جست‌وجوی استاد و پژوهشگر

Kevin Clarnoمشاهده پروفایل

دانشیار

• Nuclear and Radiation Engineering
• Computational Nuclear Energy
• Multiphysics Simulation
• +۵ مورد دیگر

Kevin Clarno is a tenured Associate Professor in the Department of Nuclear and Radiation Engineering at the University of Texas at Austin, holding the Charlotte Maer Patton Centennial Fellowship in Engineering. His research focuses on computational nuclear energy, multiphysics reactor simulation, and high-performance computing (HPC). Previously, he spent 15 years at Oak Ridge National Laboratory (ORNL), where he led major initiatives such as the Consortium for Advanced Simulation of Light Water Reactors (CASL) and contributed to the development of software tools like SCALE, CTF, and VERA. Education and Career: Assistant Professor at University of Tennessee-Knoxville (2010–2016) Senior Research Scientist at ORNL (2006–2021) Research Interests: Multiphysics coupling methods for reactor simulation Multiscale neutronics and thermal-hydraulics modeling Advanced reactor design (e.g., molten salt reactors) HPC-driven software integration for nuclear analysis Uncertainty quantification in coupled simulations Grants and Projects: Lead of CASL’s Physics Integration Focus Area Development of the Advanced Multi-Physics (AMP) fuel code ORNL-led strategic research projects in reactor simulation Labs and Tools: VERA: Virtual Environment for Reactor Applications CTF: Thermal-hydraulic solver for PWR analysis MPACT: Neutronics simulation tool within SCALE

Travis B. Thompsonمشاهده پروفایل

استادیار

• Mathematical Modeling
• Scientific Computing
• Machine Learning in Medicine
• +۵ مورد دیگر

Travis B. Thompson, Ph.D. is an Assistant Professor in the Department of Mathematics and Statistics at Texas Tech University, leading the TM4 (Texas Tech Translational and Theoretical Mathematical Modeling and Machine Learning in Medicine) research group. His academic journey includes postdoctoral work at Rice University, Simula Research Laboratory, and the University of Oxford, focusing on mathematics applied to neurodegenerative diseases. Education: Ph.D. in Mathematics from Texas A&M University (2013) Dr. Thompson develops theoretical mathematical models and applies scientific computing and machine learning to study neurological pathologies, particularly Alzheimer’s disease. His work explores complex biological processes on networks, translational healthcare applications, and nutritional security implications. Current research trends integrate neuroimaging data with finite element simulations to model tau progression , amyloid beta dynamics , and glymphatic clearance in age-related diseases. Scientific awards and honors were not explicitly mentioned in the provided materials. Dr. Thompson’s interdisciplinary approach connects computational neuroscience with biomedical engineering , utilizing techniques like diffusion tensor imaging and level set methods to analyze pathological protein spread and brain tissue mechanics . The TM4 research group focuses on network neurodegeneration , personalized medicine , and machine learning diagnostics . Their work spans from microfluidic cancer detection to computational modeling of brain clearance mechanisms , addressing challenges in both neurodegenerative diseases and biomedical engineering through rigorous mathematical frameworks.

Johann Guilleminotمشاهده پروفایل

دانشیار

• Computational Mechanics
• Materials Science
• Mechanics of Heterogeneous Materials
• +۸ مورد دیگر

Johann Guilleminot is an Associate Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science at Duke University. He joined Duke in 2017 after a Maître de Conférences position at Université Paris-Est. His research bridges computational mechanics, materials science, and uncertainty quantification, with applications in additive manufacturing, biomedical implants, and naval systems. Education: M.S. in Theoretical Mechanics, Lille University of Science and Technology (2005) Ph.D. in Theoretical Mechanics, Lille University of Science and Technology (2008) Habilitation in Mechanics, Université Paris-Est (2014) His work focuses on probabilistic methods for heterogeneous materials, stochastic solvers, and scientific machine learning. Recent projects include data-driven uncertainty quantification in molecular dynamics and additive manufacturing simulations, funded by the Army Research Office, NSF, and national laboratories. Scientific Awards: No specific awards listed in the provided text. Lab & Collaborations: Leads the Guilleminot Lab at Duke, collaborating with Sandia National Laboratories and the U.S. Naval Research Laboratory. Research spans atomistic-to-continuum coupling, inverse problems, and stochastic modeling for predictive simulations.

Barbara Wohlmuthمشاهده پروفایل

استاد

• Numerical Simulation
• Partial Differential Equations
• Discretization Techniques
• +۴ مورد دیگر

Prof. Barbara Wohlmuth is a full professor in Numerical Mathematics at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology. She leads the International Graduate School of Science and Engineering at TUM and has held professorships at Stuttgart, Darmstadt, and Berlin universities. Her research focuses on numerical simulation of partial differential equations, multiscale solvers, and coupled multi-field problems with applications in engineering. Education: Studied mathematics at TUM and Université Joseph Fourier in Grenoble, received her doctorate from TUM in 1995, and completed habilitation in Augsburg. Visiting professorships in USA, France, and Hong Kong. Research interests include discretization techniques, predictive modeling, and interdisciplinary collaboration with engineering disciplines. Notable achievements: 2012 Gottfried Wilhelm Leibniz Prize (Germany’s highest academic honor in sciences), 2005 Sacchi-Landriani Prize. Publications emphasize advanced numerical methods in fluid dynamics, geophysics, and biomedical engineering. Active in editorial roles for international journals and scientific committees across Europe and USA. Elected member of Bavarian and European Academies of Sciences. Key contributions include: Development of robust numerical algorithms for exascale simulations Pioneering work in coupled multi-physics modeling Innovative methods for computational contact mechanics Leadership in graduate education initiatives

Benjamin Stammمشاهده پروفایل

استاد

• Numerical Analysis
• Scientific Computing
• High-Performance Computing (HPC)
• +۷ مورد دیگر

Prof. Benjamin Stamm is a Professor of Numerical Mathematics at the University of Stuttgart, leading the Chair of Numerical Mathematics for High Performance Computing within Faculty 08. He holds a Ph.D. and master's degree in mathematics from École Polytechnique Fédérale de Lausanne (EPFL) and has previously worked at RWTH Aachen University, Sorbonne Université UPMC Paris 6, UC Berkeley, and Brown University. His research focuses on numerical analysis, scientific computing, and simulations, particularly efficient discretizations for PDEs, eigenvalue problems, error certification, reduced basis methods, and HPC implementations. He develops scalable numerical methods for problems in computational chemistry and physics, emphasizing accuracy, efficiency, and interdisciplinary collaboration with chemists, physicists, and materials scientists. Prof. Stamm’s work includes contributions to domain decomposition methods, polarization energy calculations, and software development like the ddX library. His publications span topics such as model order reduction, quantum simulations, and molecular dynamics. Collaborations and software tools underscore his commitment to bridging computational methods with real-world scientific challenges.

Matthias S. Maierمشاهده پروفایل

دانشیار

• Multiscale Methods
• Computational Fluid Dynamics
• Finite Element Methods
• +۳ مورد دیگر

Matthias S. Maier is an Associate Professor in the Department of Mathematics at Texas A&M University, affiliated with the College of Arts & Sciences. His research focuses on multiscale methods, computational fluid dynamics, and finite element software development, particularly with the deal.II library. He organizes an annual undergraduate summer school on PDE modeling and simulation. Research Interests: Multiscale effects in Maxwell’s equations, computational fluid dynamics, finite element methods, and numerical analysis. His work includes studies on surface plasmon-polaritons, homogenization theory, and high-performance computing for hyperbolic systems. Developed ryujin, a high-performance finite-element solver for compressible flows. Contributed to deal.II, a widely used open-source finite element library. Recipient of NSF awards (DMS 1912847, DMS 2045636) and AFOSR funding. Teaching includes courses on numerical methods (Math 417, 610), mathematical modeling (Math 442), and finite element methods (Math 676). He advises graduate students in applied mathematics and computational science. Labs/Teams: Core developer of deal.II and contributor to the ryujin framework. Collaborates with interdisciplinary teams in physics and engineering.

Sonia Flissمشاهده پروفایل

استاد

• Wave Propagation
• Mathematical Analysis
• Numerical Analysis
• +۵ مورد دیگر

Sonia Fliss is a Full Professor at ENSTA Paris , affiliated with the Department of Applied Mathematics and the POEMS laboratory (UMR CNRS-INRIA-ENSTA) . She teaches applied mathematics courses on partial differential equations (PDEs) , finite element methods , and periodic homogenization to undergraduate and graduate students. Doctor in Applied Mathematics (2009) Authorized to supervise research (2019) Research Interests : Sonia Fliss specializes in the modeling and numerical analysis of wave propagation in periodic, quasi-periodic, and random media . Her work includes transparent boundary conditions , guided waves , and asymptotic methods for acoustic, electromagnetic, and elastic wave phenomena . Recent Publications highlight her contributions to the Half-Space Matching Method , edge states in honeycomb structures , and scattering problems in unbounded domains . Her numerical techniques address multi-scale waveguides and time-harmonic propagation . Laboratory : As a member of the POEMS team, she collaborates on interdisciplinary projects involving mathematical analysis , computational physics , and engineering applications in domains like defence, energy, and transport .

Sébastien Tordeuxمشاهده پروفایل

دانشیار

• Numerical Analysis
• Wave Propagation
• Electromagnetic Theory
• +۳ مورد دیگر

Sébastien Tordeux is an Associate Professor at the University of Pau, affiliated with the Magique 3D research team in the Faculty of Sciences. His research focuses on numerical methods for wave propagation, particularly Trefftz methods, asymptotic expansions, and electromagnetic/acoustic scattering. He holds a PhD in Applied Mathematics from the University of Versailles Saint-Quentin and has held positions at INSA-Toulouse and ETH Zurich. He has advised multiple PhD students and organized conferences such as the 2017 conference in honor of Abderrahmane Bendali. His work emphasizes high-order numerical modeling and efficient solvers for wave equations in complex media. Education: PhD in Applied Mathematics (2004, Université de Versailles), DEA M2SAP (2001), ENSTA Engineer (1998-2001). Professional roles include Chair of Excellence in Numerical Analysis (INRIA-UPPA 2010-2015), and leadership in Master’s program administration and national academic committees (CNU 26). Research interests span Trefftz methods for time-harmonic models, numerical solvers reducing pollution effects, and asymptotic modeling for small obstacle scattering. Recent contributions include quasi-Trefftz methods for electromagnetic systems and iterative approaches for 3D wave simulations. He has supervised PhD students working on topics like antenna patch models, perforated plate acoustics, and multiscale electromagnetic diffraction. His collaborative projects involve institutions like ONERA and TU Berlin, focusing on high-accuracy wave modeling and computational methods.

• Computational Mathematics
• Applied Mathematics
• Partial Differential Equations
• +۷ مورد دیگر

Frédéric Valentin is a Senior Researcher at the National Laboratory for Scientific Computing (LNCC) in Brazil, where he led the Department of Computational and Mathematical Methods from 2015 to 2021. He holds an INRIA International Chair (2018–2023) and served on the applied mathematics board of Brazil's National Science Foundation (CNPq) from 2017 to 2020. He currently leads the IPES Research Group and acts as the Brazilian Scientific Director for the Inria-Brasil partnership. His work focuses on computational and applied mathematics, particularly in developing innovative numerical methods for multiscale phenomena in engineering and life sciences. National Laboratory for Scientific Computing - LNCC (2015–2021) INRIA International Chair (2018–2023) Inria-Brasil Partnership (Scientific Director) Valentin's research involves partial differential equations, finite element methods, domain decomposition, and numerical analysis. He explores the integration of numerical algorithms with machine learning and high-performance computing, including applications to supercomputing projects like the Brazil-European Community's HPC4E initiative. His publications span over 60 high-impact journal articles and book chapters, primarily in computational mathematics and numerical analysis. He has co-advised over a dozen PhD students and postdoctoral researchers, contributing to international collaborations and advancing multiscale modeling techniques. Notably, he played a pivotal role in developing the Santos Dumont petaflop supercomputer at LNCC, the most advanced in Latin America.

• Partial Differential Equations
• High Performance Computing
• Discontinuous Galerkin Methods
• +۳ مورد دیگر

Maurice S. Fabien is an Assistant Professor in the Department of Mathematics at the University of Wisconsin-Madison and a SIAM-MGB Early Career Fellow. In 2025, he will join MIT's Schwarzman College of Computing as an MLK Assistant Professor while on leave from UW-Madison. His research focuses on computational mathematics with specialization in partial differential equations, high-performance computing, and numerical methods including discontinuous Galerkin formulations and multigrid solvers. Research interests span: Development of structure-preserving discretizations for hyperbolic systems GPU-accelerated computational algorithms Hybridizable discontinuous Galerkin (HDG) frameworks Multiscale modeling in porous media and biomechanics Numerical analysis of nonlinear PDEs Publications demonstrate strong focus on: High-order methods for conservation laws Efficient solvers for elliptic/parabolic systems Applications in fluid dynamics and materials science GPU-based performance optimization Error analysis of energy-stable schemes Awards & Honors: SIAM-MGB Early Career Fellowship (2025) Research Team: Austin Anyanwu (Undergraduate) - Finite precision arithmetic Alexis Liu (Alumni) - GPU-accelerated elliptic solvers Patrick Li (Undergraduate) - GPU-based mesh refinement Neer Mehta (Alumni) - Adaptive mesh refinement algorithms Significant involvement since 2007 in STEM diversity initiatives focused on recruitment/retention of underrepresented groups in academia.

Zhen Liمشاهده پروفایل

استادیار

• Multiscale/Multiphysics modeling
• Soft matter and Smart materials
• Biophysics and Collective dynamics
• +۷ مورد دیگر

Dr. Zhen Li is an Assistant Professor in the Department of Mechanical Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. He joined Clemson in August 2019 after serving as a research associate professor at Brown University and a postdoctoral research associate at University of California, Merced. Education: Ph.D. in Fluid Mechanics, Shanghai University, 2012 MS in Fluid Mechanics, Shanghai University, 2008 BS in Engineering Mechanics, Wuhan University, 2005 Dr. Li's research focuses on multiscale modeling of soft matter, complex fluids, biophysics, and collective dynamics using both bottom-up (coarse-grained molecular modeling) and top-down (from continuum descriptions to fluctuating hydrodynamics) approaches, along with high-performance computing. His work spans mathematical theory for coarse-graining and model reduction, statistical methods and machine-learning approaches applied to multiscale modeling, memory effects in complex fluids, and concurrent coupling of heterogeneous solvers for scale-bridging. Analysis of Dr. Li's recent publications reveals a strong trend toward integrating machine learning with traditional computational methods, particularly neural operators for multiscale problems. His work spans diverse applications from bubble dynamics and blood flow to materials science and bioprinting, demonstrating the versatility of his computational approaches across multiple disciplines in engineering and physics. Awards and Recognition: CECAS Dean's Professor Award (2024) Award of Excellence - Junior Faculty (2021-2022) Best Research Poster Award at SC19 (2019) 2nd Place Award of Best Poster Presentation at DOE/EFRC AIM for Composites meeting (2024) Dr. Li actively mentors PhD students including Miles Lu, Ryan Wan, Haizhou Wen, and Ali Mohammadi, who have published significant research in computational mechanics. His research is supported by multiple grants including an NSF Elements grant as PI for 'SciMem: Enabling High Performance Multi-Scale Simulation on Big Memory Platforms', an NSF CDS&E grant as co-PI for 'HAM3R: Heterogeneous Automated Management of Multiscale Methods and Resources', a DOE/EFRC grant as Thrust lead co-PI for 'AIM for Composites', and a NASA EPSCoR grant as Science-PI. Dr. Li leads the MuthComp (Multiscale theory and Computation) research group, which focuses on developing interfaces between Engineering, Applied Mathematics, Physics-based Machine Learning, and High Performance Scientific Computing. The group has active collaborations with institutions including Idaho National Laboratory, University of Tokyo, and Brown University, and has developed open-source software including USERMESO for GPU-accelerated DPD simulations.

Nicholas Kevlahanمشاهده پروفایل

استاد

• Applied Mathematics
• Computational Fluid Dynamics
• Geophysical Fluid Dynamics
• +۵ مورد دیگر

Dr. Nicholas Kevlahan is a Professor in the Department of Mathematics and Statistics at McMaster University. He holds a BSc in Physics from the University of British Columbia (1985–1989), a PhD in Applied Mathematics from the University of Cambridge (1990–1994), and completed a Marie Curie postdoctoral fellowship at École Normale Supérieure in Paris (1998). He has held visiting positions at institutions including Université Grenoble-Alpes, INRIA, and the University of Cambridge. His research focuses on advanced computational and mathematical methods for fluid dynamics, including the development of the WAVETRISK code for adaptive climate modelling, data assimilation techniques, fluid-structure interaction, and compressive sampling. His work integrates interdisciplinary approaches across applied mathematics, geophysics, and engineering. Key research areas include geophysical fluid dynamics, numerical analysis, partial differential equations, and wavelet-based methods. He has published extensively on topics such as ocean circulation models, turbulence simulation, and adaptive numerical methods. Dr. Kevlahan teaches courses in numerical methods, differential equations, asymptotic analysis, and mathematical physics. He mentors a diverse group of PhD, MSc, and BSc students in his active research laboratory.

Leslie Greengardمشاهده پروفایل

استاد

• Integral Equation Methods
• Electromagnetics
• Acoustics
• +۵ مورد دیگر

Leslie Greengard is a Silver Professor of Mathematics and Computer Science at New York University's Courant Institute, part of the Faculty of Arts and Science. His research focuses on integral equation methods for electromagnetics, acoustics, plasma physics, and fluid dynamics, with recent work extending to kernel-based methods in statistical inference. He leads the Courant Mathematics and Computing Laboratory and contributes to novel electromagnetic simulation techniques. Education: Ph.D. and M.D. in Computer Science and Medicine from Yale University (1987) B.A. in Mathematics from Wesleyan University (1979) Research Interests: Greengard's group develops fast solvers for complex geometries in electromagnetics and fluid dynamics. Notable contributions include the Fast Multipole Method (FMM) for particle simulations and Quadrature by Expansion (QBX) for layer potential evaluations. His work bridges computational mathematics and applications in physics, engineering, and biomedicine. Recent Article Trends: Recent publications emphasize fast algorithms (e.g., FMM, adaptive Gauss transforms), biomedical applications (e.g., deep brain stimulation modeling), and cosmological simulations. His methods address challenges in high-dimensional data, multiscale problems, and real-time computation. Awards & Recognition: While no specific prizes are listed, his foundational work on FMM has had a transformative impact on computational science. Advising & Collaborations: Collaborations span academia and industry, with co-authors including V. Rokhlin, M. O’Neil, and others. No formal advisees are listed here, though his research involves postdoctoral fellows and graduate students. Labs & Teams: Active in the Courant Mathematics and Computing Laboratory, focusing on electromagnetic simulation and design methodologies.

Lars Grasedyckمشاهده پروفایل

استاد

• Numerical Analysis
• Hierarchical Matrices
• Tensor Approximation
• +۷ مورد دیگر

Lars Grasedyck is a Professor of Numerical Analysis at RWTH Aachen University. His research focuses on hierarchical matrices, tensor approximation, and numerical methods for partial differential equations and matrix equations. He has contributed to applications in biomedical engineering, particularly EEG/MEG inverse problems, and is involved in software development (HLIB, HLIB-pro). Education: Diploma and Ph.D. in Mathematics at Christian-Albrechts-Universität zu Kiel (1998, 2001), Postdoctoral work at Max Planck Institute, Leipzig (2002-2010). Research: Specializes in high-dimensional numerical methods, low-rank matrices, and tensors with applications in PDEs, uncertainty quantification, and biomedical modeling. Projects: Leads DFG-funded initiatives on adaptive tensor networks for parametric PDEs and tumor progression modeling. Advising: Supervises doctoral students including Thong Le, Maren Klever, and Dieter Moser. Software: Developed HLib and HLib-pro for hierarchical matrix computations. Conferences: Active in GAMM Fachausschuss Numerische Analysis, organizing workshops and symposia globally.

Rafael Sebastianمشاهده پروفایل

استاد

• Cardiac Computational Modeling
• Artificial Intelligence in Medicine
• Medical Imaging
• +۴ مورد دیگر

Rafael Sebastian is a Full Professor at Universitat de Valencia and General Director for Science and Research of the Generalitat Valenciana. He leads the Computational Multiscale Simulation Lab (CoMMLab) and collaborates with institutions like Oxford University and Yale University. Department of Computer Science, Universitat de Valencia CoMMLab Founder Spanish Network of Excellence in Cardiac Modeling His research focuses on multi-scale computational models and artificial intelligence for patient-specific cardiac simulations , aiming to improve arrhythmia risk stratification and therapy planning . Key topics include cardiac conduction system modeling , scar-related ventricular tachycardia , and machine learning pipelines for clinical applications. Recent publications emphasize automata-based simulations for atrial arrhythmias, machine learning in arrhythmia localization, and 3D geometric characterization of aortic diseases. Trends show integration of computational modeling with clinical data and medical imaging . Scientific Awards: Best Poster Award, Functional Imaging and Modeling of the Heart (2021) Cum Laude Award, SPIE Medical Imaging (2009) Student Presentation Award (2011) He has supervised 7 PhD/Master students and led grants exceeding €1 million, including projects like iSARC-GENETICS and iCardioTwins , focusing on digital twin technology and cardiac disease stratification .