جست‌وجوی استاد و پژوهشگر

Alex Pothenمشاهده پروفایل

استاد

• Combinatorial Scientific Computing
• Parallel Algorithms
• Graph Algorithms
• +۳ مورد دیگر

Alex Pothen is a Professor of Computer Science at Purdue University, affiliated with the Department of Computer Science within the College of Science. He joined Purdue in Fall 2008 and is a distinguished academic with fellowships from SIAM (2018), ACM (2022), and AMS (2024), as well as the George Pólya Prize in Applied Combinatorics (2021). His research focuses on combinatorial scientific computing (CSC), parallel algorithms, graph theory, and bioinformatics, with applications in computational surgery, power grid modeling, and quantum computing. Education : Ph.D., Applied Mathematics and Computer Science, Cornell University (1984) Master's Degree in Chemistry, Indian Institute of Technology, New Delhi (1978) Research Interests : Alex's work bridges discrete mathematics and computational science. Key areas include: - CSC : Applying combinatorial methods to solve engineering and scientific problems. - Graph Algorithms : Developing efficient parallel algorithms for matching, coloring, and partitioning. - Bioinformatics : Analyzing flow cytometry data and immunophenotyping. - Algorithmic Differentiation : Innovations in Jacobian/Hessian computation. - Quantum Computing : Exploring quantum annealing for optimization problems. Awards and Recognition : SIAM Fellow (2018) ACM Fellow (2022) AMS Fellow (2024) George Pólya Prize in Applied Combinatorics (2021) Graduate Mentoring Award (Purdue University) Named 'Most Inspiring Teacher' by students Grants and Leadership : - Directed the CSCAPES Institute (2006–2012), a DOE-funded initiative enabling petascale simulations. - Co-PI on the ExaGraph Project , funded by the DOE's Exascale Computing Program. - Editor of journals including the SIAM Journal on Scientific Computing and the Journal of the ACM.

James Brannickمشاهده پروفایل

استاد

• Lattice Field Theory
• Numerical Solutions to PDEs
• Multigrid Methods
• +۳ مورد دیگر

James Brannick is a Professor in the Department of Mathematics at Pennsylvania State University, affiliated with the Eberly College of Science. His research focuses on numerical solutions to partial differential equations (PDEs), multigrid methods, and lattice field theory, with applications in computational physics and quantum chromodynamics (QCD). He holds a Ph.D. from the University of Colorado Boulder (2005). Research interests include developing advanced multigrid algorithms for complex systems, such as coupled PDEs and spacetime discretizations, with a particular emphasis on algebraic multigrid (AMG) methods. His work bridges theoretical foundations (e.g., convergence analysis) and practical implementations (e.g., parallel solvers for large-scale problems). Recent publications emphasize AMG optimization, bootstrap techniques, and applications to fluid dynamics, quantum field theory, and materials science. Collaborative projects include NSF-funded initiatives for parallel space-time solvers and multigrid methods in lattice QCD. Brannick’s contributions address challenges like critical slowing down in lattice simulations and efficient handling of anisotropic or indefinite problems. Grants include subcontract B629176 (2019) and prior NSF awards for multigrid solvers in coupled PDE systems. His research often intersects with high-performance computing and interdisciplinary applications, such as neutron star dynamics and phase-field modeling.

• Numerical Analysis
• Environmental Engineering
• Groundwater Management
• +۳ مورد دیگر

Papadopoulou Elena is a Professor at the Technical University of Crete (TUC), affiliated with the Department of Mineral Resources Engineering (MRE) within the School of Mineral Resources Engineering. She holds an office in the Science/ECE Building (Λ), 2nd Floor, room 145Β-100. Her research focuses on numerical methods for partial differential equations, groundwater management, and parallel computing, with applications in environmental and biomedical engineering. She can be reached at epapadopoulou@tuc.gr . Her work emphasizes stochastic optimization strategies for coastal aquifer management, numerical solutions of reaction-diffusion systems using Hermite collocation and Runge-Kutta schemes, and high-performance computing techniques on GPUs. Notable contributions include studies on saltwater intrusion modeling and brain tumor invasion dynamics. She has also explored the Fokas method for multi-domain PDEs and parallel iterative algorithms for elliptic equations. Dr. Papadopoulou’s research demonstrates interdisciplinary collaboration between computational mathematics and environmental engineering, addressing real-world problems such as sustainable groundwater management and biomedical modeling. Her publications span over three decades, reflecting a sustained commitment to advancing numerical analysis and its applications.

Timo Heisterمشاهده پروفایل

استاد

• Computational Mathematics
• Finite Element Methods
• Parallel Computing
• +۲ مورد دیگر

Timo Heister is a Professor in the School of Mathematical and Statistical Sciences at Clemson University. His research focuses on computational mathematics, finite element methods, and high-performance computing applications in geophysics and materials science. He has supervised numerous PhD and Master's students including Pengfei Jia, Thomas Clevenger, Liang Zhao, and current advisees Anna Long, Quang Hoang, and Ryan Moulday. He has also mentored postdoctoral researchers Arushi Saxena, Vladimir Yushutin, and Jiaqi Zhang. His research explores: Parallel adaptive finite element methods for large-scale simulations Mantle convection modeling and geophysical fluid dynamics Phase-field fracture propagation and computational mechanics High-performance computing architectures and algorithms Scientific software development (deal.II, ASPECT) His publications primarily focus on numerical methods, computational geophysics, and parallel computing. Recent articles demonstrate advancements in finite element techniques, mantle convection simulations, and parallel algorithm design. Awards include the Mathematical Sciences Departmental Teaching Award (Clemson 2016/2017), KAUST Postdoctoral Fellowship (2011-2013), and DFG Research Training Group Fellowship (2009-2011). He leads significant NSF-funded projects including 'Future Proofing the Finite Element Library Deal.II' ($700,000) and 'Integrated Geodynamic Earth Models' ($393,570). He maintains leadership in the deal.II and ASPECT communities, organizing international workshops and hackathons.

Robert Kirbyمشاهده پروفایل

استاد

• Finite elements
• Partial differential equations
• Preconditioners
• +۲ مورد دیگر

Robert Kirby is a Professor of Mathematics and Undergraduate Advisor at Baylor University, affiliated with the College of Arts & Sciences and the Department of Mathematics. He holds a Ph.D. from the University of Texas at Austin (2000) and has held academic positions at Texas Tech University, the University of Chicago, and as a Dickson Instructor. His research focuses on automating numerical methods for partial differential equations (PDEs), including finite elements, preconditioners for multiphysics problems, and high-performance computing. Kirby's research interests bridge mathematics and computer science, emphasizing the development of efficient algorithms for PDE simulation using domain-specific languages, Bernstein polynomials, and multicore architectures. His work integrates theoretical analysis with practical software implementation in projects like FEniCS and Firedrake. His publications consistently address finite element methods, numerical stability, and computational efficiency. Recent articles emphasize automated time-stepping, domain truncation, and preconditioning techniques, reflecting a trend toward scalable and user-friendly tools for scientific computing. Grants and Advising: Kirby has secured significant funding, including NSF awards for automated algorithms and Sandia National Laboratory contracts. He advises doctoral students in mathematics and computing, with alumni at Amazon, NSA, and academia. Grants include: NSF CCF award 1117794: Metanumerical computing ($500k) NSF CCF award 0830655: Automated intrusive algorithms ($270k) DOE Early Career Award: Automatic parallel finite elements ($300k) He leads collaborations on open-source projects (FEniCS, Firedrake) and develops tools like FIAT and Irksome to streamline finite element workflows.

Amanda Diegelمشاهده پروفایل

استادیار

• Numerical Analysis
• Scientific Computing
• Partial Differential Equations
• +۳ مورد دیگر

Dr. Amanda Diegel is an Assistant Professor of Mathematics at Mississippi State University's Department of Mathematics and Statistics. Her research develops numerical methods for partial differential equations with applications in physics, biology, and materials science. Specializes in finite element methods, multigrid solvers, and stability analysis for complex systems including Cahn-Hilliard models, fluid-structure interactions, and liquid crystal dynamics. Published in leading computational mathematics journals on convergence analysis and numerical schemes for multiphysics problems. Holds PhD in Mathematics from University of Tennessee (2015) and completed postdoctoral research at Louisiana State University. Current work advances computational techniques for phase field models and coupled physical systems.

James Adlerمشاهده پروفایل

استاد

• Scientific Computing
• Numerical Analysis
• Partial Differential Equations
• +۳ مورد دیگر

James Adler is a Professor in the Department of Mathematics at Tufts University, within the School of Arts and Sciences. He is currently on academic leave. His research focuses on scientific computing and numerical analysis, particularly in developing efficient computational methods for complex fluids, plasma physics, electromagnetism, and multi-scale physical systems. He holds a PhD in Applied Mathematics from the University of Colorado at Boulder (2009), an MS in Applied Mathematics from the same institution (1996), and dual BAs in Mathematics and Physics (with a concentration in Atmospheric Science) from Cornell University (2004). Dr. Adler’s research emphasizes adaptive finite-element discretizations and multigrid solvers for nonlinear PDEs, aiming to preserve physical properties like energy conservation while optimizing computational efficiency. His work spans applications in magnetohydrodynamics, particle transport, and soft materials. He has developed tools like Morpho, a programmable environment for shape optimization and shapeshifting problems, and contributed to preconditioning techniques for Biot’s model and linear elasticity systems. His recent articles highlight advancements in numerical methods, including oscillation-free schemes for Biot’s model, physics-informed models for dynamical systems, and stable mimetic finite-difference methods. These contributions underscore his focus on bridging computational efficiency and physical accuracy across diverse applications. Adler has advised no listed students but actively collaborates on grants and research initiatives in computational mathematics. His lab work involves Morpho and related frameworks, advancing computational tools for engineering and material science challenges.

William Henshawمشاهده پروفایل

استاد

• Numerical Methods for Partial Differential Equations
• Overlapping Grids
• Adaptive Mesh Refinement
• +۵ مورد دیگر

William Henshaw serves as the Margaret A. Darrin Distinguished Professor in Applied Mathematics at Rensselaer Polytechnic Institute, affiliated with the Department of Mathematical Sciences. He maintains active roles in the Center for Modeling, Optimization and Computational Analysis (MOCA), Center for Computational Innovations (CCI), and Scientific Computation Research Center (SCOREC). His academic credentials include: Ph.D. in Applied Mathematics, California Institute of Technology, 1985 B.Math (Hons) in Applied Math and Computer Science, University of Waterloo, 1985 Dr. Henshaw's research centers on numerical methods for partial differential equations , with pioneering contributions to overlapping grid techniques . His work encompasses adaptive mesh refinement , multigrid algorithms , and high-order accurate methods for fluid dynamics, solid mechanics, and electromagnetics. He has developed robust boundary conditions for conjugate heat transfer and fluid-structure interactions, and is the primary architect of the Overture framework —an object-oriented platform for solving PDEs on complex geometries. Analysis of his 2021-2025 publications reveals sustained innovation in high-order numerical schemes, particularly for wave propagation, Maxwell's equations in nonlinear media, and multi-physics problems. His work consistently addresses stability challenges at material interfaces and develops compatibility conditions for overset grids, demonstrating cross-disciplinary impact from electromagnetics to marine engineering. Dr. Henshaw leads the Overture project, which provides computational infrastructure for multi-domain simulations. His framework enables efficient solutions for problems requiring moving grids, adaptive refinement, and coupled physics, serving as a foundation for both academic research and industrial applications in computational science.

Irfan Altasمشاهده پروفایل

دانشیار

• Data Mining
• Image Processing
• Mesh Generation
• +۳ مورد دیگر

Irfan Altas is an Associate Professor in Information Studies and the Head of School at the School of Computing, Mathematics and Engineering, Charles Sturt University. He holds a PhD from the University of Saskatchewan and an MSc from Middle East Technical University (METU). Prior to his current role, he has worked at multiple universities including the University of Saskatchewan (Canada), METU (Turkey), George Washington University (USA), and the University of Queensland (Australia). Education: PhD Sask (University of Saskatchewan), MSc METU (Middle East Technical University). His research interests span data mining, image processing, mesh generation, multigrid algorithms, and computational fluid dynamics. He has conducted international and national research projects in these domains. No specific scientific awards or grants are explicitly listed in the provided texts. He currently leads the School of Computing, Mathematics and Engineering, focusing on advancing computational and mathematical education and research.

Andrei Draganescuمشاهده پروفایل

دانشیار

• Numerical Analysis
• Partial Differential Equations (PDEs)
• Multilevel Algorithms
• +۴ مورد دیگر

Andrei Draganescu is an Associate Professor in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC). He holds a Ph.D. in Applied Mathematics from the University of Chicago (2004) and a B.Sc. in Mathematics from the University of Bucharest, Romania (1993). Before joining UMBC in 2006, he completed a postdoctoral appointment at Sandia National Laboratories. He currently serves as the Graduate Program Director for the Applied Mathematics program at UMBC. His research focuses on numerical analysis of partial differential equations, particularly multilevel algorithms for PDE-constrained optimization. He has led or co-led multiple grants funded by the National Science Foundation (NSF) and Department of Energy (DOE), including projects on multigrid methods, optimal control of PDEs, and optimization-based domain decomposition. Draganescu has advised several Ph.D. students and postdocs, including Sumaya Alzuhairy (2021), Mona Hajghassem (2017), and Jyoti Saraswat (2014). His publications span topics such as multigrid preconditioning, PDE-constrained optimization, and numerical linear algebra, with contributions to journals like SIAM Journal on Numerical Analysis and Numerical Linear Algebra with Applications . He has organized conferences such as the 2024 Fall Finite Element Circus and the Sayas Numerics Days. His teaching spans graduate and undergraduate courses in numerical analysis, matrix analysis, and differential equations.

Gordon Erlebacherمشاهده پروفایل

استاد

• Artificial Intelligence
• Data Science
• Machine Learning
• +۵ مورد دیگر

Gordon Erlebacher is a Professor at the Department of Scientific Computing at Florida State University (FSU), where he also serves as Program Director for Interdisciplinary Data Science . His work bridges artificial intelligence, scientific computing, and educational technology. B.Sc. , Free University of Brussels M.Sc. , Free University of Brussels Ph.D. , Columbia University His research spans from early contributions in GPU acceleration of numerical methods (2010s) to modern applications of transformer-based large language models (LLMs) in education and agentic systems for administrative automation. Key areas include: Neural networks (autoencoders, pruning, graph neural networks) Topic modeling and natural language processing Seismic wave propagation and geophysical simulations Interactive visualization tools for oceanographic data AI-driven educational platforms for classroom engagement His recent publications focus on AI in public health (CV19 SelfDefense) , Alzheimer's disease prediction , and GPU-accelerated seismic modeling . Earlier work includes foundational GPU implementations for seismic wave propagation and turbulence simulations. No scientific awards are explicitly mentioned in the provided text.

Paul Carterمشاهده پروفایل

استاد آموزشی

• Computer Science Education
• Scientific Computation
• Computer Vision
• +۳ مورد دیگر

Paul Carter is a Teaching Professor in the Computer Science department at the University of British Columbia (UBC), Faculty of Science. He has been a faculty member at UBC since 2001, previously teaching at Capilano University. His academic background includes a PhD and MSc from UBC (1993 and 1988) and a BSc in Applied Mathematics from Sheffield University (1985). Research & Teaching: Dr. Carter focuses on innovative pedagogy including flipped classrooms and active learning. He developed multimedia pre-instruction tools and Docker-based grading systems for CS courses. His earlier research involved multigrid methods in computer vision. Awards: Multiple teaching awards including the prestigious Killam Teaching Prize and repeated recognition from UBC's CS Department. Courses Taught: Extensive teaching in CPSC 210 (Software Construction) and CPSC 110/APSC 160 (Introduction to Computation) across multiple terms since 2001. Courses emphasize practical coding skills and computational literacy. Professional Engagement: Collaborated on auto-grading systems with Reid Holmes and Andrew Stec. Maintains a lab focused on educational technology and computational methods.

Marc Alexander Schweitzerمشاهده پروفایل

استاد

• Numerical Methods
• Computational Mechanics
• Meshfree Methods
• +۴ مورد دیگر

Marc Alexander Schweitzer is a Professor of Mathematics at the University of Bonn and affiliated with Fraunhofer SCAI. His work focuses on numerical methods and computational mechanics, particularly meshfree methods, peridynamics, and algebraic multigrid techniques. He contributes to advancing computational tools for engineering and materials science applications. Research interests include partition of unity methods, multiscale modeling, fracture mechanics, and efficient linear solvers. His work integrates mathematical theory with practical computational challenges, such as adaptive algorithms and parallel computing. Publications highlight contributions to peridynamic fracture models, scalable solvers for material design, and visualization of fracture progression in particle-based systems. Collaborative efforts with Fraunhofer SCAI emphasize industrial applications and algorithmic innovation.

Weihong Guoمشاهده پروفایل

استاد

• Mathematical Image Analysis
• Inverse Problems
• Scientific Computing
• +۳ مورد دیگر

Professor Weihong Guo holds the position of Professor and Chair in the Department of Mathematics, Applied Mathematics, and Statistics at Case Western Reserve University. His research focuses on Mathematical Image Analysis, Inverse Problems, Scientific Computing, and Computer Vision, with significant contributions to medical imaging and computational mathematics. His work emphasizes developing advanced algorithms for image segmentation, denoising, and reconstruction, often integrating deep learning with traditional variational methods. Notable applications include lung nodule detection, MRI muscle segmentation, and quantum tensor decomposition in biomedical contexts. Guo’s research also extends to graph signal processing, dynamic network analysis, and sparse recovery techniques. He has published extensively in top-tier journals and conferences, demonstrating a strong interdisciplinary approach bridging applied mathematics, computer science, and biomedical engineering.

Richard Browerمشاهده پروفایل

استاد

• Lattice Field Theory
• Quantum Computing
• High-Performance Computing
• +۵ مورد دیگر

Richard Brower is a Professor in the Department of Electrical and Computer Engineering at Boston University's College of Engineering, with an affiliated faculty role in the Department of Physics. His research focuses on numerical lattice field theory, quantum computing, and high-performance computing for exascale supercomputers. He holds a PhD in Physics from the University of California. Education: B.S. Physics, Harvard University, 1963 M.A. Applied Math, Harvard University, 1964 PhD, Physics, University of California, 1968 Research Interests: Brower advances multi-grid software for exascale systems, explores quantum computing applications for qubit and analog hardware, and investigates quantum physics in particle, string, and gravity contexts. His work includes stealth dark matter studies, Lorentzian conformal field theories, and lattice QCD simulations. Publications Trends: His recent articles address lattice quantization methods, stealth dark matter spectra, and conformal symmetry in 2D/3D Ising models. He frequently integrates geometric and topological approaches with computational physics. Scientific Awards: A.P. Sloan Research Fellow (1974–1976) Past Managing Editor, International Journal of Computational Physics Advising & Grants: Brower collaborates on DOE exascale computing projects and leads efforts in multigrid algorithms for fermion calculations. His work spans lattice gauge theory applications in quantum computers and beyond-Standard-Model physics. Labs/Teams: Affiliated with BU's computational physics and quantum computing research groups, contributing to national HEP computing initiatives.