Peter K. Kitanidis is a Professor in the Department of Civil and Environmental Engineering and the Institute for Computational and Mathematical Engineering at Stanford University . His research focuses on groundwater flow , hydrologic forecasting , and stochastic inverse modeling , with applications to pollutant remediation and CO₂ storage monitoring . Education : Diploma, National Technical University of Athens (1974) M.S., MIT (1976) Ph.D., MIT (1978) Research Interests : Groundwater modeling and contaminant transport Hydraulic tomography and aquifer characterization Stochastic methods for uncertainty quantification Bioremediation and enhanced in-situ pollutant decay Dilution and mixing processes in heterogeneous media Real-time river flow forecasting Scientific Awards : L.G. Straub Award (1979) W.L. Huber Research Prize (1994) ISI Highly Cited Researcher (2001) AGU Hydrologic Sciences Award (2011) ASCE Pioneers in Groundwater Lecturer (2011) Advising and Grants : Advised 20+ PhD and MS students (1978–2018) Principal investigator on NSF, EPA, and DOE-funded projects Developed software for groundwater data analysis and CO₂ monitoring Contributed to bioremediation protocols and hydraulic tomography algorithms Labs and Teams : Kitanidis Laboratory for groundwater crisis solutions Collaborated with Oak Ridge National Laboratory and Stanford Hydrogeology Group Mentored postdocs (2000–2017) in reactive transport and inverse modeling
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.
Dr. Jean-Christophe Nave is an Associate Professor in the Department of Mathematics and Statistics at McGill University, specializing in applied mathematics, numerical analysis, and computational methods. His research focuses on numerical methods for partial differential equations, fluid mechanics, interface problems, and computer graphics. He holds a PhD from UCSB (2004) and has held academic positions at MIT and McGill since 2005. Currently, he serves on committees such as the Steering Committee of the Institut des Sciences Mathematiques and the CRM Applied Mathematics Lab. His educational background includes a PhD under Professors Xu-Dong Liu and Sanjoy Banerjee. Key research areas include level set methods, fluid-structure interaction, and invariant numerical methods. Notable works include the Correction Function Method for interface problems and the Characteristic Mapping Method for advection problems. Nave’s publications span topics like Poisson equations with discontinuous coefficients, fluid dynamics simulations, and high-order numerical schemes. He has advised numerous graduate and undergraduate students, contributing to their research in applied mathematics and computational science. His work bridges theoretical rigor and practical applications in engineering and physics. He teaches advanced courses such as Numerical Analysis I/II and Computational Methods in Applied Mathematics. His research group collaborates on projects involving fluid dynamics, elasticity, and geometric algorithms, with a focus on developing robust numerical tools for complex systems.
Anne BOUTIN is a Research Director at the French National Center for Scientific Research (CNRS) and Professor at the École Normale Supérieure (ENS) in Paris, France. She leads the Department of Chemistry at ENS, focusing on the thermodynamics of confined fluids and molecular simulations of porous materials like metal-organic frameworks (MIL-53, ZIF-8) and zeolites. Education: Habilitation (1999), PhD in Chemical Physics (1992), Graduate of École Normale Supérieure (1992), MSc (1990) – all from University of Paris XI, Orsay. Positions: CNRS Research Fellow (1994–2009, University of Paris XI; 2009–present, ENS), Visiting Scientist at UC Santa Barbara (1999), Postdoctoral at Imperial College London (1993–1994). Research spans thermodynamic modeling of adsorption-induced structural transitions, polarizable force fields for charged materials, fluid dynamics in nanopores, and stability analysis of flexible frameworks. Google Scholar highlights recent work on electrolyte intrusion (2023) and defect impacts in zeolites (2023). Publications emphasize computational approaches to water confinement , gas separation , and material flexibility . Her 41+ peer-reviewed articles reflect expertise in Monte Carlo , molecular dynamics , and force field development . Awards: Nathalie Demassieux PhD Award (1993) CNRS Bronze Medal (1999) Legion of Honor (2017) Teaching: Statistical thermodynamics. Students: Supervised 17 PhD/postdoc directions.
Mohamed Amara is a full-time Professor at the University of Pau and the Pays de l'Adour (UPPA) since 1996, affiliated with the Laboratory of Mathematics and their Applications (CNRS-UMR 5142). He served as its director (1999-2007), Director of the Doctoral School of Exact Sciences (ED211, 2007-2008), and UPPA's Scientific Council Vice-President (2008-2012). He has been UPPA's President since 2012 (re-elected until 2020). Education: Mathematics from University of Algiers (1973), Pierre and Marie Curie University (DEA 1974, Doctorate 1978, State Doctorate 1983) Academic Roles: Research Associate at Ecole Polytechnique (1978-1982), Algerian Electricity and Gas Company (1983-1992), Professor in Algiers (1988-1994), Tunis (1994-1995), and Associate Professor at Paris 6 (1995-1996) His research focuses on numerical simulation of partial differential equations for environmental/energy applications, including mechanics in porous media (petroleum engineering, geoscience), fluid mechanics (aerodynamics, estuarine hydrodynamics), non-Newtonian flows, and wave propagation. Articles highlight expertise in discontinuous Galerkin methods, Helmholtz problems, finite element discretization, and multiphysics systems. He managed 20 doctoral theses and led national mathematics programs at ANR (2007-2011). He chairs the Cocktail association for higher education IT systems and collaborates with INRIA's Magique 3D team (since 2006).
Soumya Dutta is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur (IITK), where he leads the INSIGHT: Intelligent Scientific and Visual Computing of Big Data Research Group. He joined IIT Kanpur in October 2022 after working as a Scientist II at Los Alamos National Laboratory (LANL) from July 2019 to August 2022, and previously as a Postdoctoral Research Associate at LANL from June 2018 to July 2019. His educational background includes a Ph.D. and M.S. in Computer Science and Engineering from The Ohio State University (2011-2018), where he was part of the GRAVITY research group, and a B.Tech. in Electronics and Communication Engineering from West Bengal University of Technology, India (2005-2009). Research Interests: Dr. Dutta's research focuses on the intersection of machine learning, visual computing, big data, and high-performance computing. His primary research areas include Machine Learning for Visual Computing and Image Analysis, Big Data Visualization and Analytics, Data Science and HPC, Machine Learning for Scientific Computing, and Explainability and Interpretability of AI Models. His work addresses various big data characteristics including the 5 Vs: Volume, Velocity, Variety, Veracity, and Value. He develops techniques that make complex machine learning models more interpretable and explainable, enabling their effective adoption in real-life applications across scientific domains, social media, IoT, healthcare, and industry applications. Dr. Dutta's research group has secured multiple funded projects including: DAVi: An Intelligent Data Analytics and Visualization Framework (funded by ISRO), Intelligent Visual Computing of Extreme-scale Data for Accelerating Scientific Discovery (IIT Kanpur Initiation Grant), Enabling Interactive Big Data Analytics and Visualization at Exascale (SERB), Development of AI-Enabled National Portal for Efficient Search of Missing People (C3iHub), and Proactive and Generalized Deepfake Defense Mechanisms (C3iHub). Best Reviewer, Honorary Mention Award for IEEE Transactions on Visualization & Computer Graphics (TVCG), 2021 Best Paper Award at ISAV 2021, co-located with Supercomputing (SC) LAAP Award at Los Alamos National Laboratory, 2021 Best Paper Award at TopoInVis 2019 Best Paper Award at ISAV 2018, co-located with Supercomputing (SC) Best Poster Award in 12th Annual CSE Student Poster Exhibition, The Ohio State University, 2018 Best Poster Award in 11th Annual CSE Student Poster Exhibition, The Ohio State University, 2017 Best Paper Honorable Mention Award at IEEE Visualization Conference (IEEE VIS) 2016 Dr. Dutta actively mentors a large group of students including Ph.D., M.Tech., and B.Tech. students. His current Ph.D. students include Shanu Saklani, Sankhadeep Bhowmick, Ananya Chaturvedi, Arpita Santra, Anubhav Dixit (co-supervised), and Robin Shah. He has supervised numerous M.Tech. students with thesis topics ranging from uncertainty-aware neural networks to deepfake detection. Dr. Dutta currently teaches courses including CS360 - Introduction to Computer Graphics and CS661 - Big Data Visual Analytics. The INSIGHT research group collaborates internationally with researchers from Meta, Oak Ridge National Laboratory, and National Taiwan Normal University. The group's work focuses on building machine learning and data science-based solutions to analyze large-scale multifaceted data in a scalable way, enabling interactive and interpretable analytics of complex data from scientific simulations, social media, IoT, healthcare, and other application domains.
Dr. Yunjie Yang is an Associate Professor at the University of Edinburgh's School of Engineering, with affiliations at the Edinburgh Futures Institute (EFI), the Edinburgh Generative AI Laboratory (GAIL), and the Edinburgh Centre for Robotics. He previously held the Chancellor's Fellow in Data Driven Innovation (2018-2023) and Bayes Innovation Fellow (2023-2024) positions. His research focuses on AI-powered sensing and imaging, machine learning, and soft sensors & electronics for robotics. Yang received his PhD in Engineering Electronics from the University of Edinburgh, MSc in Control Science & Engineering from Tsinghua University, and BEng in Measurement & Control Engineering from Anhui University. After his PhD, he worked as a Postdoctoral Research Associate in Chemical Species Tomography before securing his lectureship. His research interests center on developing intelligent sensing systems that replicate human perception capabilities for robotics and intelligent systems. He pioneers flexible sensing and imaging technologies across various scales through innovative multi-modal sensors, soft electronics, and their modeling using machine learning approaches. His work aims to enable autonomous physical artificial intelligence by bridging the gap between robotic systems and human-like perception. Analysis of his recent publications reveals a strong focus on soft robotics perception, particularly through electrical impedance tomography (EIT) and transformer-based architectures. His research spans medical imaging applications, digital twin modeling for industrial processes, and machine learning approaches for sensor data interpretation. The trend shows increasing integration of physics-informed deep learning with traditional tomographic techniques to achieve higher accuracy and efficiency. European Research Council (ERC) Starting Grant (2024) IEEE J. Barry Oakes Advancement Award (2024) IEEE I&M Society Graduate Fellowship Award (2015) Multiple Best Paper Awards Senior Member of IEEE Fellow of the International Society for Industrial Process Tomography Fellow of the Higher Education Academy ESI highly cited papers Dr. Yang serves as Associate Editor for IEEE Transactions on Instrumentation and Measurement and holds editorial positions with Scientific Reports and IEEE Sensors Journal. His research has been licensed to overseas research institutes and industry partners and received wide media coverage including BBC, EFE, USA Today, and STV. He has secured significant grant funding including the prestigious ERC Starting Grant. He leads the Edinburgh SMART Lab (Sensing/imaging + Machine Learning + Robotics), which aims to replicate human perception capabilities for robotics and advance flexible sensing technologies through innovative multi-modal sensors and machine learning approaches. The lab focuses on enabling autonomous physical artificial intelligence with applications spanning medical diagnostics, industrial monitoring, and advanced robotics systems.
Howard Elman is a Professor in the Department of Computer Science at the University of Maryland, with affiliations to the Institute for Advanced Computer Studies (UMIACS) and as an Affiliate Professor in the Department of Mathematics. His research spans numerical analysis, computational fluid dynamics, and uncertainty quantification, focusing on iterative solvers for partial differential equations. Education: PhD in Computer Science, Yale University (1982); BA in Mathematics, Columbia University (1975); Stuyvesant High School (1971) Elman's research integrates Scientific Computing with Numerical Linear Algebra , Computational Fluid Dynamics , and Uncertainty Quantification . His work addresses Stochastic Galerkin Methods , Reduced-Order Modeling , and Low-Rank Approximations for PDEs with random data. Recent publications emphasize Surrogate Models and Deep Learning in Bayesian inverse problems. His scientific awards include SIAM Fellowship (2009) and roles as Associate Editor for journals like Mathematics of Computation and SIAM Journal on Scientific Computing . He served as SIAM Editor-in-Chief (1998-2004) and Vice President for Publications. Contact: helman@umd.edu | Office: 4210 Iribe Center | Courses: AMSC/CMSC 460 Computational Methods
Ronaldo I. Borja is a Professor in the Department of Civil and Environmental Engineering at Stanford University's School of Engineering. His academic career spans decades of research and teaching in theoretical and computational solid mechanics, geomechanics, and geosciences. He teaches undergraduate, graduate, and doctoral level courses including Geotechnical Engineering (CEE 101C), Mechanics and Finite Elements (CEE 281), Computational Poromechanics (CEE 314), and Plasticity Modeling and Computation (CEE 315). Professor Borja's research focuses on theoretical and computational solid mechanics, with particular emphasis on geomechanics and geosciences. His work includes the development of multi-scale discontinuity frameworks for crack and fracture propagation utilizing strong discontinuity and extended finite element methods; solution techniques for multi-physical processes such as coupled solid deformation-fluid diffusion in saturated and unsaturated porous media; stabilized finite element methods for solid/fluid interaction and nonlinear contact mechanics; and nanometer-scale characterization of the inelastic deformation and fracture properties of shales. His research spans multiple projects including shale characterization, poromechanics, and large deformation inelasticity. His recent publications demonstrate expertise across computational mechanics, geomechanics, and materials science, with a focus on finite element methods, constitutive modeling, and multi-scale analysis. His work bridges theoretical developments with practical applications in geotechnical engineering and earth sciences. 2016 ASCE Maurice A. Biot Medal for work in computational poromechanics Professor Borja serves as editor of two leading journals in his field: the International Journal for Numerical and Analytical Methods in Geomechanics and Acta Geotechnica. He has also authored the textbook 'Plasticity Modeling and Computation' published by Springer. His research is supported by multiple projects examining shale mechanics, poromechanics in unsaturated porous media, and large deformation inelasticity in crystalline materials.
Yali Tang is an Assistant Professor in the Department of Mechanical Engineering at Eindhoven University of Technology (TU/e), specializing in fluid dynamics and transport phenomena within multiphase flows and physicochemical conversions . Her work targets Iron Power technology , green steel production , and alkaline water electrolysis for hydrogen generation, combining advanced computational models with experimental validation . Education: Master's in Chemical Engineering from Sichuan University (2011) PhD in Mechanical Engineering at TU/e (2015) with Prof. Hans Kuipers Research Interests: She focuses on interphase interactions , interfacial transport mechanisms , and high-resolution simulations (down to 40 nm mesh) to predict bubble coalescence and film dynamics. Her studies on hydrogen bubble growth , dendritic iron formation , and gas distribution in electrolyzers aim to refine reactor design and industrial processes. Collaborations with industrial partners ensure practical applicability of her computational models. Recent Publications: Her 2025 work includes dimensional analysis of liquid film formation, solutal Marangoni effects in electrolysis, and X-ray validation of gas distribution models. Earlier studies (2020–2023) cover defluidization behavior of iron fines, CFD-DEM modeling of raceways, and acoustic field applications in particle dynamics. Labs & Collaborations: She leads computational efforts within the Power & Flow group under Prof. Niels Deen, contributing to the EIRES Research cluster. Her work bridges academic research with industrial innovation in fluid dynamics and energy transition technologies.
Christophe Ancey is an Associate Professor at the École Polytechnique Fédérale de Lausanne (EPFL), where he serves in the School of Architecture, Civil and Environmental Engineering (ENAC), specifically in the Institute of Civil Engineering (IIC) and the Environmental Hydraulics Laboratory (LHE). He also holds teaching appointments in SGC-Teaching and EDME-Teaching departments at EPFL. His office is located at GC A1 401, Station 18, 1015 Lausanne, Switzerland. Dr. Ancey holds both a PhD and engineering degree from Ecole Centrale de Paris and Grenoble National Polytechnic Institute. After completing his doctoral work (1994-1997) on rheology of granular flows under Pierre Evesque, he worked as a researcher at Cemagref before joining EPFL in 2004. He directs the Environmental Hydraulics Laboratory and serves as associate editor for Water Resources Research, a leading journal in hydrology. Dr. Ancey's research spans fluid dynamics, rheology, and hydraulics with emphasis on geophysical flows and natural hazards. His work focuses on three interconnected themes: rheology of concentrated suspensions (particularly granular flows in avalanches and mudflows), inverse problems in rheology (determining microscopic behavior from macroscopic measurements), and particle entrainment in turbulent suspensions (erosion and sediment transport processes). His recent publications (2022-2025) demonstrate continued innovation in sediment transport modeling, experimental techniques like PIV for complex flows, and integration of machine learning approaches. The research shows progression from fundamental fluid mechanics to practical applications in natural hazard assessment and river engineering, with particular attention to granular segregation, avalanche-obstacle interactions, and river morphodynamics. Associate Editor, Water Resources Research Co-founder of Toraval, an engineering consulting firm specializing in avalanche risk management Dr. Ancey teaches courses on Fluid Mechanics, Flood and Dam Break Waves, Hydrological Risks and Structures, and Similarity and Transport Phenomena in Fluids. He has supervised numerous PhD students whose work spans environmental hydraulics, granular flows, and sediment transport, with current students including Chen Yanan, Farazande Sofi, and Giboulot Axel Loïc among others.
N.K. Anand is a Distinguished Professor of Mechanical Engineering at Texas A&M University, holding the James J. Cain III Regents Professorship. He leads research in advanced computational methods and thermal-hydraulic systems, with affiliations to Multidisciplinary Engineering and Nuclear Engineering programs. His work focuses on physics-informed machine learning, finite volume methods, and aerosol transport in nuclear reactor contexts. Education: PhD (Mechanical Engineering, Purdue University, 1983), M.S. (Kansas State University, 1979), and B.E. (Bangalore University, 1978). Awards include the ASME James Harry Potter Gold Medal (2020) and multiple teaching/administrative excellence awards from Texas A&M. Research emphasizes fluid dynamics modeling (e.g., PINNs for periodic flows, turbulent deposition studies), heat pipe systems, and nuclear reactor thermal-hydraulics. His Versatile Test Reactor (VTR) contributions include cartridge loop designs and aerosol transport experiments. Active in high-temperature reactor safety, with facilities studying pebble beds, helical coil exchangers, and HTGR upper plenum dynamics. Publications span physics-informed ML applications, finite volume techniques, and nuclear thermal systems. Grants supported development of advanced CFD tools and reactor safety infrastructure. His lab collaborates on international nuclear energy projects and emerging AI-driven simulation methodologies.
Prof. Catherine O'Sullivan is a Professor of Particulate Soil Mechanics at Imperial College London's Department of Civil and Environmental Engineering, part of the Faculty of Engineering. She leads the Geotechnics Section and serves as Editor-in-Chief of the ASCE Journal of Geotechnical and Geoenvironmental Engineering. Her research focuses on particulate soil mechanics, employing Discrete Element Modelling (DEM) and micro-CT imaging to study sand behavior, reservoir sandstones, and internal erosion. Notable recognitions include the 2016 Shamsher Prakash Research Award and the 2021 President’s Teaching Innovation Award. Education : PhD in Civil Engineering, University of California, Berkeley (2002) MEngSc in Civil Engineering, University College Cork (Ireland) BEng (Civil Engineering), University College Cork (Ireland) Research Interests : Prof. O'Sullivan's work integrates computational and experimental methods to explore granular material behavior. Key areas include DEM validation, μCT analysis, and pore network modeling. Her group collaborates across disciplines, involving physicists and mechanical engineers alongside civil engineers. Awards & Recognition : 2015 Geotechnique Lecture Student Choice Supervision Award (nominated twice) 2023 Alert Geomechanics Special Lecture Advising & Grants : She supports PhD and postdoctoral researchers through Imperial scholarships and fellowships. Her students often explore particulate soil behavior, with many securing prestigious awards. Labs & Teams : Leads the Geotechnics Section at Imperial, fostering interdisciplinary research in geomechanics and computational modeling.
Assoc Prof Ng Teng Yong is an Associate Professor at the School of Mechanical & Aerospace Engineering (NTU), specializing in numerical modeling and simulation. With a background as Research Manager at A*STAR Institute of High Performance Computing, his work spans materials science, nanotechnology, and aerospace engineering. Current focus on graphene-based desalination membranes Expertise in molecular dynamics simulations Investigates nanoscale fluid mechanics and structural dynamics Recent publications highlight advancements in energy-efficient electrodialysis, smart robotics, and nonlinear vibration analysis. His interdisciplinary approach integrates computational methods with experimental validation in additive manufacturing and soft material mechanics.
Ivan C. Christov is an Associate Professor of Mechanical Engineering at Purdue University's School of Mechanical Engineering in West Lafayette, Indiana. His research focuses on fluid dynamics, non-Newtonian fluid mechanics, and multiphase processes, with applications in biomedical engineering, micro/nanotechnology, and advanced materials. He leads the Transport: Modeling, Numerics & Theory laboratory. Education: Ph.D., Northwestern University, 2011 M.S., Northwestern University, 2008 M.S., Texas A&M University, 2007 S.B., Massachusetts Institute of Technology, 2005 Research Interests: Soft hydraulics, computational science, scientific machine learning, nonlinear waves, and fluid-structure interactions. His work spans theoretical modeling, numerical simulation, and experimental validation in complex fluid systems. Publications: Recent work includes studies on flow-rate pressure-drop relations in deformable microchannels, physics-informed neural networks for particle dynamics, and fluid-structure interaction in cerebral aneurysms. Themes include microfluidics, elastohydrodynamics, and rheological characterization of soft materials. Awards: Fulbright U.S. Scholar (2022) Outstanding Engineering Instructor (multiple recognitions) Richard P. Feynman Distinguished Postdoctoral Fellowship (2013) Labs/Teams: Directs the Transport laboratory at Purdue, focusing on interdisciplinary research in fluid mechanics and computational methods. Collaborates on biomedical fluid dynamics and advanced materials characterization.
