جست‌وجوی استاد و پژوهشگر

Xiaozhe Huمشاهده پروفایل

استاد

• Scientific Computing
• Numerical Analysis
• Multigrid Methods
• +۷ مورد دیگر

Xiaozhe Hu is a full-time Professor in the Department of Mathematics at Tufts University since July 2024. Previously held positions include Associate Professor (2019-2024) and Assistant Professor (2014-2019) at Tufts, and Adjunct Associate Professor (2020-2022) at the University of Bergen, Norway. Education : PhD in Computational Mathematics (Zhejiang University, 2009); BS in Information and Computer Science (Zhejiang University, 2004) His research focuses on scientific computing and numerical analysis , particularly: Development of adaptive and parallel numerical methods for PDEs and graph problems Multigrid/multilevel solvers for large-scale coupled systems Quantum algorithms and spectral graph theory Applications in poromechanics , reservoir simulation , and bioinformatics Recent publications demonstrate expertise in preconditioning techniques for Biot’s model, meshless methods for fluid-structure interaction, and data-driven discretization approaches . Awards include the Reimann-Louville Award (2016) and outstanding PhD graduate recognition (Zhejiang Province, 2009). PhD Students : Junyuan Lin (Loyola Marymount), Peter Ohm (RIKEN), Casey Cavanaugh (LSU), Kaiyi Wu, Eoghan O'Keefe Master Students : Charles Colley (Purdue PhD), Yue Shen (Florida State PhD), Samuel Rabinowitz, Phong Huang, Samuel Hocking Co-organizer of the Computational and Applied Mathematics Seminar and contributor to open-source HAZmath finite element library.

George Slotaمشاهده پروفایل

دانشیار

• Graph Mining
• Network Analysis
• Big Data Analytics
• +۵ مورد دیگر

George Slota serves as an Associate Professor in the Computer Science Department within the School of Science at Rensselaer Polytechnic Institute (RPI). Previously, he held positions as a Computer Scientist at Sandia National Labs New Mexico and as a Computer Engineer at the Naval Undersea Warfare Center. His research spans graph and network mining, big data analytics, machine learning, bioinformatics , and their intersections with parallel computing, scientific computing, and high performance computing . His work focuses on scalable algorithms for trillion-edge graphs, graph partitioning, community detection, and GPU-accelerated graph processing. His recent publications reveal strong emphasis on graph coarsening and spectral methods distributed graph algorithms for biconnectivity and coloring random graph generation and validation GPU acceleration for graph neural networks His research has been recognized with a prestigious NSF CAREER award supporting his project "Scalable Graph Processing to a Quadrillion Edges and Beyond." Award: National Science Foundation Faculty Early Career Development (CAREER) Award His work demonstrates consistent funding and collaboration with major national laboratories including Sandia National Labs and involvement in ExaGraph projects targeting exascale computing challenges. He maintains an active research group focused on extreme-scale graph analytics.

Arnold Reuskenمشاهده پروفایل

استاد

• Numerical Analysis
• Finite Element Methods
• Multigrid Methods
• +۷ مورد دیگر

Prof. Dr. Arnold Reusken is a full Professor of Numerical Mathematics at RWTH Aachen University, affiliated with the Institute for Geometry and Practical Mathematics (IGPM). He has held the Chair for Numerical Mathematics since 1997 and maintains an active research and academic profile in computational mathematics. Education: Ph.D. in Mathematics, University of Utrecht (1988) M.Sc. in Mathematics, University of Utrecht (1984) His research focuses on the development and analysis of numerical methods for partial differential equations, with particular emphasis on finite element methods, multigrid solvers, and computational techniques for two-phase incompressible flows and PDEs on surfaces. His work bridges theoretical numerical analysis and practical scientific computing applications in fluid dynamics and interfacial phenomena. He has made significant contributions to trace finite element methods, surface Stokes equations, and unfitted discretizations. The recent publication trend shows sustained activity in numerical methods for evolving surfaces, surface fluid dynamics, and preconditioning techniques. His work often involves rigorous error and stability analysis, demonstrating a strong theoretical foundation. Editorial Roles: Associate Editor, Journal of Numerical Mathematics (2015–present) Associate Editor, IMA Journal of Numerical Analysis (2020–present) Former Associate Editor, SIAM Journal on Numerical Analysis (2016–2021) Former Associate Editor, SIAM Journal on Scientific Computing (2002–2008) Former Associate Editor, Computing & Visualization in Science (2010–2021) Member of Advisory Board, Computing (1997–2009) Prof. Reusken has advised numerous students and researchers, though specific names are not listed in the provided text. He has been involved in collaborative research projects and has secured funding for work in numerical simulation and computational fluid dynamics. He co-authored the influential textbook Numerik für Ingenieure und Naturwissenschaftler , now in its third edition, and has contributed to other key publications in the field. He leads a research group at IGPM focused on numerical methods for interface and surface problems, contributing to both fundamental algorithm development and practical implementation in scientific computing. His team works on cutting-edge methods for simulating complex fluid systems with moving boundaries and topological changes.

Jaroslav Hronمشاهده پروفایل

دانشیار

• Fluid Mechanics
• Biomechanics
• Numerical Analysis
• +۳ مورد دیگر

Dr. Jaroslav Hron is an Associate Professor at the Department of Numerical Mathematics within the Faculty of Mathematics and Physics at Charles University in Prague, Czech Republic. His primary research focuses on fluid mechanics, biomechanics, and numerical analysis, with particular expertise in fluid-structure interaction problems and their applications to cardiovascular mechanics. Dr. Hron's research interests span fluid mechanics, biomechanics, numerical analysis, and computational mathematics. His work primarily investigates fluid-structure interaction problems, particularly as they apply to cardiovascular mechanics and hemodynamics. He has made significant contributions to the understanding of non-Newtonian fluids, pressure-dependent viscosities, and the development of advanced numerical methods for solving complex fluid flow problems. His research often bridges the gap between theoretical mathematics and practical applications in biomedical engineering. Analysis of his recent publications reveals a strong focus on cardiovascular applications, particularly in determining pressure data from velocity measurements and modeling blood flow in complex geometries. His work demonstrates a consistent progression from theoretical considerations to practical applications in biomechanics, with increasing emphasis on computational methods for fluid-structure interaction problems. The research shows interdisciplinary collaboration across mathematics, engineering, and medical fields. Dr. Hron has been actively involved in academic advising and research supervision, contributing to the development of computational methods in fluid mechanics. His work has established important benchmarks in fluid-structure interaction simulation, particularly in hemodynamic applications. His laboratory work primarily focuses on computational fluid dynamics and fluid-structure interaction, with applications to cardiovascular mechanics. He has developed and refined numerical methods for simulating complex fluid behaviors, particularly those relevant to biological systems.

Yunhui Heمشاهده پروفایل

استادیار

• Numerical Analysis
• Scientific Computing
• Finite Element Methods
• +۵ مورد دیگر

Yunhui He is an Assistant Professor in the Department of Mathematics at the University of Houston. His research focuses on numerical analysis and scientific computing, with expertise in finite element methods, multigrid methods, and local Fourier analysis. He has held postdoctoral positions at institutions like the University of British Columbia and the University of Waterloo. His work includes contributions to preconditioning techniques, acceleration methods, and the numerical solution of partial differential equations. Education: PhD in Mathematics (2018), Memorial University of Newfoundland MSc in Computational Mathematics (2015), Chinese Academy of Sciences BSc in Mathematics and Applied Mathematics (2012), Capital Normal University Research Interests: Dr. He’s research emphasizes numerical methods for PDEs, multigrid algorithms, and iterative solvers. He explores topics like finite element methods, preconditioning strategies, and local Fourier analysis to enhance computational efficiency in fluid dynamics and optimal control problems. His work bridges theoretical analysis and practical implementation, with applications in engineering and physics. Articles Trends: Recent publications highlight advancements in multigrid relaxation schemes, Anderson acceleration for nonlinear PDEs, and preconditioners for coupled flow systems. His work often integrates local Fourier analysis to optimize solver performance, with applications to Stokes-Darcy equations and optimal control problems. Grants & Awards: He co-organized the 2025 NSF-funded CBMS Conference on Applied Mathematics and Machine Learning (DMS-2430460), demonstrating leadership in academic collaboration. Teaching: Taught courses including Linear Algebra, Partial Differential Equations, and Numerical Analysis at the University of Houston and other institutions. His pedagogical focus aligns with computational mathematics and scientific computing. Lab/Team: Hosts Santolo Leveque as a postdoctoral fellow (2024–present), advancing collaborative research in numerical methods and multigrid theory.

Tony F. Chanمشاهده پروفایل

استاد

• Mathematical Image Processing
• Computer Vision
• Computational Brain Mapping
• +۱۴ مورد دیگر

Tony F. Chan is currently President and Professor of Mathematics and Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST). He holds the title of Professor Emeritus in the Department of Mathematics at the University of California, Los Angeles (UCLA), where he previously served as Professor with joint appointments in Computer Science and Bioengineering. He was Dean of the Division of Physical Sciences at UCLA (2001–2006) and Assistant Director at the National Science Foundation (NSF) for Mathematics and Physical Sciences (2006–2009). President, HKUST Professor, Mathematics & Computer Science and Engineering, HKUST Professor Emeritus, Mathematics, UCLA Assistant Director, NSF (2006–2009) Dean, Division of Physical Sciences, UCLA (2001–2006) His research interests are centered around mathematical image processing, computer vision, computational brain mapping, and numerical algorithms. He has made seminal contributions to variational methods, total variation regularization, level set methods, and multiscale computational techniques. His work bridges pure mathematics with applications in biomedical imaging, VLSI design, and scientific computing. His recent publications focus on image segmentation, inpainting, brain surface mapping, and nonlocal filtering. These works demonstrate a strong trend toward geometric and variational models for image analysis, with increasing emphasis on medical and biological applications such as neuron tracking and cortical mapping. One of the most cited mathematicians (ISI Highly Cited) Chan has mentored over 25 PhD students and 15 postdoctoral fellows, contributing significantly to the training of next-generation researchers in applied mathematics and computational science. He has led major research initiatives including the Institute for Pure & Applied Mathematics (IPAM) and has been involved in numerous professional services at national and international levels. His work has been supported by major funding agencies including the NSF. He leads the Image Processing Group at UCLA and has been instrumental in advancing interdisciplinary research at the intersection of mathematics, engineering, and neuroscience.

• Optimization Algorithms
• Network Flows
• Large Sparse Systems
• +۵ مورد دیگر

Donald Goldfarb is the Alexander and Hermine Avanessians Professor in the Department of Industrial Engineering and Operations Research at Columbia University. His research spans optimization algorithms, network flows, and large-scale computational methods, with applications in finance, image processing, and machine learning. Research Focus: Goldfarb specializes in developing efficient algorithms for convex and nonconvex optimization problems, including linear, quadratic, semidefinite, and second-order cone programming. His work extends to robust optimization for financial modeling and innovative methods for image restoration using total variation techniques. Publication Trends: His recent research emphasizes optimization in computational imaging (e.g., image decomposition and restoration) and finance (e.g., robust portfolio management). Algorithmic contributions include interior-point methods, Bregman iterations, and large-scale convex optimization techniques.

Alexander Poppمشاهده پروفایل

استاد

• Computational Contact Dynamics
• Computational Solid and Structural Dynamics
• Fluid-Structure Interaction
• +۷ مورد دیگر

Univ.-Prof. Dr.-Ing. Alexander Popp is a Full Professor of Computer-Based Simulation at the University of the Bundeswehr Munich, Germany, where he also serves as Vice Dean of the Department of Civil Engineering and Environmental Sciences. Additionally, he is the Vice Director of the DLR Institute for the Protection of Terrestrial Infrastructures in Sankt Augustin, Germany. His academic career spans prestigious institutions including TU Munich, Columbia University, and The University of Tokyo, demonstrating his international recognition in computational mechanics. Professor Popp's research spans the entire field of computational mechanics and numerical analysis, with a particular focus on computational contact dynamics, solid and structural dynamics, and multiphysics problems. His work encompasses fluid-structure interaction, non-conforming discretization methods, tribology, and computational plasticity. More recently, he has expanded his research into digital twins for critical infrastructure protection and biomedical applications, particularly in cardiovascular mechanics and stent graft modeling for endovascular repair. His group develops advanced finite element formulations for beams and shells, isogeometric analysis, and integrates machine learning techniques with traditional computational methods. Professor Popp has received numerous prestigious awards including the O.C. Zienkiewicz Award for Young Scientists from ECCOMAS (2018), the Top Teaching Trophy of the Munich School of Engineering (2018), and the ZD.B Junior Research Group Award (2017). His research is supported by significant funding from the Deutsche Forschungsgemeinschaft (DFG), the German Federal Ministry of Education and Research, and industry partners. He leads several major research projects including "AutoStent - An autonomous design assistant for aneurysm repair" and "Combination of data- and physics-based methods for hybrid digital twins" through the dtec.bw research center. As a leader in the computational mechanics community, Professor Popp serves on multiple editorial boards including Scientific Reports (Springer Nature) and Advanced Modeling and Simulation in Engineering Sciences. He is an Associate Editor for the Journal of Theoretical, Computational and Applied Mechanics and will serve as Co-Chairman for the 17th World Congress on Computational Mechanics (WCCM) and ECCOMAS Congress in 2026. His laboratory, the Institute for Mechanics of Components and Systems (IMCS), focuses on bridging fundamental research with real-world applications in civil engineering, infrastructure protection, and biomedical engineering.

Jed Brownمشاهده پروفایل

دانشیار

• High-Performance Computing
• Scientific Computing
• Fast Algorithms
• +۳ مورد دیگر

Jed Brown is an Associate Professor and Associate Chair of Inclusion in the Department of Computer Science at the University of Colorado Boulder. He holds a Dr.Sc. in computational methods from ETH Zürich and has a background in developing high-performance scientific computing tools like the PETSc library. His research focuses on fast algorithms, parallel computing, and robust community software for modeling physical systems. Education: Dr.Sc. in Computational Methods, ETH Zürich Affiliations: Center for Efficient Exascale Discretization (CEED), Parallel Ice Sheet Model (PISM) project Research interests include scalable numerical methods, GPU acceleration for exascale systems, and software sustainability. Key achievements include the 2015 SIAM/ACM Prize for contributions to computational science and the 2010 Piolet d'Or for mountaineering. He teaches courses on numerical methods and high-performance computing and leads the PhyPID research group. Awards: 2015 SIAM/ACM Prize, 2014 IEEE TCSC Young Achiever Award, 2010 Piolet d'Or Advocacy efforts include promoting academic freedom and equity in STEM through initiatives like the University of Colorado’s Inclusion Committee. Active in open-source software development and community-building in computational science.

Michael Innerbergerمشاهده پروفایل

پژوهشگر

• Numerical Analysis
• Partial Differential Equations
• Finite Element Methods
• +۳ مورد دیگر

Michael Innerberger is a researcher at TU Wien's Faculty of Mathematics and Geoinformation , affiliated with the Research Group Numerics of PDEs . He holds a Dr.techn. (PhD) and Dipl.-Ing. in engineering. His work focuses on adaptive finite element methods (FEM) , numerical analysis , and multigrid solvers for partial differential equations (PDEs), with an emphasis on achieving optimal computational complexity and robust error estimation. Recent contributions include the development of hp-robust multigrid solvers and the MooAFEM MATLAB framework for higher-order adaptive FEM. His research addresses both symmetric and nonsymmetric PDEs, including semilinear and nonlinear cases. Education: Dipl.-Ing. (TU Wien), Dr.techn. (2022, TU Wien) Key Areas: Adaptive mesh refinement, goal-oriented error control, iterative linearization, and numerical software development. Publications highlight advancements in rate-optimal and cost-optimal adaptive algorithms for elliptic PDEs, demonstrating rigorous complexity analysis and practical implementation strategies. Collaborations include Dirk Praetorius, Roland Becker, and Jens Markus Melenk. Labs/Teams: Core member of the Numerics of PDEs research group, contributing to theoretical and computational advancements in scientific computing.

• Numerical Analysis
• Domain Decomposition
• PDE-Constrained Optimization
• +۷ مورد دیگر

Tommaso Vanzan is a Researcher at the Department of Mathematical Sciences "GL Lagrange" (DISMA) at Politecnico di Torino . He holds a PhD from the University of Geneva (2020) under Prof. Martin J. Gander and was a postdoctoral researcher at EPFL (2020-2023) under the CSQI Chair. His research focuses on iterative methods , optimization algorithms , and uncertainty quantification for PDE-constrained problems, with applications in Stokes-Darcy coupling and manifold-based machine learning . His recent work includes multilevel quadrature for risk-averse optimization and spectral coarse spaces for substructured Schwarz methods. He has received the SWICCOMAS Prize 2021 for computational methods. He contributes to the open-source GDGMatlab library for finite element and discontinuous Galerkin methods, emphasizing educational and research usability over high-performance computing. Future developments target generalized boundary conditions in DG formulations.

Bedřich Sousedíkمشاهده پروفایل

دانشیار

• Applied and Computational Mathematics
• Numerical Analysis
• Scientific Computing
• +۵ مورد دیگر

Bedřich Sousedík is an Associate Professor in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC). He joined UMBC in 2014, transitioning from roles as a Research Associate at the University of Southern California and University of Maryland, College Park. His academic journey includes a Ph.D. in Applied Mathematics (2010, University of Colorado Denver) and a Ph.D. in Civil Engineering (2008, Czech Technical University in Prague). Education: Ph.D. in Applied Mathematics, University of Colorado Denver (2010) Ph.D. in Civil Engineering, Czech Technical University (2008) M.Eng. in Mechanical Engineering, Czech Technical University (2001) Research Interests: Focuses on applied and computational mathematics, including numerical analysis, scientific computing, uncertainty quantification, stochastic finite element methods, and domain decomposition techniques. His work emphasizes scalable algorithms for complex systems with parametric uncertainty. Professional Contributions: Author of ~43 refereed publications (24 journals, 12 conferences) with an h-index of 15 (Google Scholar) Principal Investigator on NSF grants totaling $219,999 for multilevel methods research Recipient of prestigious awards including the 2010 Prof. Babuška Prize and 2020 Early Career Excellence Award Advising & Teaching: Supervised 8 Ph.D. and Master’s students since 2014 Mentored undergraduate research projects in computational fluid dynamics and epidemiological modeling

Miriam Schulteمشاهده پروفایل

استاد

• Computational Fluid Dynamics
• High-Performance Computing
• Scientific Computing
• +۳ مورد دیگر

Miriam Schulte is a Professor at the University of Stuttgart’s Institute for Parallel and Distributed Systems, leading the Institute for the Simulation of Large Systems. She holds a Carl von Linde Junior Fellowship and has held academic roles since 2002, including heading the CFD Group at TUM. Her expertise spans computational fluid dynamics (CFD), high-performance computing (HPC), and numerical methods for PDE solvers. She earned her diploma (1997) and PhD (2001) in mathematics from TUM, followed by habilitation in Computer Science (2010). Her research focuses on optimizing algorithms for efficient simulation software, integrating mathematics and computer science. Key areas include fluid-structure interactions, multi-physics coupling, and scalable parallel computing. She has contributed to frameworks like Peano for adaptive Cartesian grids and developed methodologies for partitioned fluid-structure interaction simulations. Publications highlight advancements in HPC, multi-physics coupling, and parallel algorithms. Awards include the Bayerische Begabtenfoerderung (1993–1997). Her work bridges computational methods with real-world applications, emphasizing scalability and efficiency in large-scale simulations.

• Numerical Analysis
• Partial Differential Equations
• Computational Mathematics
• +۳ مورد دیگر

Prof. Dr. Derviş Subası is a faculty member in the Department of Mathematics at Eastern Mediterranean University's Faculty of Arts and Sciences. His research focuses on numerical analysis, partial differential equations, and computational mathematics, with significant contributions to inverse problems and domain decomposition methods. He has supervised multiple master's and PhD students and is actively involved in academic research. Education: PhD and MS in Mathematics from Eastern Mediterranean University (1997, 1992), BS in Electrical and Electronics Engineering (1989). His research interests include numerical solutions of partial differential equations, preconditioning techniques, and iterative methods for linear systems. He has contributed to advancements in fractional calculus-based algorithms, block-grid methods for singular boundary conditions, and parallel computing for parabolic problems. Prof. Subası has co-supervised ongoing PhD research and mentored numerous master's students, with theses focusing on parabolic inverse problems, diffusion coefficient determination, and finite difference schemes. His work often intersects applied mathematics and computational modeling. Contact details: Office AS140, Phone +90 392 630 1138, Email dervis.subasi@emu.edu.tr .

Luigi Martinelliمشاهده پروفایل

استاد

• Fluid Mechanics
• Computational Fluid Dynamics
• Aerodynamics
• +۳ مورد دیگر

Luigi Martinelli is a Professor at Princeton University's School of Engineering and Applied Science , specifically affiliated with the Department of Mechanical and Aerospace Engineering . He holds a Ph.D. from Princeton (1987) and has been a faculty member since 1994, teaching courses in Aerodynamics, Applied and Computational Mathematics, and Design. Ph.D., Princeton University, 1987 Undergraduate, Politecnico di Milano, Aeronautical Engineering His research focuses on Fluid Mechanics , Computational Fluid Dynamics (CFD) , and Shape Optimization , with applications to aerospace, biomedical devices, and renewable energy systems. He has pioneered multigrid methods for viscous conservation laws and shape optimization techniques using Reynolds Averaged Navier-Stokes equations. Recent publications span 2024–2025, combining CFD advancements (e.g., high-order methods for Euler equations, molecular gas dynamics) and biomedical applications (e.g., biliary stent removal, asthma anthropometry). Collaborations include medical research cohorts (SANI registry, TreC study) and aerospace/energy projects. Scientific Awards: 2003 Graduate Mentoring Award, McGraw Center, Princeton Martinelli collaborates internationally, with affiliations at institutions in Italy (Ist. di Gasdinamica, Dip. di Energetica). His work bridges engineering design and applied clinical research , emphasizing fluid dynamics in both domains. He actively develops next-generation CFD solvers and has contributed to industry standards for transonic aircraft design.