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.
Xiaojing (Ruby) Fu is an Assistant Professor of Mechanical and Civil Engineering at the California Institute of Technology and a William H. Hurt Scholar (2024-present). Her research focuses on multiphase fluid mechanics in porous media, integrating theory, computation, experiments, and field observations to address geoscience and engineering challenges. Her educational background includes: B.S. in Engineering from Clarkson University (2011) M.S. from Massachusetts Institute of Technology (2015) Ph.D. from Massachusetts Institute of Technology (2017) Professor Fu's research centers on cryosphere hydrology, subsurface engineering, and phase transitions in porous media. She investigates multiphase flow dynamics in contexts like permafrost thaw, snow metamorphism, and carbon sequestration using phase-field modeling and experimental techniques. Her work bridges fundamental physics with applications in environmental resilience and energy systems, emphasizing predictive capabilities for large-scale phenomena through simplified multiscale theories. Analysis of her 15 most recent publications reveals intense focus on cryosphere processes (snow, permafrost) using advanced phase-field modeling and fiber-optic sensing. Key trends include freezing infiltration patterns, meltwater transport in layered snow, and seismic monitoring of soil moisture. Her work increasingly integrates field validation with computational models for environmental applications like drought monitoring and carbon sequestration. Her scientific recognition includes: William H. Hurt Scholar (2024) Professor Fu actively mentors graduate students, as evidenced by qualified students in her research group. She teaches core courses including Thermal Science (ME 11 abc) and Computational Methods for Flow in Porous Media (ME/CE/Ge/ESE 146), training students in both theoretical foundations and applied techniques for subsurface flow problems. She leads the Fu Research Group on Mechanics and Physics of Porous Media Flow, which develops multiscale theories to predict large-scale environmental and energy system behaviors. The group combines mathematical modeling, laboratory experiments, and field observations to address problems in geologic carbon storage, cryosphere dynamics, and subsurface resource management, with recent emphasis on climate change impacts and monitoring technologies.
Sujan Pal is a Hydroclimate Scientist at Argonne National Laboratory , focusing on hydrometeorology, hydroclimatology, and land-atmosphere interactions through numerical modeling and field experiments. He serves as an associate mentor for multiple observational systems in the Atmospheric Radiation Measurement (ARM) user facility and monitors environmental data at Argonne Testbed for Multiscale Observational Science (ATMOS). Ph.D., University of Illinois at Urbana-Champaign (2017-2021) M.S., The University of Arizona (2015-2017) B.E., Jadavpur University (2010-2014) His research spans hydrometeorological modeling, urban climate impacts, and machine learning applications in environmental science. He leads high-resolution flood simulations and contributes to the DOE-funded CROCUS project studying urban climate change in Chicago. Recent publications highlight his work on convection-permitting climate modeling, extreme rainfall dynamics, and flood risk assessment across South America, the U.S. Northeast, and Argentina. His methodologies integrate field data with advanced computational tools. ARM Service Award 2025 Argonne Commercialization Excellence Award 2024 Argonne IMPACT Awards (2024, 2023, 2022) He actively collaborates with national user facilities and contributes to spatiotemporal modeling of water-related hazards as an Associate Editor for Frontiers in Water .
Jan Martin Nordbotten is a full-time Professor at the Department of Mathematics, University of Bergen (UiB), with adjunct positions at Princeton University and NORCE. His research focuses on applied mathematics, particularly in porous media, CO2 storage, fluid dynamics, and interdisciplinary applications in hydrology, biomedicine, and ecology. He completed his PhD at UiB in 2004 and became Norway's third youngest professor in 2007. His work emphasizes numerical methods, multiscale modeling, and experimental validation. Affiliations: UiB (full-time), Princeton (adjunct), NORCE (adjunct) Research Group: Center for Sustainable Subsurface Resources Research interests span mathematical modeling of subsurface processes, including flow in fractured media, geomechanics, and phase-field fracture. Notable contributions include analytical and numerical solutions for CO2 leakage, multiphase flow, and development of tools like DarSIA for image processing in porous media. Publications highlight advancements in mixed-dimensional models, finite element methods, and experimental validation of CO2 storage forecasts. His work bridges theoretical mathematics with practical applications in energy and environmental systems.
Grethe Winther is a Professor and Head of Section in the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU), specializing in Materials and Surface Engineering. Her research is centered on the analysis and modeling of microstructure and mechanical properties of metals, with a strong emphasis on dislocation structures, deformation textures, and recrystallization processes. Her research interests include: Dislocation structures and boundary analysis in deformed metals Crystal plasticity modeling using synchrotron data (3DXRD) Orientation relationships in recrystallization Prediction of mechanical properties in industrial metal forming Multiscale modeling of plastic deformation and surface roughening The recent articles (2025) highlight a consistent focus on advanced characterization techniques like dark-field X-ray microscopy and discrete dislocation dynamics simulations. These works explore the formation of geometrically necessary boundaries, dislocation cell evolution, and multiscale surface deformation, reflecting a strong integration of experimental and computational methods in materials science. Key themes include plastic deformation mechanisms, microstructure evolution, and predictive modeling in metallic systems. Grethe Winther actively supervises multiple PhD projects, including those on dislocation dynamics, X-ray microscopy, and ductile failure simulations. She collaborates extensively with researchers such as H.F. Poulsen and C.V. Nielsen. Her work is supported by ongoing research projects at DTU, focusing on fundamental and applied aspects of metal deformation and microstructure. She is affiliated with the Materials and Surface Engineering section at DTU, where she leads research efforts combining advanced experimental techniques with theoretical modeling to understand and predict metal behavior under deformation.
Ian C. Bourg is an Associate Professor at Princeton University with dual appointments in the Department of Civil and Environmental Engineering and High Meadows Environmental Institute . He directs undergraduate studies in CEE and leads the Interfacial Water Group , focusing on atomistic-level simulations and macroscopic modeling of environmental systems. His concurrent affiliations include the Princeton Institute for the Science and Technology of Materials and Chemical and Biological Engineering department. Education Ph.D. in Civil and Environmental Engineering, University of California-Berkeley (2004) MSc in Chemical Engineering, INSA Toulouse (1999) B.Eng. in Chemical Engineering, INSA Toulouse (1999) Research Interests span clay mineral surface geochemistry, geologic CO 2 sequestration, kinetic isotope effects, water behavior at interfaces, and coupling geochemistry with geomechanics in porous media. His work integrates molecular simulations with experimental validation to study environmental phenomena like contaminant transport, soil carbon storage, and water dynamics in clays. Publications from 2023-2025 reveal expertise in molecular dynamics of clay-water systems, organic contaminant partitioning, cement hydration, and isotope fractionation. Key themes include Environmental Nanoscience , Geochemical Modeling , and Soft Matter Physics applications to environmental systems. Scientific Recognition NSF CAREER Awardee (2018) Advising includes mentoring 12 current and former PhD/postdoc researchers, with notable alumni at institutions like Cornell, University of Poitiers, and Oak Ridge National Laboratory. His group has produced 20+ undergraduate advisees now in academia and industry. Laboratory develops multiscale simulation tools like HybridBiotInterFoam and HybridPorousInterFoam, with active collaborations in nuclear waste management, soil remediation, and sustainable construction materials.
Dr. Nicolas Francois is an Associate Professor in the Department of Materials Physics at Australian National University (ANU), specializing in experimental geomaterials physics, soft matter, and fluid hydrodynamics. He leads the X-ray Tomography and Applications Research Group, combining curiosity-driven and applied research in out-of-equilibrium systems. ARC Industry Fellow (2024-2030): Improving Australian iron ore comminution for green steel production ARC DECRA Fellow (2016-2018): Biofilms in two-dimensional turbulent flows His research spans fundamental questions in: Fragmentation of solid materials Autonomous devices powered by chaotic flows Hydrodynamic waves Stochastic thermodynamics Granular matter Polymer rheology and applied areas in: Comminution of geomaterials Mechanics of fractured rocks Wave-energy conversion Environmental fluid mechanics Publications reveal a trajectory focused on X-ray tomography applications, granular dynamics, and turbulence-driven systems. He utilizes advanced imaging techniques to study material failure mechanisms and fluid-structure interactions, contributing to fields ranging from green steel production to biofilm dynamics. Current student projects and grants emphasize sustainable resource processing and fundamental fluid physics.
Eduardo Gildin is a Professor of Petroleum Engineering and Associate Department Head for Graduate Studies at Texas A&M University's College of Engineering. He holds the L.F. Peterson '36 Professorship and directs the university's graduate studies in petroleum engineering. His research focuses on reservoir modeling, control optimization, model reduction techniques, and CO2 sequestration. Gildin has pioneered data-driven approaches for reservoir simulation, integrating machine learning and physics-based models to enhance efficiency and accuracy. Education: Ph.D. in Aerospace Engineering, University of Texas at Austin (2006) M.S. in Mechanical Engineering, University of São Paulo, Brazil (1998) B.S. in Mechanical Engineering, Faculdade de Engenharia Industrial, Brazil (1995) Research Interests: Model reduction of large-scale dynamical systems Control and optimization of reservoir operations CO2 storage and geological carbon sequestration Machine learning applications in reservoir engineering and drilling automation Geomechanics and compaction damage evaluation Key Awards: 2020: William O. and Montine P. Head Memorial Research Award 2017-2018: Dean of Engineering Excellence Award 2013-2019: Energi Simulation Chair in Robust Reduced Complexity Modeling 2021: Distinguished Membership in Society of Petroleum Engineers Grants and Advising: Gildin has secured major funding for projects on reservoir simulation, drilling automation, and CO2 storage. He advises graduate students on topics such as surrogate modeling and reinforcement learning applications in petroleum systems. His lab collaborates with industry partners to translate research into practical tools for reservoir management and subsurface operations. Labs and Teams: He leads the Reservoir Simulation and Control Lab, focusing on advanced computational methods for reservoir optimization. His team develops open-source drilling models and collaborates globally on projects like the DREAMS (Drilling and Extraction Automated System) initiative.
Bo Guo is Associate Professor of Hydrology and Atmospheric Sciences at the University of Arizona, with joint appointment in Applied Mathematics. His research develops computational models for fluid flow and contaminant transport in geological systems. Research focuses on subsurface hydrology, particularly PFAS contamination mechanisms, multiscale porous media flow, and environmental remediation strategies. Recent work advances machine learning approaches for flow simulation and interfacial contaminant behavior in vadose zones. Publications address practical environmental challenges including groundwater contamination, carbon sequestration, and shale gas production. Research integrates experimental validation with computational modeling across scales from pore-level to field applications.
Roger Beckie is a Professor in the Department of Earth, Ocean & Atmospheric Sciences at the University of British Columbia (UBC), part of the Faculty of Science. His research focuses on physical, geochemical, and biological processes in environmental systems, particularly in hydrogeology, mine drainage, and groundwater contamination. He holds a B.A.Sc. from the University of Waterloo and a Ph.D. from Princeton University, and is a Professional Engineer (P.Eng.). Key research areas include fugitive gas migration from energy wells, mine waste rock geochemistry, and groundwater hydrology. His work addresses challenges such as acid rock drainage prediction, metal attenuation mechanisms, and the impacts of subsurface gas migration in petroleum development regions. He collaborates with industry and government to advance environmental management strategies and has contributed to large-scale field experiments, including controlled gas release studies in northeastern British Columbia. Beckie supervises graduate students in interdisciplinary projects, emphasizing field investigations and numerical modeling. He is affiliated with UBC's Institute of Applied Mathematics and teaches advanced courses in groundwater hydrology and contamination. His research integrates hydrological, geochemical, and isotopic tools to address complex environmental systems, with applications in mining, oil and gas, and groundwater sustainability.
Chris Valentin Nielsen is an Associate Professor in the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU). His research focuses on metal forming, joining processes, and tribology, with expertise in formability, tool development, and numerical modeling. His work contributes to UN Sustainable Development Goals related to sustainable manufacturing. He supervises PhD students in projects such as sustainable busbars for electric vehicles and adjustable tool design for high-volume production. His research interests include metal forming (e.g., deep drawing, ironing), joining technologies (resistance welding, laser welding), and advanced manufacturing methods like additive manufacturing. He employs finite element modeling and experimental analysis to bridge fundamental and applied research. Collaborations span global institutions, addressing challenges in material behavior, process optimization, and tool durability. Recent publications explore topics such as dieless Nakajima testing for additive materials, punch design improvements, and asperity deformation mechanics. His work emphasizes sustainability, robust production systems, and eco-friendly lubrication solutions. Projects involve interdisciplinary teams, integrating numerical simulations with industrial applications to enhance manufacturing efficiency and material performance.
Yashar Mehmani is an Assistant Professor in the Leone Family Department of Energy and Mineral Engineering at Pennsylvania State University. His research focuses on porous media flow and mechanics, with an emphasis on multiscale computing to bridge microscale physics and macroscale observations. Applications include subsurface energy systems, CO2 storage, and groundwater contamination. He holds a faculty position within the College of Earth and Mineral Sciences and is affiliated with the IEE (Institute for Energy and the Environment). His research interests span computational geomechanics, multiphase flow modeling, and pore-scale to continuum upscaling. Recent work includes developing machine learning-enhanced preconditioning methods for porous media simulations and kinetic theories for bubble dynamics in subsurface systems. He has received a NSF CAREER Award (2022) for integrating computational and experimental approaches in porous material studies. Key Projects: Impact of CO2 Mineralization on Microstructure Evolution, Multiscale Preconditioning Algorithms Grants: NSF CAREER Award (2022), IEE Seed Grants (2023) Dr. Mehmani collaborates with interdisciplinary teams on energy transition challenges. His lab develops advanced numerical tools for simulating subsurface processes at multiple scales, with applications in carbon sequestration and geothermal energy systems.
Anirban Mondal is an Associate Professor and Director of Graduate Studies at Case Western Reserve University's Department of Mathematics, Applied Mathematics and Statistics, specializing in Bayesian Inference, Markov Chain Monte Carlo Methods, and Uncertainty Quantification. Holding a Ph.D. in Statistics from Texas A&M University, his research spans spatial statistics, inverse problems, and data mining applications across biomedical, materials science, and public health domains. Education: Ph.D. in Statistics, Texas A&M University His recent publications (2022-2024) demonstrate interdisciplinary applications including heart disease prediction via optimized machine learning, additive manufacturing defect analysis, and pandemic transmission modeling. While primarily focused on Bayesian frameworks and computational statistics, his work extends to geomechanics, remote sensing, and environmental risk assessment. Current research explores advanced sampling algorithms, functional data emulation, and multiscale hierarchical modeling for complex systems. Key trends include uncertainty quantification in machine learning systems (2024), Bayesian calibration methods (2023), and pandemic modeling (2022). His work balances methodological innovation with real-world applications in medical diagnostics, materials science, and climate science. Contact: anirban.mondal@case.edu
Stefano Scialo' is an Associate Professor in the Department of Mathematical Sciences "G.L. Lagrange" (DISMA) at the Polytechnic University of Turin. He is also a member of the Interdepartmental Center Ec-L - Energy Center Lab and serves as the contact person for the Bachelor's Degree Program in Mathematics for Engineering (L3). His academic journey began with a Master’s in Aerospace Engineering (2007), followed by a PhD in Mathematics for Engineering (2014), both from the same institution. After his PhD, he held postdoctoral and Assistant Professor positions at DISMA before being promoted to Associate Professor. Education: PhD in Mathematics for Engineering, Politecnico di Torino, 2014 Master in Aerospace Engineering, Politecnico di Torino, 2007 His research focuses on advanced numerical methods for partial differential equations, particularly in the context of complex multiscale and multiphysics systems. Key areas include the Virtual Element Method (VEM), domain decomposition techniques based on PDE-constrained optimization, and the simulation of flows in fractured porous media. He has made significant contributions to 3D-1D coupled problems, with applications in geosciences and biomedical modeling such as tumor-induced angiogenesis. His methodological work emphasizes robustness, scalability, and applicability to non-conforming and polygonal meshes, enabling high-performance computing solutions. The trend in his recent publications reveals a strong emphasis on developing and analyzing mixed virtual element methods, optimization-based coupling strategies, and their applications to engineering and biological systems. His work bridges theoretical numerical analysis with practical implementations in fluid dynamics and subsurface flow. Scientific Contributions: Principal Investigator of the FREYA project (2023–2026) on hybrid numerical approaches for fault reactivation. Coordinator of the INdAM-GNCS research project (2018–2019). Member of the research group "Numerical Analysis and Scientific Computing" at DISMA. Stefano Scialo' actively supervises doctoral students, including Matteo Trombini in the PhD program in Mathematical Sciences. He teaches a range of courses such as Advanced Scientific Programming in MATLAB, Numerical Methods and Scientific Computing, and specialized topics on Virtual Element Methods. He also contributes to curriculum development and academic governance through roles in doctoral colleges and degree program committees, including those for Mathematical, Mechanical, Aerospace, and Automotive Engineering. Laboratories and Research Groups: Member, Interdepartmental Center Ec-L - Energy Center Lab Research Group: Numerical Analysis and Scientific Computing (DISMA)
Dr Andrew Starkey is a Reader in the School of Engineering at the University of Aberdeen, where he also completed his PhD in 2001. He holds an Honours degree in Applied Mathematics from the University of St Andrews. He is actively involved in research and currently accepting PhD students in Engineering. His work bridges academia and industry, with a focus on AI applications in engineering, bioinformatics, and geosciences. University: University of Aberdeen School: School of Engineering Academic Rank: Reader Email: a.starkey@abdn.ac.uk Phone: +44 (0)1224 272801 Dr Starkey's research centers on Explainable AI (XAI) , Green AI , and Autonomous AI , with applications in robotics, econometrics, bioinformatics, seismic data analysis, and virtual reality. He has developed novel methods for feature selection, autonomous learning, and knowledge abstraction from agent-environment interactions. His work emphasizes low computational cost and transparency in AI systems. The most recent publications reflect a strong trend in applying AI to complex real-world problems, including digital rock technology, robotic grasping, real-time event detection, and medical data analysis. His interdisciplinary research combines machine learning with domain-specific knowledge in engineering and life sciences, often resulting in practical, industry-ready solutions. Millennium Product Award John Logie Baird Award for Innovation Enterprise Fellowship from Royal Society of Edinburgh and Scottish Enterprise Dr Starkey has supervised multiple research projects and secured funding from major bodies including EPSRC, BBSRC, and industry partners. His past work on the GRANIT project led to the development of AI-based condition monitoring for ground anchorages, resulting in commercialization through BlueFlow Ltd. He has collaborated with researchers across disciplines, including Dr Alasdair MacKenzie (bioinformatics), Dr Anne Schwab (seismic analysis), and Dr David Hazlerigg (genomics). He leads research in AI-driven engineering solutions and is the CEO of BlueFlow Ltd, a spinout company commercializing AI technologies developed at the University of Aberdeen. His lab focuses on developing autonomous, explainable, and environmentally sustainable AI systems for real-world deployment.
