Fast Direct Solvers Professors and Supervisors

Natalie BeamsView profile

Academic

University of TennesseeUnited States

Natalie Beams is a Research Assistant Professor at the Innovative Computing Laboratory (ICL) within the University of Tennessee's Department of Electrical Engineering and Computer Science (EECS). She holds a PhD in Theoretical and Applied Mechanics from the University of Illinois at Urbana-Champaign (UIUC), an MS from UIUC, and a BS in Mechanical Engineering (summa cum laude) from the University of Oklahoma. Her research focuses on numerical methods for PDEs, high-performance computing (HPC), and GPU-accelerated algorithms, with contributions to projects like the Exascale Computing Project's CEED and CLOVER initiatives. Key research areas include finite element methods , integral equation solvers , and mixed-precision algebraic multigrid techniques . She has developed software tools such as the libCEED library for high-order discretizations and contributed to the Ginkgo and MAGMA libraries. Her work emphasizes exascale computing, GPU optimization, and parallel algorithm design. Education: PhD in Theoretical & Applied Mechanics, UIUC (2017) MS in Theoretical & Applied Mechanics, UIUC (2014) BS in Mechanical Engineering, University of Oklahoma (2010) Awards & Honors: Best Workshops Paper Award, PPAM Conference (2022) 2011/2012 Computational Science & Engineering Fellow 2010 College of Engineering Carver Fellow List of Teachers Ranked as Excellent by Students (UIUC, 2014) Grants & Projects: Active contributor to the Exascale Computing Project (ECP), leading efforts in CEED (libCEED library) and CLOVER (MFEM-Ginkgo interoperability). Collaborates with Rice University and other institutions on HPC and numerical algorithms. Labs & Teams: Core member of the Innovative Computing Laboratory (ICL) at UTK, specializing in exascale software and GPU-accelerated computing.

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Xuemei ChenView profile

Associate Professor

University of California, San DiegoUnited States

Xuemei Chen is an Associate Professor in the Department of Mathematics and Statistics at the University of North Carolina Wilmington (UNCW), where she has been a faculty member since 2020, promoted to Associate Professor in 2023. She previously served as an Assistant Professor at New Mexico State University (2018–2020) and the University of San Francisco (2016–2018), following postdoctoral positions at the University of Missouri Columbia and the University of Maryland College Park. Ph.D. in Mathematics, Vanderbilt University Research focus: Applied harmonic analysis, compressed sensing, frame theory, and high-dimensional data solvers Active Principal Investigator on NSF grants DMS-2307827 and DMS-1908880 Mentors undergraduate and graduate students in signal and image processing projects Her research lies at the intersection of mathematical signal processing and data science, with a strong emphasis on theoretical foundations of sparse representations, frame design, and iterative algorithms like the Kaczmarz method. She investigates atomic norm minimization, matrix separation, and structured signal recovery, contributing to both theoretical understanding and practical applications in image inpainting and tensor recovery. Her work frequently appears in top journals such as SIAM Journal on Imaging Sciences and Applied and Computational Harmonic Analysis . The 15 most recent articles reflect a consistent trajectory in applied harmonic analysis and compressed sensing, with increasing focus on structured recovery, frame optimization, and algorithmic convergence. Keywords span Mathematics, Signal Processing, Numerical Analysis, Optimization, Image Processing, and Data Science , with subfields including Frame Theory, Sparse Recovery, Kaczmarz Algorithms, Atomic Norm Minimization, Low Coherence Frames, and Tensor Recovery . The trend shows deepening theoretical analysis paired with algorithmic innovation for high-dimensional problems. NSF DMS-2307827: Recovering structured signals: atoms, matrix separation, and applications (PI, 2023–2026) NSF DMS-1908880: Recovering Signals Sparse in a Frame: Theory and Applications (PI, 2019–2023) Xuemei Chen actively advises students, including undergraduates engaged in research on sparse signal recovery and image processing. She supports students through research assistantships and hourly positions, emphasizing strong analytical and coding skills. Her teaching portfolio includes courses in statistics, regression, numerical analysis, linear algebra, and data science, reflecting her interdisciplinary expertise. She maintains open educational resources on GitHub and encourages student engagement through research and professional development opportunities. She leads a research group focused on mathematical data science, with projects suitable for master’s and advanced undergraduate students. Her GitHub repositories, such as Undergraduates and Image-Processing , support student training and open collaboration. She fosters a research environment that bridges pure mathematics with real-world applications in data and image analysis.

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Mikael MortensenView profile

Professor

University of OsloNorway

Mikael Mortensen is a Professor in Fluid Mechanics at the University of Oslo 's Department of Mathematics. His research spans Computational Fluid Dynamics (CFD), turbulent flow modeling, spectral numerical methods, and applications in biomedical flows, turbulent combustion, and space physics. He contributes to the 4DSpace strategic research initiative studying ionospheric instabilities. Primary Affiliation: Department of Mathematics, University of Oslo Collaborations: Center for Biomedical Computing (Simula), KAUST, Chinese Academy of Sciences Research Themes High-performance spectral Galerkin methods ( Shenfun ) Direct Numerical Simulation of turbulence ( spectralDNS ) Space plasma physics and ionospheric turbulence Multiphase flow modeling in industrial contexts Machine learning applications in fluid dynamics Publication Trends Mortensen's recent work focuses on spectral methods for turbulence modeling, plasma instability simulations, and machine learning applications in fluid dynamics. His articles demonstrate expertise in Python-based high-performance computing frameworks for CFD and interdisciplinary applications in space physics and biomedical engineering. Supervision PhD Students: Anna Piterskaya (2019-2025), Tormod Landet (2016-2019), Christopher Friedemann (2018-2020) Master Students: Jacob H. Hudtwalcker, Jacob T.S. Langmoen, Kei Yamamoto, Sverre Vinje Collaborative Initiatives Co-founder of the Kobe-Oslo Partnership (10 MNOK funding from Norwegian Research Council) Organized 15+ international workshops on plasma simulations Active in Python-based scientific computing education

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Joar BaggeView profile

Research Fellow

University of Texas at AustinUnited States

Joar Bagge is a Postdoctoral Fellow at the Center for Numerical Analysis within the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin, where he joined in September 2023 to collaborate with Prof. George Biros and Prof. Gunnar Martinsson on computational engineering projects. He earned his PhD in Applied and Computational Mathematics from KTH Royal Institute of Technology, Stockholm, Sweden, in 2023 under the supervision of Prof. Anna-Karin Tornberg, focusing on special quadrature methods and fast summation techniques for boundary integral equations and Stokes flow. Bagge's research integrates numerical analysis, machine learning, and high-performance computing to develop efficient algorithms for microscale fluid simulations and elliptic PDEs. His work emphasizes special quadrature methods, fast direct solvers, and boundary integral equation formulations, with practical applications in scientific computing. His scientific recognition includes: Peter O'Donnell, Jr. Postdoctoral Fellowship in Computational Engineering and Sciences Bagge actively contributes to the Parallel Algorithms for Data Analysis and Simulation Group at the Center for Numerical Analysis, developing open-source software like the Spectral Ewald Unified package (SE_unified_v2) for fast summation methods in particle simulations.

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Carlos BorgesView profile

Associate Professor

University of Central FloridaUnited States

Carlos Borges is an Associate Professor in the Department of Mathematics at the University of Central Florida (UCF), part of the College of Sciences. He holds a Ph.D. in Mathematics from Worcester Polytechnic Institute (2013) and has held postdoctoral positions at the Oden Institute (University of Texas-Austin) and the Courant Institute (NYU). His research focuses on inverse scattering problems, numerical analysis, and neural networks, with applications in computational engineering and mathematical physics. Education: PhD in Mathematics (Worcester Polytechnic Institute, 2013), MS in Mathematics (Instituto de Matematica Pura e Aplicada, 2007), BEng in Computer Engineering (Instituto Militar de Engenharia, 2002). Research interests include inverse scattering (acoustic, electromagnetic), domain decomposition methods, fast solvers, and machine learning integration in numerical analysis. His work emphasizes high-resolution parameter reconstruction and multi-frequency data utilization for obstacle identification. He advises four students: PhD candidates Isabela Vasconcellos Viana and Nick Arustamyan, and undergraduates Tina Tran and Eli Rivera-Sanchez. His recent publications (2023–2025) highlight advances in neural network-driven inverse scattering and robust algorithms for irregular geometries. He organizes the Scientific Computing Seminar at UCF, focusing on numerical analysis and computational methods. His software contributions include solvers for inverse scattering problems, available on GitHub. He is active in professional societies like SIAM and IEEE, and regularly presents at conferences such as SIAM CSE 2025.

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Michael O'NeilView profile

Associate Professor

New York UniversityUnited States

Michael O'Neil is an Associate Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University, and holds an affiliation with the Tandon School of Engineering. He holds a Ph.D. in Applied Mathematics from Yale University (2007), an M.Phil. from Yale (2005), and an A.B. in Mathematics from Cornell University (2003). His research focuses on fast numerical algorithms for solving integral equations in mathematical physics and computational statistics. Key areas include electromagnetic scattering, computational fluid dynamics, and Gaussian processes. He has developed methods such as the Fast Multipole Method (FMM), butterfly algorithms, and high-order quadrature schemes for singular integrals. O'Neil has led multiple grants, including ONR awards for electromagnetic design solvers and fast algorithms for inverse problems. He advises several Ph.D. students and collaborates with researchers at institutions like the Flatiron Institute and Columbia University. His teaching spans numerical methods, differential equations, and computational statistics, emphasizing practical applications in engineering and physics.

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Stéphane DevismesView profile

Professor

University of Picardie Jules VerneFrance

Stéphane Devismes is a Professor at the University of Picardy Jules Verne , affiliated with the ALCO team (Algorithmics and Complexity) in the Laboratoire MIS (UR 4290) . His research focuses on Distributed Algorithms , Fault-Tolerance , and Autonomous Mobile Robots with self-stabilizing properties. University: University of Picardy Jules Verne Department: Laboratoire MIS (UR 4290) Team: ALCO (Algorithmics and Complexity) Research Interests: Devismes specializes in Self-Stabilizing Algorithms for distributed systems, particularly in Graph Exploration and Robot Coordination . His work addresses challenges like chirality constraints , asynchronous environments , and polynomial convergence . Publication Trends: Recent articles emphasize infinite grid exploration with minimal robots, model checking for distributed algorithms, and efficient self-stabilization in directed networks. Themes include autonomous systems , resource allocation , and network dynamics , often leveraging propositional satisfiability and SAT solvers . Scientific Awards: 2022 Wilkes Award for "Terminating Exploration Of A Grid By An Optimal Number Of Asynchronous Oblivious Robots" Best Paper at NETYS'2020 Best Student Paper at ICDCN'2023 Grants & Projects: Involved in the ANR SkyData project for intelligent autonomous data systems and developed the SASA simulator for self-stabilizing algorithms. His HDR (2020) on "Generality and Efficiency in Self-Stabilizing Distributed Systems" highlights his leadership in certified algorithmic frameworks. Teaching & Mentorship: Authored seminal books like "Introduction to Distributed Self-Stabilizing Algorithms" (2019) and "Bases de données - Informatique BUT 1re année et L1" (2024). Co-organized conferences including AlgoTel'2025 and served on program committees for PODC , DISC , and SSS .

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Min Hyung ChoView profile

Associate Professor

University of Massachusetts LowellUnited States

Min Hyung Cho is an Associate Professor in the Department of Mathematical Sciences at the University of Massachusetts Lowell (UMass Lowell). He specializes in computational mathematics, applied mathematics, electromagnetics, numerical solutions of PDEs, and scientific computing. His research focuses on developing high-performance numerical algorithms for wave scattering problems in complex media, including layered media and metamaterials. He has contributed to integral equation methods, fast multipole methods (FMM), and volume integral equation techniques for solving Helmholtz and Maxwell equations in layered environments. Cho holds a Ph.D. in Applied Mathematics from the University of North Carolina at Charlotte. He has received the Teaching Excellence Award (2017) from UMass Lowell. His work has been supported by grants such as the NSF Collaborative Research grant (2020) and a Simons Foundation Collaboration Grant (2016). Cho’s recent research emphasizes spectrally-accurate numerical methods for acoustic scattering, robust fast direct solvers for quasi-periodic problems, and efficient algorithms for layered media Green’s functions. His publications span journals like Journal of Computational Physics , SIAM Journal on Scientific Computing , and Computer Physics Communications . His academic activities include organizing minisymposia at conferences like ICIAM and presenting at institutions such as Yale University and the University of Arizona. He also contributes to educational initiatives in differential equations and computational mathematics.

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Amir BoagView profile

Professor

Tel Aviv UniversityIsrael

Prof. Amir Boag is a Professor in the Physical Electronics Department of the School of Electrical Engineering at Tel Aviv University. He received his B.Sc. and B.A. degrees Summa Cum Laude in 1983, M.Sc. in 1985, and Ph.D. in 1991, all from the Technion - Israel Institute of Technology. His academic journey includes faculty positions at the Technion (1991-1992), a Visiting Assistant Professorship at the University of Illinois at Urbana-Champaign (1992-1994), and industry experience at Israel Aircraft Industries (1994-1999) before joining Tel Aviv University in 1999. Prof. Boag's research focuses on computational electromagnetics and acoustics, specializing in numerically efficient algorithms, beam representations of fields, radar imaging techniques including Synthetic Aperture Radar, quantum-electromagnetic simulations, and antenna/nano-antenna design. His work bridges theoretical developments with practical applications in electromagnetic and acoustic systems. His research group comprises over ten graduate students working on cutting-edge problems in wave physics. Prof. Boag has published more than 130 journal articles and presented over 300 conference papers. He has been instrumental in organizing the Tel Aviv University Antenna Symposium and Underwater Acoustics Symposium, with the 13th Underwater Acoustics Symposium scheduled for October 23, 2025. IEEE Fellow (2008) for contributions to integral equation based analysis, design, and imaging techniques Fellow of the Electromagnetics Academy Former Associate Editor for IEEE Transactions on Antennas and Propagation Holder of approximately ten patents in electromagnetics and antenna design Prof. Boag's research demonstrates strong continuity in developing efficient numerical methods for electromagnetic and acoustic problems, with increasing focus on quantum-electromagnetic interactions and nano-scale applications in recent years. His work maintains strong connections between fundamental theory and practical engineering applications, particularly in radar and imaging systems.

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Jannis TeunissenView profile

Researcher

National Research Institute for Mathematics and Computer ScienceNetherlands

Jannis Teunissen is a researcher in the Multiscale Dynamics group at Centrum Wiskunde & Informatica (CWI), the Dutch national center for mathematics and computer science. He also serves as a visiting lecturer at the Centre for mathematical Plasma Astrophysics at KU Leuven. Education: BSc in Physics & Astronomy and Master in Computational Science from University of Amsterdam PhD in computational plasma physics at CWI (obtained "cum laude") Postdoctoral research at KU Leuven's Centre for mathematical Plasma Astrophysics Dr. Teunissen's research focuses on computational plasma physics, particularly on simulating electric discharges. His work bridges theoretical modeling, computational methods, and experimental validation. He develops advanced computational techniques for studying streamer discharges, which are fast-moving ionized channels that form the first stage of sparks. These phenomena have important applications in environmental technology, high-voltage engineering, and atmospheric science. His research employs a range of computational methods including adaptive mesh refinement (AMR), plasma fluid modeling, particle-in-cell simulations, geometric multigrid solvers, and high-performance computing techniques. More recently, he has been applying machine learning methods to space weather research. Analysis of his publication history shows a strong focus on streamer discharge phenomena across different gas mixtures, with emphasis on macroscopic parameterization, electric field measurements, and radio emission calculations. Hershkowitz Early Career Award and Review (2024) from Plasma Sources Science and Technology Early Career Scientist Prize on Plasma Physics (2023) from IUPAP Student Award of Excellence of the Gaseous Electronics Conference (2015) PhD obtained "cum laude" (2015) Dr. Teunissen has been actively involved in several research projects including "Reliable nExt GENERation Actuation sysTEms (REGENERATE)" and "Plasma for Plants: Towards controlled and efficient plasma-activated water generation for a cleaner environment." His work has resulted in numerous publications focusing on streamer discharges in various gas mixtures, their radio emissions, electric field measurements, and computational modeling approaches. His research has significant implications for understanding natural phenomena like lightning and developing more environmentally friendly alternatives to traditional insulating gases used in high-voltage technology.

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