Find Academic Supervisors

• Numerical Analysis
• Numerical Linear Algebra
• Optimization
• +3 more

Dr. Jan Van Lent is a Senior Lecturer in Applied Mathematics at the University of the West of England , affiliated with the Faculty of Engineering and Technology and its Engineering, Design and Mathematics department. His academic focus spans numerical methods, computational mathematics, and interdisciplinary applications in biological modeling. His research interests include Numerical Analysis , Numerical Linear Algebra , Optimization , Scientific Computing , Machine Learning , and Optimal Transport . He has contributed extensively to numerical methods for PDEs and multigrid techniques, alongside interdisciplinary work in ant colony behavior modeling. His publications reflect a strong emphasis on multigrid methods for partial differential equations (e.g., energy-minimizing coarse spaces , implicit Runge–Kutta discretizations ), with applications in both traditional numerical analysis and biological systems. His 2020 work on Python in teaching highlights his pedagogical contributions to computational education.

Matthias BoltenView profile

Professor

• Numerical Analysis
• Numerical Linear Algebra
• Multigrid Methods
• +4 more

Professor Matthias Bolten is a faculty member at the University of Wuppertal, leading the Scientific Computing and High Performance Computing group. His research focuses on numerical methods, including multigrid techniques, structured matrices, and PDE/ODE solvers, with applications in computational science and engineering. Current projects: DFG Collaborative Research Center 1701 (B04, C01), ExaStencils (SPP 1648), DAAD-funded collaboration on PDE-constrained optimal control, and contributions to the Human Brain Project. Professional memberships: Deutsche Mathematiker-Vereinigung (DMV), Gesellschaft für Angewandte Mathematik und Mechanik (GAMM), Society for Industrial and Applied Mathematics (SIAM). Contact: G.15.23, +49 202 439-3060, bolten@uni-wuppertal.de

Jérémie GaidamourView profile

Researcher

• Quantum Mechanics
• Numerical Methods
• High Performance Computing
• +5 more

Jérémie Gaidamour is a CNRS Research Engineer at the Institut Élie Cartan de Lorraine (University of Lorraine), specializing in high-order numerical methods and high-performance computing (HPC) for quantum mechanics simulations. His work focuses on parallel solvers for large sparse linear systems, including hybrid direct-iterative methods and algebraic multigrid (AMG). PhD Thesis: "Conception d'un solveur linéaire creux parallèle hybride direct-itératif" (2009, Université de Bordeaux I) His research interests span Quantum Mechanics (Bose-Einstein condensates via Gross-Pitaevskii equations), Numerical Methods (pseudo-spectral techniques, domain decomposition), and High-Performance Computing (MPI, parallel algorithms). Recent publications highlight advancements in fractional gradient flows and energy-minimizing AMG preconditioners for nonlinear Schrödinger equations. Software development projects include BEC2HPC (parallel spectral methods for BECs), MueLu (AMG solver in Trilinos), and HIPS (hybrid direct-iterative sparse solver). He has contributed tutorials on HPC tools (MPI, OpenMP, GPU/Xeon Phi) and participated in Grid'5000/IDRIS support teams.

Kanat TangwongsanView profile

Researcher

• Algorithms
• Parallel Computing
• Streaming Algorithms
• +6 more

Kanat Tangwongsan is a researcher and educator in computer science, with a PhD from Carnegie Mellon University and prior roles at IBM T.J. Watson Research Center. He has extensive experience in algorithm design, parallel computing, and self-adjusting computation, with publications in top-tier venues like DEBS, ICDE, SPAA, and TOCS. Education: PhD in Computer Science (Carnegie Mellon University), B.S. in Computer Science and Mathematics (Carnegie Mellon University) Research Interests: His work spans algorithms , parallel computing , streaming data structures , and self-adjusting programs , focusing on theoretical improvements and practical implementations for graph problems, convex hulls, and network design. Article Trends: His publications emphasize low-latency streaming , parallel SDD solvers , k-means clustering , and triangle enumeration , with applications in large-scale graph analysis, numerical systems, and cache-oblivious methods. Advising and Collaborations: He has collaborated with prominent researchers like Guy E. Blelloch, Martin Hirzel, and Anupam Gupta, contributing to open-source algorithm frameworks and educational materials.

Ioannis KoutisView profile

Associate Professor

• Algorithms
• Numerical Linear Algebra
• Combinatorial Optimization
• +3 more

Ioannis Koutis serves as Associate Professor in the Computer Science Department at New Jersey Institute of Technology and holds an Adjunct Faculty position in the Computer Science Department at Carnegie Mellon University. His research expertise spans algorithm design with specialization in numerical linear algebra , combinatorial optimization , and spectral methods . He leads development of significant computational tools including CMG (Combinatorial Multigrid), SpA (Spectral Algorithms), and SpClu (Spectral Constrained Clustering), which address complex problems in large-scale system solving. Analysis of his publication record reveals focus on efficient solvers for symmetric diagonally dominant systems, with applications spanning computer vision and image processing. His work demonstrates strong integration of theoretical foundations with practical implementations. NSF grant #1018463 as co-PI NSF grant CCF-#1149048 supporting CMG development Dr. Koutis maintains active research collaborations and has established software projects that have gained recognition in the computational research community, with his GitHub repositories showing significant engagement from other researchers.

Achi BrandtView profile

Professor

• Multigrid Methods
• Multiscale Scientific Computation
• Fluid Dynamics
• +13 more

Prof. Achi Brandt is a leading figure in multiscale scientific computation at the Weizmann Institute of Science , where he holds a professorship in the Department of Applied Mathematics & Computer Science. He also serves as Professor in Residence at the Department of Mathematics, University of California, Los Angeles , and is Chief Scientist at VideoSurf , a video search engine leveraging computer vision. Affiliations: Weizmann Institute (Departments of Applied Mathematics & Computer Science) University of California, Los Angeles (Mathematics Department) VideoSurf (Chief Scientist) Research Focus: Multigrid Methods for PDEs Multiscale Algorithms in Physics & Engineering Computer Vision & Image Processing Optimization & Control Theory Quantum Field Theory Simulations Publications and Technical Reports demonstrate expertise in multigrid solvers for fluid dynamics, quantum equations, and computer vision tasks. His work bridges theoretical mathematics and practical applications in data assimilation, tomography, and machine learning. Collaborations: Extensive co-authorships with researchers in computer vision (Sharon, Galun, Basri), fluid dynamics (Thomas, Diskin), and optimization (Ron, Safro) indicate cross-disciplinary impact.

Barry LeeView profile

Associate Professor

• Numerical PDEs
• Computational Linear Algebra
• Multigrid Methods
• +1 more

Dr. Barry Lee is an Associate Professor in Southern Methodist University's Department of Mathematics. He develops computational methods for large-scale industrial and scientific applications, focusing on scalable algorithms for partial differential equations and integral equations. His research includes efficient solvers for Boltzmann transport equations (neutron/photon transport), Maxwell equations (fusion applications), elasticity equations (structural design), and power grid networks. A core focus is developing multigrid and multilevel methods optimized for parallel computing platforms. Lee collaborates with researchers at Lawrence Livermore National Laboratory, Pacific Northwest National Laboratory, and ERCOT. His work appears in SIAM journals, Numerical Linear Algebra with Applications, and Mathematics of Computation.

Jaap van der VegtView profile

Professor

• Numerical Analysis
• Scientific Computing
• Finite Element Methods
• +2 more

Jaap van der Vegt serves as Professor in Mathematics of Computational Mechanics and heads the Chair of Mathematics of Computational Science within the Department of Applied Mathematics at the University of Twente's Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), with additional affiliation to the MESA+ Institute. His research centers on Numerical Analysis and Scientific Computing, specializing in finite element methods for partial differential equations—particularly discontinuous Galerkin formulations. Key contributions include solution adaptive techniques (a priori and a posteriori error analysis), higher-order discretizations, structure-preserving compatible schemes, and multigrid solvers for algebraic systems. These methodologies are rigorously applied to computational fluid dynamics (encompassing compressible, incompressible, multiphase, and free surface flows) and computational electromagnetics, driving innovations in mathematical modeling of complex physical phenomena. Prof. van der Vegt operates within the Mathematics of Computational Science (EEMCS-AM-MACS) research group, leveraging synergies with the MESA+ Institute to advance interdisciplinary computational mathematics and address multiscale engineering challenges.

• Applied Mathematics
• Computational Science
• Plasma Physics
• +7 more

Giovanni Samaey is a Professor of Applied Mathematics and Mathematical Engineering at KU Leuven's Faculty of Engineering Science. He leads research in computational and multiscale methods, focusing on plasma edge modeling for nuclear fusion reactors, Bayesian inversion, and Monte Carlo algorithms. Appointed in 2011, he currently supervises ten PhD students and has held a five-year membership in the Young Academy. His work bridges academic research with societal impact, co-founding Platform Wiskunde Vlaanderen to strengthen mathematics in Flanders. Education: Graduated in Computer Science (specializing in applied mathematics) from KU Leuven (1996), completed a PhD in 2001 under Prof. Dirk Roose, supported by an NFWO fellowship. He transitioned from engineering studies due to a passion for mathematics' societal impact, initially avoiding academia but ultimately embracing teaching and research. Research Interests: Development of numerical methods for multiscale phenomena, including micro-macro acceleration algorithms, multilevel Monte Carlo techniques, and hybrid fluid-kinetic models. His work addresses challenges in plasma physics, fusion energy systems, and inverse problems. Key contributions include the X-Factor book (with Joos Vandewalle) promoting mathematics outreach and advancing computational tools for plasma edge simulations. Awards/Honors: Member of the Young Academy (2016-2021), NFWO Aspirant Fellowship (2001-2002). His efforts in STEM advocacy and mathematics promotion through Platform Wiskunde Vlaanderen highlight his dedication to education and public engagement. Advising & Leadership: Supervises a team of PhD students in applied mathematics and computational science. Active in curriculum development and interdisciplinary collaborations, particularly in fusion energy modeling. His research group contributes to codes like EMC3-EIRENE for plasma edge simulations. Labs/Teams: Leads projects in multiscale numerical methods and plasma simulation within KU Leuven's engineering faculty. Collaborates internationally on fusion reactor modeling and Monte Carlo algorithm design, emphasizing computational efficiency and scalability.

• High-performance Computing
• Parallel Algorithms
• Scientific Computing

Professor Tobias Weinzierl heads the Scientific Computing research group at Durham University's Department of Computer Science. He is Co-director of the Institute for Data Science (IDAS) with responsibility for Large-Scale Computing initiatives and serves as inaugural director of the Master in Scientific Computing and Data Analysis (MISCADA). Professor Weinzierl is a reviewer for the EuroHPC JU and principal investigator on multiple ExCALIBUR projects. His research focuses on parallel algorithms, high-performance computing, and scientific computing methodologies. Key areas include adaptive mesh refinement, task-based parallelism, and exascale computing frameworks. Professor Weinzierl develops the Peano software framework for parallel grid traversals and contributes to the ExaHyPE engine for hyperbolic PDEs. His publications span parallel computing paradigms, performance optimization, and applications in computational geophysics. Research trends include GPU offloading, fault tolerance in HPC, and SYCL programming models. Professor Weinzierl has authored books including Principles of Parallel Scientific Computing and edited volumes such as Advanced Computing . He regularly presents at major HPC conferences and workshops worldwide. He advises postgraduate students working on parallel algorithms, GPU acceleration, and computational science applications. Research groups include the Scientific Computing and Data Analysis initiatives at Durham.

Stefan ZimmerView profile

Senior Lecturer

• Numerical Simulation
• Computational Science
• Sparse Grids
• +4 more

Stefan Zimmer is a Senior Lecturer and Academic Councillor at the University of Stuttgart, affiliated with the Simulation of Large Systems department under the IPVS institute. His work focuses on numerical simulation, computational science, and algorithm development with applications in sparse grids, multigrid methods, and high-performance computing. Research interests include adaptive algorithms for large-scale systems, model reduction techniques, and interdisciplinary challenges in technical education. He has contributed to foundational works in sparse grid techniques and parallel computing architectures. Publications span from 1993 to 2015, emphasizing advancements in numerical methods, data mining, and computational fluid dynamics. Notable works include the 2015 multigrid method for adaptive sparse grids and a 2009 textbook on modeling and simulation methodologies. No awards or student advisees are explicitly listed in the provided text.

Doreen De LeonView profile

Professor

• Numerical Analysis
• Applied Mathematics
• Mathematical Modeling
• +3 more

Doreen De Leon is a Professor in the Department of Mathematics at California State University, Fresno (Fresno State), affiliated with the College of Science and Mathematics. She has taught at Fresno State since 2004, preceded by four years at UCLA. B.S. in Electrical Engineering from UCLA M.A. in Mathematics from UCLA Ph.D. in Applied Mathematics from UCLA (2000) Her research focuses on Numerical Analysis and Applied Mathematics , particularly in wavelet multigrid methods, Euler-Cauchy equations, and mathematical modeling. She integrates computational tools like MATLAB and Maple into research and instruction. Recent publications highlight her work on variable-coefficient equations, wavelet preconditioning, and polynomial non-homogeneities in differential equations. She mentors both undergraduate and graduate students in projects related to computational mathematics and epidemic modeling. Undergraduate Students : Brennan Fagan, Ashleen Nagra, Travis Walker, Jose Mireles, Aramayis Orkusyan, Manuel Lopez, Majerle Reeves Graduate Students : Rawan Araim, Elda Bautista

• Numerical Analysis
• Scientific Computing
• Domain Decomposition Methods
• +4 more

Conor McCoid is a Research Fellow in Mathematics at McMaster University, specializing in numerical analysis, scientific computing, and computational geometry. His research focuses on domain decomposition methods, spectral collocation techniques, and algorithm development for computational problems such as phase field models and geometric intersections. He holds a PhD from the University of Geneva, where he worked on domain decomposition under Martin Gander, and a Master's from Simon Fraser University in spectral collocation methods with Manfred Trummer. His postdoctoral work at McMaster involves accelerating phase field models for fractures and optimizing Schwarz methods. McCoid's research interests include developing robust algorithms for geometric intersections (e.g., triangles and tetrahedra), improving spectral collocation methods for boundary value problems, and prototyping machine learning algorithms for music analysis (e.g., Mozart classification). His GitHub repository includes code for tetrahedral intersections, adaptive Schwarz methods, and music data processing using tools like music21 and scikit-learn. His publications explore numerical methods for multigrid cycles, nonlinear solvers, and geometric algorithms, often emphasizing robustness and efficiency in computational contexts. Collaborations include work with Blaise Bourdin (McMaster), Felix Kwok (Université Laval), and Martin Gander (Geneva).

John C. BowmanView profile

Professor

• Statistical closures in turbulence
• Spectral Reduction
• 3D vector graphics
• +10 more

John C. Bowman is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. He has maintained an active teaching career with courses spanning undergraduate to graduate levels, including Honours Calculus (Math 117/118), Real Variables (Math 417), Coding Theory (Math 422), Linear Algebra II (Math 225), and Statistical Theories of Turbulence (Math 655). His office is located in CAB 521, and he has consistently provided regular office hours for student consultation across multiple courses. Dr. Bowman earned his BS in Engineering from the University of Alberta, followed by an MA and PhD from Princeton University. His educational background has informed his diverse research trajectory spanning mathematical physics, computational mathematics, and scientific visualization. His teaching philosophy emphasizes deep conceptual understanding, as evidenced by his development of comprehensive hyperlinked 3D lecture notes for Honours Calculus. His primary research focuses on statistical theories of turbulence, particularly his development of Spectral Reduction as a reduced statistical description of turbulence. His work shows remarkable agreement with full numerical simulations, even in flows containing long-lived coherent structures. Additional research interests include 3D vector graphics (notably the Asymptote language he developed), implicit dealiasing of convolutions, exponential integrators, and exactly conservative integrators for numerical simulations. He has been involved with the University of Alberta's Geophysical Fluid Dynamics Research Group, where his work bridges theoretical mathematics with practical computational applications. Dr. Bowman's publication record demonstrates consistent contributions to computational mathematics and fluid dynamics over several decades. His recent work (2015-2024) shows continued innovation in computational methods for turbulence simulation, with particular attention to dealiasing techniques, efficient convolution algorithms, and novel approaches to spectral methods. His research has practical applications in assessing dissipation mechanisms in large-eddy simulations and modeling high-Reynolds number turbulence. As an educator, Dr. Bowman has developed comprehensive teaching materials and maintains an active engagement with students through office hours and detailed course websites. His commitment to mathematical education extends to high school outreach through contributions to the "Pi in the Sky" mathematics magazine. His work on the Asymptote vector graphics language has created a valuable tool for mathematical visualization and scientific publishing that integrates seamlessly with TeX.

Christopher BattyView profile

Associate Professor

• Computer Graphics
• Scientific Computing
• Physics-based Simulation
• +6 more

Christopher Batty is an Associate Professor and Director of Infrastructure at the University of Waterloo's Department of Computer Science. His research focuses on computer graphics and scientific computing, with an emphasis on physics-based numerical simulation of fluids and solids for applications in animation, visual effects, and interactive environments. He holds a Ph.D. from the University of British Columbia (2010) and a B.C.Sc. from the University of Manitoba (2004). His work spans fluid dynamics, solid mechanics, and geometry processing, addressing challenges like surface reconstruction, multi-scale simulations, and efficient solvers for complex fluid-solid interactions. Recent contributions include novel methods for divergence-free fluid editing, discrete elastic rod optimization, and Monte Carlo-based approaches for PDEs on surfaces. Batty’s research integrates computational geometry, numerical analysis, and optimization to create scalable and accurate tools for procedural fluid and solid simulation. His articles emphasize robustness in handling thin obstacles, narrow gaps, and intricate boundary conditions, often leveraging advanced techniques like closest point methods and monolithic solvers. No scientific awards are listed, though his extensive publication record reflects significant contributions to the field. He leads projects on adaptive liquid simulations, surface-only deformable models, and high-resolution embedded fluid surfaces.