans acoustics, structural health monitoring, biomechanical systems evaluation, underwater acoustics, and geophysics . Recent projects include developing passive elastography techniques for soft tissues using physiological vibrations and exploring ambient noise-based tomography for ocean environments. His interdisciplinary approach bridges multi-scale engineering challenges with multi-wave tools (acoustical, electrical, optical). Publications: His work focuses on advanced acoustic technologies, including underwater communication systems, passive acoustic identification tags, and ray-based tomography methods. Themes include seamount effects on sound propagation, machine learning for acoustic modeling, and environmental sensing using shipping noise. Awards: R. Bruce Lindsay Award (2011) Fellow of the Acoustical Society of America (2007) Institute of Acoustics A.B. Wood Medal (2009) Advising & Grants: Dr. Sabra mentors graduate students in acoustics and wave phenomena, emphasizing interdisciplinary collaboration. His research is supported by grants focused on underwater acoustics, environmental sensing, and biomedical applications. Labs/Teams: His research group develops novel sensors and algorithms for oceanographic and biomedical applications, collaborating with industry and academic partners.
Andrew Zammit Mangion is an Associate Professor at the University of Wollongong , affiliated with the School of Mathematics and Applied Statistics . His research focuses on spatio-temporal statistics, computational methods, and environmental informatics, with applications in climate science and geospatial data analysis. Education : PhD in Statistics (University of Sheffield, 2012), B.Eng. (University of Malta, 2007) Research Themes : Spatio-temporal modeling, Bayesian inversion frameworks (e.g., WOMBAT v2.S), deep learning integration, and statistical software development (e.g., FRK package) Grants & Projects : ARC DECRA Fellow (2018), Chief Investigator on ARC Discovery Project (greenhouse gases), ARC Special Research Initiative (Securing Antarctica's Environmental Future), and ARC Industrial Transformation Hub (TIDE). Collaborations : University of Bristol, University of Edinburgh, ESA CCI, NASA OCO-2 data projects Scientific Awards include the prestigious Australian Research Council Discovery Early Career Researcher Award (DECRA). His work spans Antarctic ice sheet analysis, CO2 flux inversion, and scalable spatial statistical models for environmental monitoring.
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.
Professor Dr. Martin Grepl is a faculty member at RWTH Aachen University, where he holds the Lehr- und Forschungsgebiet Optimierung mit partiellen Differentialgleichungen (Teaching and Research Area in Optimization with Partial Differential Equations). He has been affiliated with RWTH Aachen since 2009, first as a Professor (W1) and since 2014 as a Professor (W2). Education: Diplom-Ingenieur (Aerospace Engineering), University of Stuttgart (2000) Master of Science (Mechanical Engineering), MIT (2001) Doctor of Philosophy (Mechanical Engineering), MIT (2005) His research focuses on numerical methods for partial differential equations (PDEs) , particularly model order reduction , reduced basis methods , finite element methods , and optimal control for parametrized PDEs. He also investigates parameter estimation , inverse problems , and control constraints in elliptic and parabolic PDE systems. The scientific awards he has received include the Studienstiftung des deutschen Volkes (1997-2000), a Fellowship from the Dr. Jürgen Ulderup-Stiftung (1998-1999), and the Lehrpreis der Fachschaft Mathematik/Physik/Informatik (2011). His work spans applications in manufacturing , medical physics , and fluid dynamics , as evidenced by his patents and collaborative research. His publications demonstrate expertise in reduced basis methods for nonaffine/nonlinear PDEs , trust region optimization , and error bounds for real-time and many-query scenarios. His collaborations often involve interdisciplinary applications, including thermal conduction , welding processes , and glomerular filtration modeling .
Philippe Moireau is a Full Professor in the Department of Applied Mathematics at École Polytechnique, where he is also affiliated with the Center for Applied Mathematics (CMAP). He serves as the head of the Inria Project-Team MΞDISIM (Mathematical and Mechanical Modeling with Data Interaction for Simulation in Medicine) and holds the distinguished position of Ingénieur Général of The Corps des Mines. His primary research focuses on inverse problems and data assimilation for partial differential equation models, with particular emphasis on: Observer-based methods from optimal control perspectives Stabilization approaches for evolution equations Numerical analysis of time-dependent control problems Digital twin applications in cardiovascular medicine Professor Moireau's publication portfolio demonstrates consistent focus on mathematical methods for physical systems, with recurring themes in: Data assimilation techniques for PDE-based models Numerical stabilization and discretization methods Cardiovascular biomechanics and hemodynamics Stochastic modeling of biological systems Epidemiological forecasting and control He leads the ANANKΞ project-team at Inria focused on Analysis And Numerics of physical-Knowledge-based Estimation. His educational contributions include lectures on data assimilation theory at CEMRACS and courses on mathematical modeling in cardiac biomechanics at Institut Polytechnique de Paris.
