جستجوی استاد راهنمای دانشگاهی

Xu Jinchaoمشاهده پروفایل

استاد

• Multigrid methods
• Machine learning
• Finite element methods
• +۵ مورد دیگر

Xu Jinchao is a Professor of Applied Mathematics and Computational Sciences at King Abdullah University of Science and Technology (KAUST) and the Verne M. Willaman Professor of Mathematics at Penn State University. He has held distinguished roles, including Director of the Center for Computational Mathematics and Applications at Penn State since 1997 and is an Affiliated Faculty member of the College of Information Sciences and Technology at Penn State. His research focuses on numerical partial differential equations (PDEs), multigrid methods, machine learning, finite element methods, and domain decomposition methods. He is renowned for pioneering contributions such as the Bramble-Pasciak-Xu (BPX) preconditioner, Hiptmair-Xu (HX) preconditioner, Xu-Zikatanov (XZ) identity, and Morley-Wang-Xu (MWX) element. His work bridges computational mathematics and machine learning, including the development of MgNet, which unifies multigrid methods with convolutional neural networks. Xu has been recognized with numerous awards, including Fellowships from SIAM, AMS, AAAS, and the European Academy of Sciences. Notable accolades include the 2008 DOE Top 10 Breakthroughs for his HX preconditioner and the 1995 Feng Kang Prize for Scientific Computing. He has organized over 100 conferences and serves on editorial boards of top journals such as Mathematics of Computations and Numerische Mathematik . His leadership includes directing research centers and advancing computational science through collaborative efforts.

Bruce MacDowell Maggsمشاهده پروفایل

استاد

• Computer Networks
• Distributed Systems
• Algorithms
• +۷ مورد دیگر

Bruce MacDowell Maggs is a Professor in the Department of Computer Science at Duke University and serves as Vice President of Research at Akamai Technologies. His career bridges academic research and industrial innovation in computer science, particularly in distributed systems and networking. Research Interests: His work spans computer networks , distributed systems , parallel algorithms , content delivery , and fault-tolerant computing . He has made significant contributions to network routing, load balancing, scalability of web applications, and energy efficiency in large-scale systems. His research often combines theoretical rigor with practical system design. The recent publications highlight a consistent focus on scalability , network performance , and security in internet-scale applications. Key themes include query caching , traffic modeling , resilient routing , and energy optimization , reflecting his deep involvement in the infrastructure of modern web services. No scientific awards are mentioned in the provided text. Advising and Teaching: He has advised numerous Ph.D. students, many of whom are now faculty or researchers at top institutions. His former students include Ramesh Sitaraman, Anja Feldmann, and Andrea Richa. He currently advises Anat Talmy at Duke. He has taught a wide range of courses at Duke, Carnegie Mellon, and MIT, including Computer Networks, Operating Systems, Algorithms, and Discrete Mathematics. Labs and Teams: While not explicitly named, his research is closely tied to systems and networking groups at Duke and his industrial work at Akamai, a leader in content delivery networks. His collaborations with Tom Leighton and others at Akamai suggest leadership in research teams developing foundational internet technologies.

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.

Mark Ainsworthمشاهده پروفایل

استاد

• Numerical Approximation of Partial Differential Equations
• A Posteriori Error Estimation
• High Order Finite Element Methods
• +۵ مورد دیگر

Mark Ainsworth is a Francis Wayland Professor of Applied Mathematics at Brown University and holds a joint faculty appointment with Oak Ridge National Laboratory. He obtained his PhD from Durham University (1989) and has held prominent roles such as Director of the Centre for Numerical Algorithms and Intelligent Software (2011-2012). His research focuses on numerical analysis, particularly finite element methods for partial differential equations, a posteriori error estimation, and high-performance computing challenges like resiliency on exascale systems. Education: PhD in Mathematics, Durham University, 1989 BSc in Mathematics, Durham University, 1986 Research Interests: Numerical approximation of PDEs A posteriori error estimation and adaptive methods High order finite element methods Resiliency of numerical algorithms on emerging architectures Fractional PDEs and scientific data compression Awards: SIAM Fellow (2014) FIMA (2010) Whitehead Prize (2004) J.L. Lions Prize (2004) Fellow of Royal Society of Edinburgh (2003) Grants & Leadership: Co-PI for ARO MURI on fractional PDEs (2015-2020) Directed NAIS center (2011-2012), a £5M multi-institutional initiative Organized major international conferences on computational mathematics Labs/Teams: Collaborations include Oak Ridge National Lab and international research networks in numerical analysis and scientific computing.

• Numerical Linear Algebra
• Numerical Nonlinear Algebra
• Parallel Computing
• +۷ مورد دیگر

Prof. Zhong-Zhi Bai is a Professor of Computational Mathematics at the Institute of Computational Mathematics and Scientific/Engineering Computing, part of the Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing. He is based at the State Key Laboratory of Scientific/Engineering Computing, a leading institution in applied mathematics and high-performance computing in China. His research spans a broad range of topics in computational mathematics, with a strong emphasis on numerical linear and nonlinear algebra , parallel computations , numerical optimization , and sparse matrix techniques . He has made significant contributions to the development and analysis of preconditioners and numerical methods for differential-algebraic equations and partial differential equations . His work is foundational in advancing iterative solvers and large-scale scientific simulations. The available publications, including the notable book Milestones in Matrix Computation (2007), reflect a long-standing engagement with matrix computation and numerical algorithms. The research trends indicate a deep focus on theoretical and practical aspects of numerical methods, particularly in the context of high-performance and scientific computing environments. Prof. Bai serves on the editorial boards of several leading journals in the field, including: Applied Numerical Mathematics Communications on Applied Mathematics and Computation Computational and Applied Mathematics East Asian Journal on Applied Mathematics Electronic Transactions on Numerical Analysis Journal of Computational and Applied Mathematics Journal of Mathematical Study Journal of Scientific Computing Numerical Algorithms Numerical Linear Algebra with Applications He has advised students and mentored researchers, though specific names are not listed. His work is supported by institutional affiliations and likely national research grants, given his position in a State Key Laboratory. He has developed software tools and delivered lecture series, contributing to both research and education in computational mathematics. Prof. Bai is actively involved in the numerical linear algebra community and maintains a comprehensive academic presence, including publications, editorial service, and software contributions. He leads or participates in research teams focused on advancing computational methods within the State Key Laboratory framework.

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.

• Materials Science
• Computational Chemistry
• Density Functional Theory
• +۳ مورد دیگر

Michael Herbst is an Assistant Professor (tenure-track) at EPFL, holding a joint appointment in the School of Basic Sciences (SB) and the School of Engineering (STI). He leads the Mathematics for Materials Modelling (MatMat) research group, focusing on error control in atomistic simulations, density-functional theory (DFT), and interdisciplinary computational methods. His work bridges mathematics, materials science, and computer science, emphasizing robust algorithms and Julia-based software development. Herbst holds a PhD from Heidelberg University and has held postdoctoral positions at RWTH Aachen and Inria Paris. He is a core member of the MARVEL and CESMIX research centers. Education: 2018: Dr. rer. nat. (magna cum laude), Heidelberg University 2009–2013: BA and MSci (1st class) in Natural Sciences, University of Cambridge 2008–2009: Studies in Mathematics/Physics, TU Kaiserslautern Research Interests : Herbst's research centers on developing reliable computational methods for materials modeling, including error estimation in DFT, black-box SCF algorithms, and Julia-based tools like the Density-Functional Toolkit (DFTK). His work addresses challenges in high-throughput simulations, numerical stability, and interdisciplinary collaboration across mathematics, physics, and computer science. Grants & Projects : MARVEL Center for Computational Design (EPFL) CESMIX Center for Extreme-Scale Simulations (MIT) EMC² Project (Sorbonne/Inria/École des Ponts) Awards : HGS MathComp PostDoc Fellowship (2018–2021) DAAD Travel Funding (2018) Exploratory Research Space Fund (RWTH Aachen, 2022) Labs & Teams : Head of the MatMat group at EPFL, focusing on error-controlled simulations and open-source software development.

• 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.

• Scientific Workflow Management
• Distributed Computing
• Numerical Linear Algebra
• +۳ مورد دیگر

Wilfried Gansterer is a Professor at the Faculty of Computer Science, University of Vienna, leading the Theory and Applications of Algorithms research group. His work focuses on numerical algorithms, distributed computing, and machine learning, with notable contributions to graph neural networks and fault-tolerant systems. Active in projects such as Algorithmic Data Science for Computational Drug Discovery (2020–2028) and REPEAL (Resilience vs. Performance in Numerical Linear Algebra, 2016–2020). Research Interests: Dr. Gansterer’s expertise spans graph neural networks, matrix compression, adversarial defense mechanisms, and high-performance computing. His work addresses challenges in efficient computation, resilience against node failures, and optimizing distributed systems. Projects : Algorithmic Data Science for Computational Drug Discovery (2020–2028) REPEAL: Resilience vs. Performance in Numerical Linear Algebra (2016–2020) Verteiltes Rechnen (Distributed Computing, 2007–2014) Awards : 2023 Best Paper Award for work on Crossfire: An Elastic Defense Framework for Graph Neural Networks. Labs/Teams : Directs the Theory and Applications of Algorithms group, focusing on algorithmic innovation in distributed and high-performance computing environments.

Sriramkrishnan Muralikrishnanمشاهده پروفایل

پژوهشگر

• Numerical Analysis
• High Performance Scientific Computing
• Plasma Physics
• +۴ مورد دیگر

Sriramkrishnan Muralikrishnan is a Research Staff member at the Department of Mathematics and Education within the Jülich Supercomputing Center (JSC) at Forschungszentrum Jülich, Germany. His work focuses on developing advanced computational methods for high-performance scientific computing, particularly in plasma physics and related multi-physics applications. Dr. Muralikrishnan's research spans several key computational domains: Numerical Analysis and High-Order Methods High Performance Scientific Computing for Exascale Architectures Plasma Physics Simulations Fast Solvers and Preconditioners Parallel-in-Time Integration Techniques Performance Portable Programming His recent publications demonstrate a strong focus on particle-based computational methods, particularly Particle-in-Cell and Particle-in-Fourier techniques. His work consistently addresses challenges in energy conservation, scalability across architectures, and noise reduction in plasma simulations. A significant portion of his research involves developing performance-portable frameworks that can efficiently leverage modern supercomputing hardware from different vendors without code rewrites. Dr. Muralikrishnan is actively involved in open-source scientific software development: Lead developer of IPPL (a performance portable library for grids and particles) Developer of OPAL (an open-source particle accelerator library) His research has direct applications in plasma physics, fusion energy research, and advanced accelerator design, with a strong emphasis on making computational methods accessible through open-source development and advocating for diversity in scientific computing.

• 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.

Luke Olsonمشاهده پروفایل

استاد

• Numerical Analysis
• High-Performance Computing
• Scientific Computing
• +۴ مورد دیگر

Luke Olson is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), part of the College of Engineering. He holds an affiliate appointment in the Department of Mechanical Science and Engineering. His research focuses on numerical methods, high-performance computing, and parallel algorithms, particularly algebraic multigrid (AMG) solvers and sparse matrix computations. He leads the development of open-source libraries like PyAMG and RAPtor, advancing computational tools for scientific and engineering applications. Education: Ph.D. in Applied Mathematics from the University of Colorado Boulder (2003), M.S. in Mathematics from the University of Iowa (1999), and B.A. in Mathematics and Physics from Luther College (1997). Research Interests: Algebraic multigrid methods and preconditioners High-performance computing and parallel algorithms Numerical solutions to partial differential equations Scientific computing and software development Machine learning integration in numerical simulations Recent Articles Trends: Recent work merges machine learning with traditional numerical methods, such as neural network closures for turbulent combustion and reduced basis approximations using neural networks. Ongoing contributions include optimizing multigrid methods for exascale architectures and enhancing communication efficiency in distributed systems. Awards: Recognized with the NSF CAREER Award (2007), UIUC Campus Award for Excellence in Teaching (2024), and the Donald Biggar Willett Faculty Scholar distinction (2016). Active in conference organization, including the Copper Mountain Conference on Multigrid Methods. Teaching & Grants: Teaches courses like Numerical Methods for PDEs and Scientific Machine Learning. Leads projects funded by NSF and industry collaborations, emphasizing education innovation through the AE3 fellowship (2014–2016). Labs/Teams: Directs the Scientific Computing Group at UIUC and contributes to interdisciplinary initiatives like the Center for Exascale-enabled Scramjet Design (CEESD).

Santiago Badiaمشاهده پروفایل

استاد

• Computational Science and Engineering
• Numerical Analysis
• Finite Element Methods
• +۳ مورد دیگر

Santiago Badia is a Full Professor of Computational Science and Engineering at Universitat Politècnica de Catalunya (UPC), holding an adjoint researcher position at the International Center for Numerical Methods in Engineering (CIMNE). He leads the Large Scale Scientific Computing (LSSC) group at CIMNE, focusing on finite element methods, numerical analysis, and high-performance computing. His research emphasizes fluid dynamics, multiphysics problems, and scalable solvers for large-scale systems. Previously, he worked at Politecnico di Milano and Sandia National Labs. He developed the FEMPAR software framework, a parallel finite element tool for PDE simulations, achieving landmark scalability (e.g., 60 billion unknowns on 458,672 cores). FEMPAR is recognized in the High-Q Club of European codes. His expertise includes discontinuous Galerkin methods, XFEM, and domain decomposition preconditioners. Research interests span metal additive manufacturing, superconductor devices, and nuclear engineering applications. Awards include FEMPAR's High-Q Club inclusion. He advises PhD and MSc students (e.g., Jesus Bonilla, Eric Neiva, Marc Olm) and has open positions in postdoc/PhD levels. His team includes researchers like Javier Principe and Alberto Martín. Ongoing projects involve advancing parallel algorithms, multiphysics simulations, and software scalability for exascale computing.

Simon Adrianمشاهده پروفایل

استاد

• Computational Electromagnetics
• Electromagnetic Integral Equations
• Antenna Design
• +۳ مورد دیگر

Prof. Simon Adrian holds the Chair of Theoretical Electrical Engineering at the Institute of General Electrical Engineering, University of Rostock, Germany. His research focuses on computational electromagnetics with critical applications in antenna design, electromagnetic compatibility, and medical technology. He serves as Associate Editor for the IEEE Transactions on Antennas and Propagation and contributes to the IEEE Antennas and Propagation Society Education Committee, demonstrating significant academic leadership in the global electromagnetics community. His primary research addresses low-frequency instability challenges in electromagnetic integral equations through innovative numerical techniques. Key areas include Calderón preconditioners, quasi-Helmholtz projectors, B-spline discretizations, and adaptive cross approximation methods. These approaches enable robust simulations across diverse applications from radar systems and antenna design to biomedical problems like deep brain stimulation and electroencephalography. Recent work emphasizes broadband stability and efficient solvers for multiply-connected geometries. Analysis of Prof. Adrian's publication trends (2023-2025) reveals a concentrated effort on overcoming fundamental limitations in electromagnetic modeling. His work consistently targets low-frequency regimes where traditional methods fail, developing mathematically rigorous stabilization techniques while expanding into biomedical applications. The integration of isogeometric analysis with specialized discretization strategies represents a cutting-edge direction in computational electromagnetics. Professional engagement includes active membership in the Institute of Electrical and Electronics Engineers (IEEE), IEEE Antennas and Propagation Society, and Union Radio-Scientifique Internationale (URSI), reflecting his commitment to advancing the field through collaborative research and scholarly communication.

• Numerical Linear Algebra
• Matrix Theory
• Numerical Analysis
• +۶ مورد دیگر

Davide Palitta is an Assistant Professor (tenure track) at the Department of Mathematics of the University of Bologna. His research focuses on numerical linear algebra, matrix equations, and their applications in fields like data assimilation, deep learning, and parallel computing. He holds a PhD in Mathematics from the University of Bologna (2018) and has held postdoctoral positions at the Max Planck Institute in Magdeburg (2018–2021) and a visiting fellowship at Brown University (2020). His work emphasizes the development of efficient numerical algorithms for large-scale matrix equations, including Sylvester, Lyapunov, and Riccati equations. Key contributions include Krylov subspace methods, randomized techniques, and time-parallel integration strategies. His recent publications span topics such as sketched Krylov methods, tensor-based solvers, and preconditioning strategies for weak-constraint data assimilation. Palitta collaborates actively with international institutions and organizes seminars like the Scube series on numerical linear algebra. He is involved in upcoming conferences on matrix equations, data assimilation, and parallel computing. No scientific awards are explicitly mentioned in the provided materials.