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Vasileios MaroulasView profile

Professor

• Computational Bayesian Statistics
• Topological Data Analysis
• Machine Learning
• +7 more

Vasileios Maroulas is a Professor of Mathematics at the University of Tennessee, Knoxville, with joint appointments at the Haslam College of Business and the Bredesen Center for Interdisciplinary Research and Graduate Education. He serves as Associate Vice Chancellor and Director of the AI Tennessee Initiative. His research focuses on computational Bayesian statistics, topological data analysis, machine learning, and their applications in science and engineering. He holds senior research fellow positions at the US Army Research Lab and is an Elected Member of the International Statistical Institute. Education: Ph.D. in Mathematics from the University of North Carolina at Chapel Hill. Research interests span computational probability, statistical learning, and interdisciplinary applications in materials science, medicine, and national defense. His work integrates topology, geometry, and machine learning to address complex data challenges. Key areas include Bayesian inference for persistent homology, quantum computing for topological analysis, and health analytics for opioid vulnerability modeling. His research is funded by agencies such as AFOSR, ARL, ARO, DOE, NSF, and the Simons Foundation. Notable achievements include the US Army Research Lab Fellowship, Leverhulme Visiting Fellowship, and UT’s Excellence in Research Award. He advises a dynamic research group (MRG) focusing on AI, Bayesian methods, and topological data science. Alumni include faculty members at the University of Hawaii and industry leaders in machine learning and data science. Current projects include quantum distance approximation, Bayesian sheaf neural networks, and spatiotemporal opioid vulnerability analysis. Labs/Teams: Maroulas Research Group (MRG) at the University of Tennessee, collaborating with national labs (ARL, ORNL) and industry partners (e.g., Eastman, Thor Industries).

Weisen ShenView profile

Associate Professor

• Seismic Tomography
• Earth's Lithosphere Structure
• Geothermal Studies
• +3 more

Weisen Shen is an Associate Professor in the Department of Geosciences at Stony Brook University, where he has been a faculty member since 2018. He holds a B.S. from the University of Science and Technology of China (2008), a Ph.D. from the University of Colorado Boulder (2014), and completed postdoctoral research at Washington University in St. Louis (2015–2017). His research focuses on seismic tomography, lithosphere structure, and geothermal studies. Key interests include improving seismic imaging techniques, quantifying uncertainties in Earth's interior models, and linking seismic data to tectonic and thermal processes. He uses surface waves, receiver functions, and Bayesian methods to study crustal and upper mantle composition, temperature, and deformation. Recent work emphasizes Antarctica, Alaska, and U.S. continental regions. Selected articles highlight advancements in 3D crustal modeling, Antarctic geothermal heat flow, and seismic anisotropy analysis. His group actively seeks students and postdocs for projects on global seismic imaging and subglacial geology.

Paul GendronView profile

Associate Professor

• Signal Processing
• Underwater Acoustics
• Detection Theory
• +1 more

Paul Gendron is Associate Professor of Electrical and Computer Engineering at the University of Massachusetts Dartmouth's College of Engineering. He holds PhD (WPI), MS (Virginia Tech), and BS (UMass) degrees in Electrical Engineering. His research focuses on statistical signal processing for underwater applications, including adaptive filtering for communication channels, low-probability acoustic communications, magnetic anomaly detection, and seismic event classification. Dr. Gendron has secured over $1M in research funding from the Office of Naval Research for undersea surveillance technology development. He teaches graduate courses in communication theory and signal processing, and advises PhD candidates in electrical engineering. Prior to academia, he held research positions at Naval Research Laboratory (2000-2007) and SPAWAR Systems Center Pacific (2008-2012). Recent publications demonstrate advancement in Bayesian methods for underwater localization and detection systems, with applications to marine sensing and surveillance technologies.

Vipin KumarView profile

Professor

• Data Mining
• High-Performance Computing
• Climate Science
• +4 more

Vipin Kumar is a Regents Professor and William Norris Endowed Chair in the Department of Computer Science and Engineering at the University of Minnesota . He leads the CSE Data Science Initiative and has held roles such as Department Head (2005–2015) and Director of the Army High Performance Computing Research Center (1998–2005). His research focuses on data mining, high-performance computing, climate science, and environmental applications . Education: B.E., Electronics & Communication Engineering, Indian Institute of Technology Roorkee (1977) M.E., Electronics Engineering, Philips International Institute, Netherlands (1979) Ph.D., Computer Science, University of Maryland, College Park (1982) Research Interests: Kumar pioneers Knowledge-Guided Machine Learning (KGML) for environmental science, climate change analysis, and global ecosystem monitoring. His work integrates computational methods with domain knowledge to address challenges in agriculture, hydrology, and climate modeling. Key contributions include scalable algorithms (e.g., METIS, PSPASES) and frameworks for big data-driven discovery. Publications: Over 300 articles and 10 books, including seminal works like Introduction to Parallel Computing and Introduction to Data Mining . Recent efforts focus on applying machine learning to monitor global change and predict environmental phenomena like floods, carbon cycles, and lake temperatures. Scientific Awards: ACM SIGKDD Innovation Award (2012) IEEE Sidney Fernbach Award (2016) NSF Expeditions in Computing Grant (Climate Change Project) Fellowships: ACM, IEEE, SIAM, AAAS Advising & Grants: Advisor to over 50 graduate students (listed in full description). Principal Investigator of major grants including NSF BIGDATA, NSF INFEWS, and DARPA World Modeler initiatives. Leads interdisciplinary teams on projects like the Global Observatory for Planetary Health and Ecosystem Resources (GOPHER) . Labs/Teams: Director of GOPHER, a multi-institutional effort to monitor Earth’s ecosystems using AI and satellite data. Active in initiatives like the Climate Change Data Expedition and the Data Science Leadership Summit .

Andres SchmidtView profile

Assistant Professor

• Biogeochemical Modeling
• Machine Learning
• Wildfire Risk Assessment
• +2 more

Andres Schmidt is an Assistant Professor developing machine learning approaches for environmental challenges, including wildfire risk assessment and carbon cycle modeling. His research integrates process-based models with neural networks to analyze biogeophysical processes. Core methodologies: Bayesian-regularized neural networks for environmental prediction Geostatistical clustering for regional risk assessment Deep learning for post-fire recovery tracking Carbon flux optimization using observational networks Recent work (2016-2025) shows strong focus on wildfire applications (7 papers) and carbon-water cycle modeling (5 papers). Technical innovations include Bayesian-regularized ensembles for water contamination prediction, modular geostatistical frameworks for risk assessment, and convolutional networks for infrastructure recovery monitoring. Data integration: Leverages flux tower networks, aircraft campaigns, satellite imagery, and sensor arrays to train models across scales from Oregon forests to Indonesian peatlands.

Neill CampbellView profile

Professor

• Visual Computing
• Machine Learning
• Computer Vision
• +6 more

Neill D. F. Campbell is a Professor of Visual Computing and Machine Learning at the University of Bath, Department of Computer Science. He holds an honorary Associate Professor position at University College London. His research focuses on shape modeling, machine learning, and their applications in computer vision and graphics. Campbell directs the CAMERA research centre and co-leads the Centre for Mathematics and Algorithms for Data (MAD). He leads the MyWorld project, a £46M creative hub in the Bath-Bristol region. Education: PhD in Engineering (Automatic 3D Model Acquisition from Uncalibrated Images) from the University of Cambridge (2011), MEng in Engineering from the University of Cambridge (2006). Research Interests: Modeling shape and appearance using machine learning, Bayesian nonparametric models, generative models, uncertainty quantification, and applications in entertainment, health, and sports science. Active in developing structured uncertainty prediction networks (SUPN) and exploring generative models for inverse problems. Key Projects: MyWorld (Strength in Places Fund), CAMERA, EPSRC CDT in Statistical Applied Mathematics, and collaborations with industry partners. Recent work includes anomaly detection, depth estimation, and conditional sampling techniques. Awards: Royal Society Industry Fellow (2017–present). Over 54 research outputs spanning CVPR, ECCV, SIGGRAPH, and NeurIPS, focusing on structured uncertainty, generative models, and vision applications. Labs/Teams: Director of CAMERA, co-director of MAD, and collaborator in UKRI CDT in Responsible AI. Active in organizing workshops on uncertainty quantification in computer vision (UNCV).

• Dynamical Systems
• Model-Based Reinforcement Learning
• Probabilistic Modeling
• +4 more

Hany Abdulsamad is a Visiting Professor at the Department of Electrical Engineering and Automation, specializing in Sensor Informatics and Medical Technology. His research bridges control systems , machine learning , and dynamical systems , with a focus on probabilistic modeling and optimization. Key Collaborations : Finnish Center for Artificial Intelligence (FCAI), Quantum Machine Learning projects Research Themes : Nonlinear control theory, Bayesian inference, particle filtering, reinforcement learning, and physics-informed AI

Mengyang GuView profile

Assistant Professor

• Uncertainty Quantification
• Gaussian Process Emulation
• Statistical Calibration
• +5 more

Mengyang Gu is an Assistant Professor in the Department of Statistics and Applied Probability at the University of California, Santa Barbara. His research focuses on uncertainty quantification, Gaussian process emulation, statistical calibration, and machine learning for computational science and materials physics. He has developed open-source packages like RobustGaSP, RobustCalibration, and FastGaSP for R and MATLAB. Education: PhD in Statistical Science from Duke University (2016) Research: Uncertainty Quantification, Gaussian Processes, Materials Science, Dynamical Systems Grants: NSF awards for computer model calibration, BioPACIFIC MIP funding Awards: Scialog Fellow, Hellman Fellowship, SIAM Early Career Prize, ACM Best Paper Students: Mentored PhD graduates including Xubo Liu, Yue He, Hanmo Li, and Xinyi Fang His group collaborates with materials scientists, physicists, and biologists to develop statistical methods for complex systems. Recent projects include physics-informed ML for polymer phase identification, inverse Kalman filtering, and AI-enabled materials exploration.

Jiaying WengView profile

Assistant Professor

• Sufficient Dimension Reduction
• Statistical Learning
• Non-parametric Analysis
• +2 more

Dr. Jiaying Weng is Assistant Professor of Mathematical Sciences at Bentley University. Her teaching interests include Introductory Statistics, Regression, Statistical Learning, and Business Analytics. Her research focuses on developing novel statistical methods for sufficient dimension reduction and variable selection, particularly in high-dimensional data contexts. She has contributed to advancements in Fourier transform approaches for inverse dimension reduction and sparse estimation techniques. Professor Weng has published in statistical and interdisciplinary journals, applying her methodologies to diverse domains including environmental health monitoring, computational statistics, and decision support systems. Her work often addresses challenges in analyzing complex, large-scale datasets through innovative mathematical frameworks.

Michael CardiffView profile

Professor

• Hydrogeology
• Aquifer Heterogeneity
• Geothermal Reservoirs
• +3 more

Michael Cardiff is a Professor and Department Chair at the University of Wisconsin–Madison. His research focuses on characterizing aquifer heterogeneity to improve predictions of groundwater flow, transport, and reactions. He employs multi-physics numerical modeling, Bayesian inference, optimization methods, and advanced sensors to address hydrogeologic challenges. His projects include PoroTomo (DOE-funded geothermal reservoir modeling), fracture flow analysis for the Wisconsin DNR, and nitrate monitoring in agricultural settings. He teaches courses such as Hydrogeology (GEOSCI/GLE 627), MATLAB for Geoscientists (GEOSCI/GLE 875), Groundwater Flow Modeling (GEOSCI/GLE 724), Environmental Geology (GEOSCI/GLE 106), and Geophysical Inverse Modeling (GEOSCI/GLE 793), often cross-listed with Geological Engineering programs. Current graduate advisees include Rachel Breunig, G. Graham, Samantha Kershner, and Collin Roland. His research spans theoretical, field, and computational approaches to subsurface processes.

Prashant K. JhaView profile

Assistant Professor

• Fracture Mechanics
• Mechanics of Solids and Granular Media
• Multiphysics and Multiscale Modeling
• +5 more

Prashant K. Jha is an Assistant Professor in the Department of Mechanical Engineering at the South Dakota School of Mines and Technology. Previously, he served as a Mechanical Engineering Lecturer (equivalent to Assistant Professor) at the University of Portsmouth and held postdoctoral positions at Louisiana State University and the Oden Institute at UT Austin. PhD in Civil and Environmental Engineering (2016), Carnegie Mellon University ME in Mechanical Engineering (2012), Indian Institute of Science BE in Mechanical Engineering (2010), Govt. Engineering College Raipur His research focuses on computational mechanics of materials, combining peridynamics , machine learning , and nonlocal modeling to study: Fracture mechanics in soft and granular materials Multiphysics and multiscale simulations Neural operators for scientific computing Uncertainty quantification in material models Design of functional materials under extreme conditions Applications to biomedical imaging and tumor growth Recent publications emphasize neural operator surrogates with error correction, peridynamic modeling of granular media, and multi-fidelity simulations. His work has appeared in journals like CMAME , ARMA , and JCP . NSF ERI award (2025) for multi-fidelity modeling SDBOR CRG award (2025) for magnetic soft materials He advises students in computational mechanics and serves on editorial boards of Journal of Peridynamics and Nonlocal Modeling , JOSS , and Scientific Reports . The CEAD Lab develops methods for fracture analysis, smart material design, and nonlocal neural operator frameworks.

Donsub RimView profile

Assistant Professor

• Numerical Analysis
• Partial Differential Equations (PDEs)
• Model Reduction
• +6 more

Donsub Rim is an Assistant Professor in the Department of Mathematics at Washington University in St. Louis. He holds a PhD from the University of Washington, advised by Randall J. LeVeque and Gunther Uhlmann. His research focuses on numerical analysis of PDEs, inverse problems, and low-rank neural network representations (LRNRs) for real-time solutions in geophysics, medical imaging, and plasma physics. Key areas include stability analysis of neural networks for tsunami early warning, fast inversion of the approximate discrete Radon transform (ADRT), and computational applications in geophysical hazard assessment. Rim is on leave during 2024-2025 as a Visiting Scholar at the University of Washington. Previously, he was a visiting scholar at Tohoku University's IRIDeS (2023) and affiliated with the Courant Institute and Columbia University. His work bridges mathematical theory with practical tools like the 'adrt' Python library for signal processing. Research Interests: Numerical methods for PDEs, model reduction techniques, neural network applications in high-consequence domains (e.g., tsunami forecasting), and ADRT-based algorithms for dimensional splitting and sparse approximations. Collaborations include projects on probabilistic tsunami hazard assessment (PTHA), earthquake kinematic effects on tsunami propagation, and meta-learning approaches for physics-informed networks. Publications highlight contributions to real-time prediction systems, stability analysis of neural networks, and manifold approximations for transport-dominated problems. His software developments, such as the ADRT Python package, emphasize open-source tools for scientific computing. Rim has advised or collaborated with researchers at institutions including Columbia University, Tohoku University, and the University of Washington. Grants and future work involve advancing LRNR frameworks for nonlinear hyperbolic problems, improving tsunami early warning systems via GNSS data, and exploring applications of sparse physics-informed backpropagation in geophysics and plasma physics.

• Financial Mathematics
• Machine Learning
• Data Science

Dr. Huy Chau is a Lecturer in Financial Mathematics and Statistics at the Department of Mathematics, The University of Manchester. He holds a PhD from the University of Padova and Université Paris Diderot, under Professors Wolfgang J. Runggaldier and Peter Tankov. Prior to his current role, he worked as a Post-doctoral researcher at the Alfred Renyi Institute of Mathematics and as a Specially Appointed Assistant Professor at Osaka University's Center for Mathematical Modeling and Data Science. His research focuses on financial mathematics, machine learning, and data science. Key areas include stochastic processes, utility maximization, transaction costs modeling, and algorithmic trading strategies. His work often bridges theoretical advancements with practical applications in quantitative finance and statistical learning. Recent publications emphasize methodological innovations in pricing/hedging frameworks under market uncertainty, stochastic optimization techniques, and the analysis of behavioral investor dynamics in conic market models. Notable contributions address super-replication strategies under transaction costs and inversion-free numerical methods in statistical computing. Dr. Chau is actively involved in supervising PhD students and teaches advanced courses like Martingales with Applications to Finance. He collaborates internationally on projects such as the UoM-IISc Bangalore PhD initiative.

Gabriel PaternainView profile

Professor

• Differential Geometry
• Analysis
• Dynamical Systems
• +2 more

Gabriel Paternain is a Professor of Mathematics at the Faculty of Mathematics , University of Cambridge. His research focuses on Differential Geometry , Analysis , and Dynamical Systems , with particular emphasis on Geometric Inverse Problems and Geodesic Ray Transforms . Research Areas: Differential Geometry Dynamical Systems Geometric Inverse Problems Integral Geometry Partial Differential Equations Mathematical Physics Recent Work: Non-Abelian X-ray transforms Anosov surface rigidity Twistor space analysis Yang-Mills inverse problems Stochastic Bayesian inversion

Mark SpivackView profile

Researcher

• Wave Scattering
• Electromagnetic Waves
• Acoustic Waves
• +3 more

Dr. Mark Spivack is the CATAM Assistant Director at the Department of Applied Mathematics and Theoretical Physics (DAMTP) within the University of Cambridge's Faculty of Mathematics. He has held roles including Senior Research Associate (2019-2021), Director of Studies in Applied Mathematics at Emmanuel College (2006-2011), and Fellow at Robinson College (1996-2002). His research focuses on wave scattering phenomena, including acoustic and electromagnetic waves in random media and rough surfaces, as well as inverse problems in coastal morphology and waveguide propagation. His work integrates mathematical modeling with computational techniques, such as physics-informed neural networks and boundary integral methods, to address challenges in rough surface reconstruction and long-term coastal evolution. Key contributions include stochastic prediction frameworks for shoreline dynamics and ensemble modeling of beach profile changes near coastal structures like groynes. Spivack's publications span over three decades, addressing topics from inverse scattering at grazing angles to Bayesian network-based failure prediction in satellite systems. His research bridges applied mathematics with practical applications in environmental science and engineering. He currently leads DAMTP's Computational and Applied Mathematics (CATAM) program, supporting undergraduate computational mathematics education. His academic affiliations include membership in the Waves research group and leadership roles in interdisciplinary projects combining fluid dynamics, electromagnetics, and coastal morphodynamics.