جست‌وجوی استاد و پژوهشگر

Chen Greifمشاهده پروفایل

استاد

• Scientific Computing
• Numerical Linear Algebra
• Iterative Solvers
• +۳ مورد دیگر

Chen Greif is a Professor in the Department of Computer Science at the University of British Columbia (UBC). His research focuses on numerical linear algebra, iterative solvers, preconditioning techniques, and scientific computing. He has held editorial roles in SIAM journals and book series, and served as Head of the Department of Computer Science from 2016 to 2020. Greif co-authored the SIAM bestselling textbook A First Course in Numerical Methods and has published extensively in top journals. Education: Ph.D., Mathematics, University of British Columbia, 1998 M.Sc., Mathematics, Tel Aviv University, 1994 B.Sc., Mathematics, Tel Aviv University, 1991 Research Interests: Scientific computing, numerical linear algebra, iterative methods for sparse linear systems, saddle-point systems, and elliptic PDEs. His work emphasizes preconditioning techniques and numerical stability. Publications Trends: Recent work includes multigrid methods for complex systems, preconditioners for saddle-point matrices, and eigenvalue bounds analysis. His contributions bridge theory and applications in computational science and engineering. Awards: SIAM Fellow (2022) CAIMS Research Prize (2023) Multiple teaching awards (2025, 2024, 2017, 2006, 2004) Advising & Grants: Greif has advised numerous students and contributed to grants in numerical methods and computational science. He has led major conferences (e.g., International Conference on Preconditioning Techniques, 2017) and served on SIAM committees. Labs/Teams: Engaged in interdisciplinary research groups at UBC, focusing on numerical algorithms and their applications in fluid dynamics, image processing, and computational geometry.

Susanne C. Brennerمشاهده پروفایل

استاد

• Numerical Analysis
• Scientific Computing
• Finite Element Methods
• +۸ مورد دیگر

Susanne C. Brenner is a Boyd Professor at Louisiana State University, holding a joint appointment between the Department of Mathematics and the Center for Computation & Technology (CCT). She has served as Associate Director for Academic Affairs at CCT since 2008. Brenner's research focuses on numerical analysis, scientific computing, and computational methods across mechanics, electromagnetics, and PDEs. She has held leadership roles including President of SIAM (2021-2022), and has been recognized with prestigious awards such as the Humboldt Research Award and SIAM Fellowship. Editorial Roles: Managing Editor of Mathematics of Computation, Editor-in-Chief of SIAM Classics in Applied Mathematics, and member of editorial boards for SIAM Journal on Numerical Analysis, Numerische Mathematik, and others. Service: NSF MPS Advisory Committee (2016-2022), AAAS Section A Member (2017-2021), and Scientific Advisory Boards for institutions like Weierstrass Institute and Hausdorff Center for Mathematics. Her contributions span academic leadership, computational mathematics research, and international collaboration. Brenner has held visiting positions globally and serves on panels for funding agencies like the ERC and DFG.

Martin J. Ganderمشاهده پروفایل

استاد

• Numerical Analysis
• Iterative Methods for PDEs
• Domain Decomposition Methods
• +۳ مورد دیگر

Martin J. Gander is Full Professor of Mathematics at the University of Geneva (Faculty of Sciences, Department of Mathematics) and heads the Digital Analysis research group. His career has included positions at ETH Zürich, Stanford, École Polytechnique and McGill University before he moved to Geneva in 2004. Education ETH Zürich Stanford University École Polytechnique, Paris Research Interests Prof. Gander’s work lies at the intersection of numerical analysis and scientific computing , with particular emphasis on: Iterative solvers and preconditioning for large linear systems Domain-decomposition and parallel-in-time methods Absorbing boundary conditions and perfectly matched layers Waveform relaxation and multigrid techniques Geometric integration and mathematical biology Industrial Collaboration He has consulted for companies and institutions such as Venyo , Meteo Suisse , Meteorological Service of Canada , Alcan , Pratt & Whitney and Volvo . Advising & Team Prof. Gander has supervised more than twenty-five doctoral students and post-doctoral fellows, currently including Teilo Wahl , Ausra Pogozelskyte , Si-Wei Liao , Yafei Sun and Liudi Lu .

Lehel Banjaiمشاهده پروفایل

استاد

• Numerical Analysis
• Computational Mathematics
• Boundary Element Methods
• +۱۶ مورد دیگر

Professor Lehel Banjai is a faculty member at the School of Mathematical and Computer Sciences, Heriot-Watt University, where he has held the position of Professor in Mathematics since 2022. Previously, he served as Associate Professor (2015–2022) and Assistant Professor (2012–2015) at the same institution. Education: Habilitation (University of Dusseldorf, 2013), D.Phil. (Oxford, 2003), BA(Hons) in Mathematics and Computation (Oxford, 2000) His research spans numerical analysis and computational mathematics, focusing on boundary and finite element methods, space-time formulations, and convolution quadrature for time-domain problems. Key areas of interest include wave scattering (acoustic and electromagnetic), Schrödinger equations, fractional differential operators, and high-order numerical schemes. Recent publications highlight his work on Runge-Kutta convolution quadrature, fractional diffusion, nonlinear impedance boundary conditions, and efficient solvers for wave equations. He has collaborated with researchers at institutions such as Max Planck Institute, University of Zurich, and University of Dusseldorf. Students supervised include Brian Hennessy (joint with Emmanuil Georgoulis), Ebraheem Aldahham, Katherine Baker, Jeta Molla (joint with Gabriel Lord), and Oluwaseun Lijoka, among others.

Hari Sundarمشاهده پروفایل

دانشیار

• Parallel Algorithms
• High Performance Computing
• Computational Sciences
• +۳ مورد دیگر

Hari Sundar is an Associate Professor in the Department of Computer Science at Tufts University, holding the Ada Lovelace Associate Professorship. Previously, he served as an Associate Professor at the Kahlert School of Computing, University of Utah. His research focuses on developing parallel algorithms for computational sciences and high-performance computing, addressing challenges in biosciences, geophysics, computational fluid dynamics, and computational relativity. He leads efforts in adaptive mesh refinement, geometric multigrid methods, and scalable scientific computing frameworks like Dendro-GR for numerical relativity. Education: Ph.D. in Computer Science from the University of Pennsylvania (2009), and a Bachelor of Engineering from the University of Delhi (2000). Postdoctoral work at the Oden Institute, University of Texas at Austin. Research Interests: Parallel algorithms, high-performance computing architectures, computational relativity (binary black hole simulations), multiphase flow modeling, and domain-specific languages for scientific computing. His work emphasizes scalability and efficiency on modern supercomputers. Key Contributions: Development of the Dendro-GR platform for gravitational wave simulations, scalable PDE solvers, and GPU-optimized algorithms for phonon transport and genomic sequence alignment. His recent work includes advancements in gravitational waveform modeling for LISA space missions and thermodynamically consistent two-phase flow simulations. Grants & Collaborations: Active in NSF-funded projects on computational relativity, multiphase flow algorithms, and scalable PDE solvers. Collaborates across disciplines in astrophysics, materials science, and bioinformatics.

Long Chenمشاهده پروفایل

استاد

• Numerical Analysis
• Finite Element Methods
• Scientific Computing
• +۲ مورد دیگر

Long Chen is Professor in the Department of Mathematics at the University of California, Irvine. His research develops computational methods for solving partial differential equations through finite element analysis and numerical linear algebra. Research advances include hybridizable finite elements, multigrid solvers, and accelerated optimization techniques. Recent work focuses on finite element complexes, distributional formulations, and efficient implementations for high-dimensional problems. Develops open-source software packages including iFEM for adaptive finite element methods. Teaching includes numerical analysis, optimization, and computational PDE courses.

Lina von Sydowمشاهده پروفایل

استاد

• Computational Science
• Computational Finance
• Ice Sheet Modeling
• +۲ مورد دیگر

Lina von Sydow is a Professor in Computational Science at Uppsala University's Department of Information Technology. She serves as Section Dean for the Mathematical-Computer Science Section since July 2023. Her academic journey includes becoming an Associate Professor in 2000, Senior Lecturer since 1997, and leading the Department of Information Technology from 2018 to 2023. PhD in Domain Decomposition Methods (1995, Uppsala University) Postdoctoral Fellow at Oxford University (1996-1997) Her research spans computational science with dual focuses on Computational Finance and Ice Sheet Modeling . In finance, she develops numerical methods for option pricing using PDEs, radial basis functions, and stochastic volatility models. In climate science, she contributes to ice sheet dynamics through full Stokes models and adaptive time-stepping approaches, particularly in simulating grounding line migration. Recent publications (2025) address gender disparities in IT education, including comparative analysis of admission trends and intervention studies to boost female enrollment. Earlier works (2020-2015) focus on high-order finite difference methods for financial derivatives, BENCHOP benchmarking projects, and preconditioning techniques for PDEs. Scientific awards include Excellent Teacher (2013) She actively collaborates on educational reforms, co-authoring studies like Gender-aware course reform in Scientific Computing (2013). Her leadership roles include Head of Department (2018-2023) and Section Dean (2023-present), influencing academic governance and interdisciplinary research. Labs and teams: Works with Uppsala University's Computational Science group, Elmer/ICE project collaborators (e.g., Per Lötstedt, Gong Cheng), and international partners in numerical finance and climate modeling.

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.

• GPU Computing
• Parallel Computing
• Domain-Specific Languages
• +۵ مورد دیگر

Prof. Dr.-Ing. Richard Membarth is a Research Professor for System-on-a-Chip and AI at the Edge Computing at Technische Hochschule Ingolstadt (THI). He is affiliated with the Hardware-Software Co-Design group and holds a secondary position at the German Research Center for Artificial Intelligence (DFKI) Saarbrücken. Co-creator of DSL frameworks like AnyDSL and Hipacc Key contributor to MetaDL (AI metaprogramming) and PRIME (predictive rendering) His research bridges GPU computing , domain-specific languages , and compiler technology , with recent work on Vulkan SPIR-V compilation and device-driven SpMV algorithms . Notable awards include the HiPEAC Paper Award (2018) and multiple Best Paper Awards for his compiler frameworks.

• Scientific Computing
• Numerical Methods
• Computational Cardiology
• +۵ مورد دیگر

Luca Pavarino is a Professor at the Department of Mathematics, University of Pavia. His research focuses on scientific computing and numerical methods, particularly in the context of cardiac electrophysiology and multiphysics systems. He leads the Scientific Computing group, specializing in domain decomposition methods (BDDC/FETI-DP), isogeometric analysis, and parallel algorithms. His work integrates advanced numerical techniques with biomedical applications, including cardiac electromechanical coupling, drug testing on cardiac tissues, and modeling genetic cardiac disorders like LQT8 syndrome. Key contributions include scalable solvers for nonlinear systems, preconditioners for heterogeneous media, and operator learning for ionic dynamics. Research interests span computational cardiology, numerical analysis, and parallel computing, with applications to biophysics and drug discovery. His projects often involve interdisciplinary collaborations between mathematics, engineering, and medicine. Notable contributions include: Development of BDDC/FETI-DP preconditioners for cardiac models Integration of machine learning with cardiac electrophysiology High-performance computing for multiphysics systems (Biot’s consolidation, protein stability) Labs/Teams: Scientific Computing Group at the University of Pavia’s Department of Mathematics.

• Applied Mathematics
• High Performance Computing
• Machine Learning
• +۲ مورد دیگر

Prof. Panayot Vassilevski is a faculty member at the Faculty of Informatics of the Università della Svizzera italiana (USI) in Switzerland. He is engaged in collaborative research involving high-performance computing (HPC) and computational methods for partial differential equations (PDEs), with a focus on scalable discretization and solver development. His work intersects applied mathematics and computer science, particularly in the context of discrete networks and GPU-accelerated algorithms. Fields of Interest: Applied Mathematics, High Performance Computing, Machine Learning, Computational Science, Partial Differential Equations Recent work includes the GPU Accelerated Shifted Penalty Multigrid for Contact Elasticity (2024), which highlights his expertise in computational methods and HPC. This project builds on prior successful collaborations, such as the LLNL sabbatical of Prof. Rolf Krause and the internship of his student Patrick Zulian, which resulted in a publication. The collaboration also involves Dr. Zulian and the Swiss Supercomputing Center (CSCS) in Lugano. During the visit, Prof. Vassilevski and his PhD student Austen Nelson will work with Prof. Krause's team on reciprocal knowledge exchange. Advising: Austen Nelson, a PhD student under Prof. Vassilevski, is participating in the project. The visit project (Grant #229884, funded by the Swiss National Science Foundation with 10,500 CHF) emphasizes in-depth methodological collaboration and personnel exchange between USI and Portland University, as well as LLNL and CSCS. Labs and Teams: The project involves the Euler Institute at USI and the Swiss Supercomputing Center (CSCS) in Lugano. These institutions are pivotal for advancing the HPC-software project, which combines theoretical and algorithmic tasks in scalable discretization methods and PDE solvers.

Quan Zhouمشاهده پروفایل

استادیار

• Markov chain Monte Carlo sampling
• Bayesian methodology
• Variable selection
• +۷ مورد دیگر

Quan Zhou is an Assistant Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. His research focuses on developing advanced sampling methods, particularly Markov chain Monte Carlo (MCMC) algorithms, with applications in Bayesian methodology, variable selection, stochastic optimization, and statistical genetics. He holds a BS from Fudan University and a PhD from Baylor College of Medicine, followed by a postdoctoral fellowship at Rice University. He teaches courses such as Mathematical Probability, Multivariate Analysis, and Advanced Stochastic Processes. Notable contributions include work on informed MCMC samplers, Schrödinger bridge theory, and high-dimensional structure learning. He advises PhD students like Hyunwoong Chang (now at UT Dallas) and Guanxun Li (Beijing Normal University). Active in academic service, he served as President of the Southeastern Texas Chapter of the American Statistical Association (SETCASA).

Ralf Hiptmairمشاهده پروفایل

استاد

• Computational Mathematics
• Numerical Analysis
• Finite Element Methods
• +۷ مورد دیگر

Ralf Hiptmair is a Full Professor at ETH Zürich, serving as Head of the Seminar for Applied Mathematics and Deputy Head of the Department of Mathematics. He also holds the position of Director of Studies for ETH BSc and MSc in Computational Sciences and Engineering (CSE). His research spans computational mathematics, numerical analysis, finite element methods, boundary element methods, computational electromagnetism, multigrid methods, discrete differential forms, shape optimization, wave propagation, and kinetic equations. Hiptmair's work on auxiliary space methods was recognized as a breakthrough in computational science in the 2008 DOE Report on recent significant advancements in computational science. His research focuses on developing and analyzing numerical methods for partial differential equations, with particular emphasis on structure-preserving discretizations, computational electromagnetism, and boundary integral equations. His work has significant applications in engineering, physics, and computational science. Hiptmair's publications demonstrate a strong focus on advancing numerical techniques for electromagnetic problems, wave propagation, and shape optimization. His recent work shows increasing interest in computational topology, geometric numerical integration, and interdisciplinary applications of numerical methods. Featured as breakthrough in computational science in the 2008 DOE Report on recent significant advancements in computational science (for Auxiliary space methods) Hiptmair has supervised numerous doctoral, master's, and bachelor's students across mathematics, computational science and engineering, and related fields. His research group has received funding for developing advanced numerical methods with applications in electromagnetism, fluid dynamics, and computational physics. He is actively involved in teaching numerical methods courses at both undergraduate and graduate levels. Hiptmair leads research efforts in the Seminar for Applied Mathematics, collaborating with industry partners like ABB Corporate Research and Siemens on practical applications of computational methods. His work bridges theoretical numerical analysis with real-world engineering challenges.

• Numerical and Computational Mathematics
• Atmospheric Sciences
• Oceanography
• +۳ مورد دیگر

Dr Jack Betteridge is an Honorary Research Fellow in the Department of Mathematics, Faculty of Natural Sciences, at Imperial College London. His work bridges computational mathematics with environmental sciences, focusing on numerical methods for atmospheric and oceanic systems. His research interests include: Numerical and Computational Mathematics Atmospheric Sciences Oceanography Physical Geography and Environmental Geoscience Computation Theory and Mathematics Distributed Computing Analysis of his 2019-2024 publications reveals deep engagement with finite element methods, particularly through the Firedrake project for automated PDE solutions. His work emphasizes high-performance computing applications in geophysical fluid dynamics, developing novel preconditioners and solvers for atmospheric modeling while contributing to computational education for mathematicians.

Fabio Nobileمشاهده پروفایل

استاد

• Scientific Computing
• Uncertainty Quantification
• Numerical Analysis
• +۵ مورد دیگر

Fabio Nobile is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences (SB), Department of Mathematics (MATH), holding the CADMOS Chair in Scientific Computing and Uncertainty Quantification. He leads the CSQI (Chair of Scientific Computing and Uncertainty Quantification) group. His work focuses on numerical methods for partial differential equations (PDEs), uncertainty quantification, stochastic modeling, and computational fluid dynamics. He is involved in collaborative projects involving fluid-structure interaction, cardiac electro-mechanics, and energy systems. Professor Nobile has extensive teaching experience, including courses on advanced analysis, stochastic simulation, and numerical integration of stochastic differential equations. He supervises numerous PhD students and has contributed to over 200 peer-reviewed publications, covering topics such as low-rank approximation methods, multilevel Monte Carlo techniques, and optimal control under uncertainty. His research emphasizes interdisciplinary applications, including biomedical engineering (e.g., personalized cardiac simulations) and renewable energy (e.g., probabilistic load forecasting). He collaborates with industries and academic institutions globally, advancing computational methodologies for engineering and scientific challenges.