جستجوی استاد راهنمای دانشگاهی

Jim E. Morelمشاهده پروفایل

استاد

• Deterministic Particle Transport
• Hybrid Deterministic/Monte Carlo Methods
• Radiation-Hydrodynamics and Multiphysics Methods
• +۲ مورد دیگر

Jim E. Morel is a Professor and the Nancy & Ron Stinson Chair in Nuclear Engineering at Texas A&M University. He serves as Director of the Center for Large-Scale Scientific Simulations (CLASS) and holds a joint faculty appointment at Los Alamos National Laboratory. Educational Background: Ph.D., Nuclear Engineering, University of New Mexico (1979) M.S., Nuclear Engineering, Louisiana State University (1974) B.S., Mathematics, Louisiana State University (1972) His research focuses on Discretization and Multilevel Solution Techniques for Deterministic Particle Transport , Hybrid Deterministic/Monte Carlo Methods , and Radiation-Hydrodynamics . He has developed advanced methods for neutron transport, radiation diffusion, and multiphysics simulations. Recent publications explore topics in deterministic transport discretization, Monte Carlo integration, and radiation-hydrodynamics, with applications to reactor physics, radiative shocks, and inverse radiation problems. His work emphasizes numerical accuracy, parallel computing, and physics-based reduced-order modeling. Scientific Awards: Nancy & Ron Stinson Professorship in Nuclear Engineering For contact, email morel@tamu.edu or call 979-845-6072 at his office (AIEN M306).

Seshadhri Comandurمشاهده پروفایل

استاد

• Theoretical Computer Science
• Graph Algorithms
• Data Mining
• +۴ مورد دیگر

C. Seshadhri (Seshadhri Comandur) is a Professor of Computer Science at the University of California, Santa Cruz's Baskin School of Engineering. He earned his Ph.D. from Princeton University and completed postdoctoral research at IBM Almaden Labs. His primary research spans theoretical computer science, graph algorithms, data mining, and sublinear algorithms, with applications in network analysis and scalable computation. His work focuses on graph property testing, monotonicity testing, subgraph counting, and combinatorial algorithms. Research integrates randomization, high-dimensional analysis, and optimization techniques to solve problems in large-scale network datasets. Key interests include developing efficient algorithms for complex graph structures and exploring limitations of low-dimensional embeddings. Publications demonstrate consistent focus on algorithmic efficiency in graph analysis, with recent work advancing sublinear-time methods, spectral decompositions, and combinatorial dichotomies. Trends show strong emphasis on theoretical foundations of data mining and rigorous complexity analysis. Invited Lecture at ICDT 2023 Best Paper at WSDM 2020 2019 SDM/IBM Early Career Data Mining Research Award Best Paper at WWW 2017 Best Paper at ICDM 2015 Best Paper Finalist at WWW 2015 Best Student Paper at KDD 2013 Best Research Paper at SDM 2013 2013 Sandia Employee Recognition Award Advises PhD/MS students and postdocs including Sabyasachi Basu, Daniel Paul Pena, Shweta Jain, and Andrew Stolman. Leads the PTReview blog on property testing. Current group includes postdocs and graduate students working on algorithms and complexity.

Anthony P. Austinمشاهده پروفایل

استادیار

• Numerical Linear Algebra
• High-Performance Computing
• Approximation Theory
• +۳ مورد دیگر

Anthony P. Austin is an Assistant Professor in the Department of Applied Mathematics at the Naval Postgraduate School . His research focuses on numerical linear algebra, high-performance computing, approximation theory, and GPU-accelerated algorithms. He has held prior positions at Virginia Tech and Argonne National Laboratory as a J. H. Wilkinson Fellow. His work includes contributions to Chebfun, a software system for numerical computation. Education: D.Phil. in Mathematics , University of Oxford Mathematical Institute (2016) Ph.D. studies with active development in the Chebfun project Research Interests : Numerical methods for large-scale problems, high-order methods for PDEs, and computational techniques leveraging GPUs. His work emphasizes algorithmic innovation and stability in numerical simulations. Recent Contributions : Publications include advancements in spectral factorization, adversarial signal perturbations, and parallel algorithms for partial spectral computations. His seminar co-organization highlights collaboration in early-career faculty research initiatives. Professional Activities : Co-organizer of the NPS Junior Faculty Research Seminar (AY2022/Q3), focusing on fostering interdisciplinary collaboration among early-career faculty.

Piotr Luszczekمشاهده پروفایل

استاد پژوهش

• High-Performance Computing
• Numerical Linear Algebra
• Benchmarking
• +۳ مورد دیگر

Piotr Luszczek is a Research Professor and Adjunct Associate Professor at the University of Tennessee, Knoxville's Tickle College of Engineering, affiliated with the Department of Computer Science and the Innovative Computing Laboratory. He holds a Ph.D. and M.S. from the University of Tennessee, Knoxville, and a B.S. from AGH University of Science and Technology in Kraków, Poland. Affiliations : Innovative Computing Laboratory (ICL), Tickle College of Engineering. Roles : Research and teaching in high-performance computing, numerical linear algebra, and performance optimization. Research Interests focus on benchmarking, numerical linear algebra for HPC, automated performance tuning for modern hardware, and stochastic models for performance analysis. His work emphasizes scalable algorithms, GPU acceleration, and efficient use of hybrid architectures. Grants and Collaborations include projects on batched linear algebra, sparse matrix operations, and energy-efficient AI frameworks. He contributes to software libraries like PLASMA and MAGMA, optimizing for exascale computing. Labs/Teams : Active in the Innovative Computing Laboratory (ICL), developing tools for HPC benchmarking (e.g., HPCG) and parallel linear algebra libraries. Engaged in international collaborations for exascale computing initiatives.

Melina Freitagمشاهده پروفایل

استاد

• Numerical Linear Algebra
• Data Assimilation
• Inverse Problems
• +۶ مورد دیگر

Melina Freitag is Professor for Data Assimilation at the Institute for Mathematics, University of Potsdam. Her research spans numerical linear algebra, inverse problems, and model order reduction, with applications in geophysics, image processing, and machine learning. She leads the Data Assimilation Group and contributes to the SFB 1294 Collaborative Research Center. Education : Prof. Freitag earned her Diplom in Mathematics at TU Chemnitz (2004) and PhD in Mathematical Sciences from University of Bath (2007). Research Themes : Her work focuses on Krylov subspace methods, low-rank approximations, and preconditioning for large-scale systems. She bridges classical numerical analysis with modern data assimilation, addressing challenges in: Bayesian inverse problems with unstable systems Optimized spectral sampling in X-ray imaging Physics-informed neural networks for Navier-Stokes inversion Parameter-dependent eigenvalue analysis Collaborations & Grants : She collaborates with institutions like KTH Stockholm and Arizona State University. Her group secures funding through SFB 1294 and participates in INI Cambridge networks. Leadership : Co-Chair of GAMM Activity Group on Applied Numerical Linear Algebra SIAM Activity Group on Linear Algebra Chair (2022) Teaching : Delivers courses on numerical optimization, matrix methods in data science, and inverse problems, integrating computational theory with real-world applications.

• Numerical Analysis
• Scientific Computing
• Matrix Theory
• +۵ مورد دیگر

Nicola Guglielmi is a Professor of Numerical Analysis at the Gran Sasso Science Institute (GSSI) in L'Aquila, Italy, since February 2018. He currently serves as Deputy Rector of GSSI (2022–2026) and Director of the Division of Mathematics (2019–2025). Previously, he held positions at the University of L'Aquila, including Full Professor of Numerical Analysis (2006–2018). His education includes a Laurea from the University of Bologna (1991, summa cum laude) and a PhD in Computational Mathematics from the University of Padova (1996). His research focuses on numerical analysis, particularly delay differential equations, matrix theory applications, and stability analysis of dynamical systems. He has authored over 100 publications and developed the RADAR5 software for delay differential equations. Recent grants include the 2023 MIUR/PNRR project on geothermal energy modeling and the 2022 PRIN project on numerical methods for PDEs. Notable awards include the New Talent Award at SciCADE-99 (1999) and a plenary lecture at the UMI-23 conference (2023). He has supervised numerous PhD students, many now in academia and industry. Upcoming activities include visits to the Courant Institute (2025) and McGill University (2025). His work bridges theoretical and applied mathematics, with contributions to scientific computing, control theory, and data science.

Hassane Sadokمشاهده پروفایل

استاد

• Numerical Analysis
• Iterative Methods
• Linear Algebra
• +۵ مورد دیگر

Hassane Sadok is a Professor and President at the University of the Littoral Côte d'Opale (ULCO), France. He is affiliated with the Department of Mathematics within the College of Science, where he contributes to teaching and research in numerical analysis and scientific computing. Position: Professor Institution: University of the Littoral Côte d'Opale (ULCO) Administrative Role: President since 2016 Department: Mathematics School: College of Science His research focuses on numerical linear algebra, particularly iterative methods for solving large linear systems, Krylov subspace methods (such as GMRES, Arnoldi, and CMRH), and regularization techniques for ill-posed problems. He has made significant contributions to the convergence analysis of minimal residual and orthogonal projection methods. His work bridges theoretical numerical analysis with practical computational algorithms. The recent publications reflect a strong trend in developing and analyzing block and global iterative solvers for multiple linear systems, convergence theory for eigenvalue problems, and regularization strategies like the L-curve and greedy Tikhonov methods. These works are grounded in applied mathematics and computational science, with applications in engineering and data analysis. Although no formal awards are listed in the provided texts, his leadership as university president and sustained research output indicate recognition in both academic and administrative domains. He advises graduate students in numerical analysis and computational mathematics, though specific names are not listed. He has not disclosed information on research grants. His teaching includes Master’s level courses in interpolation, quadrature, and numerical resolution of nonlinear equations. While no specific research lab or team is named, his collaborations with researchers like K. Jbilou, Y. Saad, and L. Reichel suggest active participation in a research group focused on numerical methods, likely within the Laboratoire de Mathématiques Pures et Appliquées (L.M.P.A.) at ULCO.

Haim Avronمشاهده پروفایل

استاد

• Numerical Computing
• High Performance Computing
• Machine Learning
• +۳ مورد دیگر

Haim Avron is a Professor in the Department of Applied Mathematics at Tel Aviv University's School of Mathematical Sciences, where he has been employed since 2015. His research focuses on numerical computing, high-performance computing, and their applications in scientific computing and machine learning. Education: Completed PhD in Computer Science at Tel Aviv University under Prof. Sivan Toledo, followed by postdoctoral research at IBM T.J. Watson Research Center. Research interests: Foundations of numerical linear algebra and randomized algorithms Tensor-tensor algebra for multiway data representation High-performance computational methods for machine learning Optimization techniques for large-scale systems Recent publications demonstrate strong focus on tensor algebra, randomized numerical methods, and machine learning optimization, with applications ranging from quantum computing to deep learning architectures. Awards: SIAM Activity Group on Computational Science and Engineering Best Paper Prize 2025 Software contributions include development of numerical libraries such as libSkylark for matrix sketching and Blendenpik for least-squares problems.

Patrick Kürschnerمشاهده پروفایل

دانشیار

• Numerical Linear Algebra
• Model Order Reduction
• Eigenvalue Problems
• +۸ مورد دیگر

Patrick Kürschner is an Associate Professor for Numerical Mathematics and Linear Algebra at Leipzig University of Applied Sciences (HTWK Leipzig) . He previously held postdoctoral positions at the Max Planck Institute for Dynamics of Complex Technical Systems and KU Leuven's Kulak Kortrijk Campus. His work focuses on computational methods in systems and control theory. PhD in Mathematics (Otto-von-Guericke University, 2015) MSc in Mathematics (Chemnitz University of Technology, 2010) BSc in Financial Mathematics (Chemnitz University of Technology, 2008) His research interests span numerical linear algebra, matrix equations and functions, eigenvalue problems, preconditioning, model order reduction, tensor methods, and numerical algorithms for digital signal processing. He develops efficient computational techniques for large-scale systems in control theory and data science applications. Recent publications include advancements in low-rank ADI iteration, time-limited balanced truncation, inexact linear solvers, and tensor decompositions for polynomial systems. His work addresses challenges in computational neuroscience, mechanical simulations, and optimal control.

Hongkai Zhaoمشاهده پروفایل

استاد

• Computational and Applied Mathematics
• Numerical Analysis
• Scientific Computing
• +۴ مورد دیگر

Hongkai Zhao is the Ruth F. DeVarney Distinguished Professor of Mathematics and Chair of the Department of Mathematics at Duke University's Trinity College of Arts & Sciences. He holds a Ph.D. in Mathematics from UCLA (1996) and has held prior positions including Chancellor's Professor at UC Irvine and Gábor Szegő Assistant Professor at Stanford University. His research focuses on computational and applied mathematics with applications in inverse problems, imaging, PDEs, and scientific computing. Key research areas include numerical methods for wave propagation, radiative transfer, and shape analysis. Zhao has led NSF-funded projects on PDE learning, radiative transfer, and analysis training. He has received prestigious awards including the Feng Kang Prize (2007) and SIAM Fellowship (2022). Teaching includes advanced courses on stochastic processes, numerical linear algebra, and mathematical analysis. His work bridges theory and practice, contributing to medical imaging algorithms, computational physics, and geometric modeling.

Ron Morganمشاهده پروفایل

استاد

• Numerical Analysis
• Computational Mathematics
• Linear Algebra
• +۲ مورد دیگر

Ron Morgan is a Professor of Mathematics at Baylor University and serves as an Undergraduate Advisor. His primary affiliation is with the Department of Mathematics, where he focuses on numerical analysis and computational methods. Research Interests Numerical analysis of large linear systems and eigenvalue problems Development of efficient algorithms for Monster Matrices Applications in computational mathematics and sparse matrix techniques Specialized in Krylov subspace methods like GMRES and Arnoldi Scientific Contributions are centered on iterative solvers, deflation techniques, and multigrid methods for eigenvalue problems. His work spans publications in prestigious journals such as SIAM and Mathematics of Computation. Former Ph.D. Students Zhao Yang (2015) Huy Nguyen (2015) Quan Liu (2014, Co-Advised) Dywayne Nicely (2008) Dean Darnell (2006, Co-Advised) Min Zeng (1996) Contact: Ronald_Morgan@baylor.edu | (254) 710-6575

Zhongwei Shenمشاهده پروفایل

استاد

• Analysis
• Partial Differential Equations
• Homogenization Theory
• +۳ مورد دیگر

Zhongwei Shen is a Professor of Mathematics at the University of Kentucky, Department of Mathematics, within the College of Arts & Sciences. He joined UK in 1995 and was promoted to full professor in 2003, serving as Department Chair from 2007 to 2011. His research focuses on analysis and partial differential equations, with expertise in homogenization theory, elliptic systems, and spectral analysis. Education: Ph.D., Mathematics, University of Chicago, 1989 M.S., Mathematics, Chinese Academy of Sciences, 1985 B.S., Mathematics, Peking University, 1982 His research interests span mathematical analysis of PDEs, including boundary value problems in nonsmooth domains, homogenization in perforated media, and spectral properties of magnetic operators. Recent work emphasizes fluid dynamics, stochastic dynamical systems, and data-driven modeling frameworks for complex systems. Publications reflect deep engagement with elliptic systems, Stokes operators, and asymptotic analysis in diverse domains. Awards and grants details are pending explicit mentions in source texts.

Bruno Carpentieriمشاهده پروفایل

دانشیار

• Numerical Linear Algebra
• High-Performance Computing
• Computational Electromagnetics
• +۵ مورد دیگر

Bruno Carpentieri is an Associate Professor in Applied Mathematics at the Faculty of Computer Science , Free University of Bozen-Bolzano , Italy. His work focuses on Numerical Linear Algebra , High-Performance Computing , and Preconditioning Techniques for large-scale scientific problems. Education : Laurea in Applied Mathematics (1997, University of Bari Aldo Moro); PhD in Computer Science (National Polytechnic Institute Toulouse). Research Interests : Sparse linear systems, Krylov subspace methods, computational electromagnetics, PageRank problems, and fractional calculus applications in nonlinear engineering. His publications (1998–2025) emphasize efficient solvers for multi-shifted systems, parallel computing, and preconditioning strategies. Recent works (2025) explore fractional-order methods and multilayer network centrality . He has collaborated on ITER tokamak simulations (F4E/MIUR grants) and biomedical finite element models . Grants : F4E-2008-OPE-06-06-11 (ITER analysis); MIUR PRIN 2010SPS9B3. Key Contributions : VBARMS preconditioner (2014), hybrid block GMRES variants (2018), and chaos-enhanced fractional solvers (2025).

Alexander Tichaiمشاهده پروفایل

استاد پژوهش

• Nuclear Physics
• Many-Body Theory
• Theoretical Physics
• +۳ مورد دیگر

Alexander Tichai is an ERC Group Leader at the Technical University of Darmstadt's Institute of Nuclear Physics, leading the DeformedNuclei research group. His academic journey includes a PhD in Physics (2017) and dual bachelor's and master's degrees in Physics and Mathematics from TU Darmstadt. Prior to his current role, he held postdoctoral positions at CEA Saclay/Université Paris-Saclay (2018-2019) and TU Darmstadt (2019-2025). His research focuses on nuclear many-body theory , including open-shell nuclei, symmetry-broken correlation expansions, and tensor factorization techniques. He specializes in ab initio methods for nuclear structure and dynamics, with applications to neutron stars, neutrinos, and dark matter. Key techniques include coupled-cluster theory, Bogoliubov perturbation methods, and low-rank matrix decompositions. His work bridges theoretical physics and computational science, addressing challenges in heavy-nuclei simulations and precision nuclear modeling. Collaborations include the EUSTRONG and ChiralEFT4DM projects, supported by ERC funding. Tichai’s research also explores interdisciplinary applications like eigenvector continuation and quantum information entropy. He advises the DeformedNuclei research group at TU Darmstadt and contributes to the STRONGINT initiative. His publications emphasize computational advancements for nuclear many-body problems, including data compression and numerical stability in large-scale simulations.

King-Wah Chuمشاهده پروفایل

پژوهشگر ارشد

• Numerical Analysis
• Numerical Linear Algebra
• Control Theory
• +۳ مورد دیگر

King-Wah Chu is an Adjunct Senior Research Fellow at the School of Mathematics, Monash University. He has been working in Melbourne, Australia for 30 years. His research focuses on numerical analysis, numerical linear algebra, eigenvalue problems, control theory, and matrix equations. He serves as an Associate Editor for the SIAM Journal on Matrix Analysis and Applications since 2015. Education: Hong Kong and England Affiliations: Monash University, SIAM Journal His research interests include perturbation and numerical solutions of eigenvalue problems, inverse eigenvalue problems, Riccati equations, and large-scale structured computations. He has contributed to the development of algorithms for control systems and tensor computations. Key Projects: Research visit to the International Symposium on Mathematical Theory of Networks & Systems (2000) Advising & Grants: Chu has advised on multiple projects but no specific students are listed. His work involves collaborations on large-scale algebraic Riccati equations and numerical methods. Labs/Teams: Active in the School of Mathematics at Monash University, focusing on numerical linear algebra and control systems.