Find Academic Supervisors

• Numerical Analysis
• Computational Mechanics
• Phase-field Modeling
• +3 more

Prof. Dr. Carsten Gräser is a Professor in the Department of Applied Mathematics at Friedrich-Alexander University Erlangen-Nürnberg (FAU). His research focuses on numerical analysis, computational mechanics, and phase-field modeling, with an emphasis on nonsmooth optimization and multigrid methods for complex systems. 2025: Well-posedness of evaporation models for droplets. 2023: Multiscale fault systems and phase-field brittle fracture simulations. 2021: Membrane mechanics and DUNE framework developments. His work spans partial differential equations, computational geosciences, and biological membrane modeling, often involving finite element methods and nonlinear systems. Recent publications highlight applications in fluid dynamics, fracture mechanics, and geophysical fault simulation. His articles demonstrate a strong focus on numerical methods for phase-field models and nonsmooth systems, with recurring sub-fields like multigrid algorithms, membrane-mediated interactions, and finite element discretization. He collaborates widely on topics such as marine ice sheets and solder alloy coarsening. At FAU, Gräser teaches courses including Numerics of Incompressible Flows and Modeling, Simulation, and Optimization . His research integrates software development (e.g., the DUNE framework) with applications in physics, materials science, and geosciences.

Matthias MayrView profile

Lecturer

• Computational Mechanics
• Fluid-Structure Interaction
• High Performance Computing
• +6 more

Dr. Matthias Mayr is a Senior Researcher & Lecturer at the University of the Bundeswehr Munich, where he heads the Data Science & Computing Lab. He holds a permanent position at the Institute for Mathematics & Computer-Based Simulation and maintains affiliations with the RISK Research Center. Previously, he was a Postdoctoral Researcher at Sandia National Laboratories and TU München. His research focuses on coupled multiphysics problems including fluid-structure interaction, computational contact mechanics, and high-performance numerical methods. He develops monolithic coupling schemes, parallel solvers, and adaptive algorithms for large-scale simulations. Key application areas include biomedical engineering, cardiovascular mechanics, and materials science. His publications demonstrate consistent focus on computational mechanics innovations, particularly in developing efficient algorithms for multiphysics problems. Recent work emphasizes mortar methods, multigrid preconditioners, and HPC implementations for complex coupled systems. Scientific Awards: Robert J. Melosh Medal (Duke University, 2017) Multiple scholarships including Max-Weber-Programm (Bavaria, 2008-2010) Rudolf Diesel Award (TU München, 2009) He advises students in computational mechanics and HPC topics, supervising projects on multigrid methods, contact algorithms, and parallel FSI solvers. He contributes to major scientific software including Trilinos and 4C Multiphysics. He leads the Data Science & Computing Lab and participates in international research collaborations. He maintains memberships in GAMM, SIAM, IACM, and EUROMECH.

Christos Papadopoulos FilelisView profile

Assistant Professor

• Computational Mathematics
• Mathematical and Computational Physics
• Numerical Methods
• +6 more

Christos Papadopoulos Filelis is an Assistant Professor at the Department of Electrical and Computer Engineering, Democritus University of Thrace, where he conducts research and teaches courses in computational mathematics and physics. His academic journey began with a Diploma in Electrical and Computer Engineering from Democritus University of Thrace in 2010, followed by a PhD in Numerical Analysis and High Performance Computing from the same institution in 2014. His research interests span Computational Mathematics, Mathematical and Computational Physics, Numerical Methods, Scientific Calculations, High Performance Computing, and Machine Learning applications in Mathematics and Physics. He has made significant contributions to pre-coordinated iterative methods, multi-grid and multi-level methods, parallel computing, cloud computing, and big data analytics. His work bridges theoretical mathematics with practical computational applications across multiple domains. Analysis of his recent publications reveals a strong focus on developing advanced computational techniques for solving complex mathematical problems, with particular emphasis on matrix computations, preconditioning methods, and time series analysis. His research demonstrates a consistent trajectory toward more efficient algorithms for high-performance computing environments, with increasing applications in machine learning and environmental sustainability. Dr. Papadopoulos Filelis has taught undergraduate courses including Physics, Numerical Analysis, Mathematical Software, Scientific Calculations, and Calculus of Changes. He has participated in European (H2020) and National funded projects such as the SFI FinTechNext project (UCC, Ireland), demonstrating his ability to secure competitive research funding. His work with the Physics Laboratory at Democritus University of Thrace involves developing computational frameworks that integrate theoretical physics with advanced numerical methods. He has collaborated extensively with researchers across Europe, particularly in cloud computing and high-performance computing projects that address real-world computational challenges.

Alex PothenView profile

Professor

• Combinatorial Scientific Computing
• Parallel Algorithms
• Graph Algorithms
• +3 more

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 BrannickView profile

Professor

• Lattice Field Theory
• Numerical Solutions to PDEs
• Multigrid Methods
• +3 more

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.

Timo HeisterView profile

Professor

• Computational Mathematics
• Finite Element Methods
• Parallel Computing
• +2 more

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 KirbyView profile

Professor

• Finite elements
• Partial differential equations
• Preconditioners
• +2 more

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.

James AdlerView profile

Professor

• Scientific Computing
• Numerical Analysis
• Partial Differential Equations
• +3 more

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 HenshawView profile

Professor

• Numerical Methods for Partial Differential Equations
• Overlapping Grids
• Adaptive Mesh Refinement
• +5 more

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.

Andrei DraganescuView profile

Associate Professor

• Numerical Analysis
• Partial Differential Equations (PDEs)
• Multilevel Algorithms
• +4 more

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.

Marc Alexander SchweitzerView profile

Professor

• Numerical Methods
• Computational Mechanics
• Meshfree Methods
• +4 more

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.

Richard BrowerView profile

Professor

• Lattice Field Theory
• Quantum Computing
• High-Performance Computing
• +5 more

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.

Eike MuellerView profile

Associate Professor

• Numerical Analysis
• Parallel Computing
• Partial Differential Equations
• +2 more

Dr. Eike Mueller is a Reader (Associate Professor) in the Department of Mathematical Sciences at the University of Bath. His research focuses on developing fast numerical algorithms for solving physical problems across scales, from atmospheric models to subatomic particles. As part of the Numerical Analysis group, he specializes in parallel computing implementations using frameworks like DUNE and Firedrake/PyOP2. His work is characterized by close collaboration with the Met Office on weather and climate forecasting models. Research interests center on numerical techniques for complex systems, emphasizing parallel computing architectures including GPU clusters. Key areas include massively parallel solvers for PDEs, multilevel Monte Carlo methods for atmospheric dispersion, and performance-portable frameworks for physics simulations. Recent work explores machine learning integration with traditional numerical methods for enhanced computational efficiency. Publications demonstrate consistent focus on accelerating scientific computation through novel algorithms. Recent articles emphasize the application of multigrid methods to climate modeling, neural networks in numerical integration, and Bayesian inference techniques. A significant portion of work targets performance optimization on modern hardware architectures including GPU acceleration. Research leadership includes principal investigator roles for major EPSRC projects: 'MGHyPE: An ExaHyPE version with a multigrid solver' and 'IAA – Accelerating climate- and weather-forecasts with faster multigrid solvers'. Collaboration with the Met Office has resulted in operational improvements to weather prediction models through bespoke solver development.

Sven GroßView profile

Researcher

• Computational Fluid Dynamics
• Numerical Analysis
• Finite Element Methods
• +3 more

Dr. Sven Groß is a researcher at the Institute for Geometry and Practical Mathematics at RWTH Aachen University, Germany, with an office in Hauptgebäude 237. His work focuses on advanced numerical methods for fluid dynamics, particularly incompressible two-phase flows, and he is a core developer of the DROPS software package for parallel flow simulations. He maintains active research collaborations across computational mathematics and chemical engineering disciplines. His educational background includes a diploma thesis (2002) and doctoral thesis (2008), both completed at RWTH Aachen University. The doctoral work, titled "Numerical methods for three-dimensional incompressible two-phase flow problems," earned him the prestigious Borchers-Plakette award. Groß's research centers on Computational Fluid Dynamics with emphasis on Finite Element Methods for interface problems. His expertise spans adaptive 3D FE techniques, level set methods, XFEM implementations, and parallelization strategies for two-phase flow systems. Current investigations address preconditioning for unfitted finite element methods, mass transfer in falling films, and high-performance computing approaches for gas absorption processes. His methodologies bridge theoretical numerical analysis with industrial applications in chemical process engineering. Analysis of his recent publications reveals a strong trajectory toward robust preconditioning techniques for interface problems and high-fidelity simulations of reactive multiphase systems. The work increasingly integrates high-throughput computing with traditional numerical methods, demonstrating growing emphasis on computational efficiency for complex industrial-scale problems while maintaining mathematical rigor in error analysis. His scientific recognition includes: Borchers-Plakette (RWTH Aachen, 2009) for doctoral studies Friedrich-Wilhelm-Preis (RWTH Aachen, 2003) for diploma thesis Springorum-Denkmünze (RWTH Aachen, 2003) for diploma Groß has secured significant research funding through Collaborative Research Center SFB 540, leading Project C7 on numerical methods for wavy falling film simulations and contributing to former Project C6 on parameter estimation. He actively mentors students through research collaborations and has supervised numerous diploma/PhD projects, with co-authors including Kirchhart, Ludescher, and Jankuhn appearing consistently in his publication record. As a principal investigator for the DROPS project, he leads a cross-disciplinary team developing parallel adaptive multigrid techniques for incompressible flow simulations. The project maintains strong industry connections with chemical engineering groups focused on falling film reactors and mass transfer optimization, with recent extensions into high-performance computing architectures for industrial-scale simulations.

Thomas HuckleView profile

Professor

• Numerical Linear Algebra
• Scientific Computing
• Parallel Computing
• +7 more

Prof. Thomas Huckle is a Professor of Scientific Computing at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology and the Department of Computer Science. His research focuses on numerical linear algebra, parallel computing, and their applications in physics and computer science. Key interests include solving linear problems on parallel architectures, image processing, multigrid methods, preconditioning, and tensor-based high-dimensional problem approximation. Education: Studied mathematics and physics at the University of Würzburg (diploma in mathematics, 1985 PhD, 1991 habilitation). Professional History: DFG-funded research at Stanford University (1993–1994), appointed to TUM in 1995, and member of the Mathematics Department since 1997. Research Interests: Prof. Huckle’s work spans numerical methods for large-scale systems, including structured matrices, regularization techniques, and quantum computing applications. He develops algorithms for parallel computing environments and contributes to software tools like ELPA for eigenvalue problems. Grants and Labs: Engaged in projects such as the ELPA-AEO eigensolver and ESSEX-II initiatives. Active in the SCCS (Scientific Computing and Computational Science) group at TUM, focusing on high-performance computing and numerical methods.