Find Academic Supervisors

Steffen MarburgView profile

Professor

• Numerical Methods in Vibroacoustics
• Aeroacoustics
• Structural Optimization
• +6 more

Prof. Dr. Steffen Marburg is a Full Professor at the Chair of Acoustics of Mobile Systems within the TUM School of Engineering and Design at the Technical University of Munich. His research focuses on numerical methods in vibroacoustics, structural optimization, and acoustic modeling for applications in automotive, maritime, and musical instrument domains. Education: PhD from Technical University of Dresden (1998). Academic Career: Junior Professor at TU Dresden (2004), Chair of Technical Dynamics at University of the Federal Armed Forces Munich (2010), Full Professor at TUM (2015–present). Editorial Roles: Co-Editor-in-Chief of Journal of Theoretical and Computational Acoustics, Associate Editor of Journal of the Acoustical Society of America, Editor of Acoustics Australia and Mechanical Systems and Signal Processing. His research integrates computational acoustics, boundary element methods, and machine learning to address noise control and structural optimization challenges. Recent work explores acoustic metamaterials, viscothermal losses, and data-driven modeling. He has co-authored over 150 publications and led advancements in multifrequency solution methods and noise-insulating structures. Scientific awards include the Innovation Award of the Industrieclub Sachsen e.V. (1999). His editorial contributions and leadership in journals highlight his influence in computational acoustics and structural dynamics.

Karsten UrbanView profile

Professor

• Numerical Mathematics
• Scientific Computing
• Partial Differential Equations
• +9 more

Prof. Karsten Urban is a Full Professor of Numerical Mathematics at the University of Ulm, leading the Institute for Numerical Mathematics. He holds roles such as Dean of Studies in Computational Science and Engineering (CSE) and Deputy Spokesman for the Research Association for Scientific Computing in Baden-Württemberg. He is an active member of prestigious societies including the Deutsche Mathematikervereinigung (DMV) and SIAM. His academic journey includes a PhD from RWTH Aachen (1995), Habilitation (2001), and a full professorship at Ulm since 2005. Research focuses on numerical methods for PDEs, reduced basis techniques, multiscale simulations in fluid mechanics, biomechanics, quantum sciences, and financial mathematics. He has pioneered wavelet-based methods and collaborated with industries on ship propulsion and energy trading models. His work integrates mathematical rigor with real-world applications, emphasizing model reduction and computational efficiency. Editorial Roles: Managing Editor of Advances in Computational Mathematics , Editor of SN Partial Differential Equations and Applications . Awards: Teaching award of Baden-Württemberg (2005), Science-Economy Cooperation Awards (2004, 2008). Administrative Roles: Member of the University Council and ASIIN expert committee. Supervises doctoral students in numerical analysis, quantum simulations, and biomechanics. Active in interdisciplinary projects, including quantum systems (IQST) and fracture healing modeling in collaboration with biomechanics experts. His contributions bridge academia and industry, driving innovation in computational methods.

Christian EngwerView profile

Professor

• Partial Differential Equations
• Numerical Analysis
• Finite Element Methods
• +7 more

Christian Engwer is a full Professor at the University of Muenster in the Institute for Applied Mathematics, specializing in Analysis and Numerics. He leads the Engwer Group focused on Applications of Partial Differential Equations and is actively involved in the Cells in Motion initiative as a supervisor in the CiM-IMPRS Graduate Programme. His research centers on developing numerical methods for partial differential equations, particularly addressing challenges in complex geometries and multi-physics applications. He specializes in Unfitted Discontinuous Galerkin methods, which allow simulations on complex geometries without requiring domain-fitted meshes. His work spans porous media modeling, biological systems, and bioelectromagnetism applications, with significant contributions to EEG/MEG forward modeling in neuroscience. Analysis of his recent publications reveals a strong focus on model order reduction techniques, stabilized numerical schemes for cut-cell meshes, and applications in bioelectromagnetism. His work demonstrates a consistent trajectory toward developing robust, efficient numerical methods applicable to real-world problems in medical imaging and biological modeling, with increasing emphasis on high-performance computing implementations. Professor Engwer actively supervises doctoral students, with recent completions including Lukas Renelt (2025), Michael Wenske (2021), and Maria Carla Piastra (2019), among others working on topics related to numerical methods and biomedical applications. He leads several major research projects including BrainStorm: Highly Extensible Software for Advanced Electrophysiology and MEG/EEG Imaging (NIH-funded since 2019), multiple EXC 2044 Cluster of Excellence projects through 2025, and the InterKI interdisciplinary teaching program on machine learning and artificial intelligence. His group develops several important software packages including DUNE (Distributed and Unified Numerics Environment), duneuro (for bioelectromagnetism applications), and TPMC (Topology Preserving Marching Cubes). These tools support research in numerical methods and their applications to complex scientific problems.

Stephan RaveView profile

Researcher

• Numerical Analysis
• Scientific Computing
• Machine Learning
• +4 more

Dr. Stephan Rave is a Researcher in the Institute for Analysis and Numerics at the University of Münster. He is affiliated with the Applied Mathematics Münster cluster and serves as an Investigator in Mathematics Münster. His work focuses on numerical analysis, scientific computing, and machine learning, with a strong emphasis on model reduction techniques for complex systems. Education : PhD in Mathematics (2012), University of Münster, thesis on finitely summable K-homology. Master's and Bachelor's degrees in Mathematics from the University of Münster. Research Interests : Dr. Rave specializes in model order reduction (MOR) methods, including reduced basis techniques, localized orthogonal decomposition (LOD), and nonlinear approximation strategies. His work addresses challenges in multiscale modeling, domain decomposition, and parametrized partial differential equations. He also develops open-source software tools like pyMOR for MOR and contributes to initiatives like the MaRDI (Mathematical Research Data Initiative) to enhance interoperability in scientific computing. Projects : Key initiatives include the MaRDI project (2021–2026), EXC 2044 Cluster of Excellence (Geometry-based modeling), and MULTIBAT (lithium-ion battery simulation). His research bridges theoretical developments with practical applications in battery modeling, electrochemistry, and computational fluid dynamics. Grants & Awards : Funded by DFG, the German Federal Ministry of Research, and internal university grants, his work addresses strategic areas like sustainable research software and energy storage systems. He leads projects on distributed model reduction and communication-avoiding algorithms. Teaching : Dr. Rave teaches advanced numerical methods courses, including Model Order Reduction, Numerical Methods for PDEs, and Python-based computational labs. He co-organizes seminars and workshops on MOR and scientific software engineering.

Tony StillfjordView profile

Associate Professor

• Numerical Analysis
• Partial Differential Equations
• Stochastic Optimization
• +4 more

Tony Stillfjord is an Associate Professor at the Centre for Mathematical Sciences, Lund University, Sweden. He previously held postdoctoral positions at the Max Planck Institute for Dynamics of Complex Technical Systems and Chalmers/University of Gothenburg. Ph.D. and MSc in Numerical Analysis from Lund University Funded by WASP (Wallenberg AI, Autonomous Systems and Software Program) Research Interests : Numerical methods for partial differential equations, splitting schemes, stochastic optimization, and large-scale differential Riccati equations. His work focuses on developing low-rank approximations and robust optimization algorithms. Recent Publications highlight advancements in Lie/Strang splitting for operator-valued Riccati equations, stochastic descent methods, and GPU-accelerated splitting schemes. Software Contributions : Developed DREsplit (MATLAB package for differential Riccati equations) and BST20_CODE for stochastic optimization experiments. Contact : Office at MH:562E, Lund University. Email: tony.stillfjord@math.lth.se . URL: tonystillfjord.net

Barbara WohlmuthView profile

Professor

• Numerical Simulation
• Partial Differential Equations
• Discretization Techniques
• +4 more

Prof. Barbara Wohlmuth is a full professor in Numerical Mathematics at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology. She leads the International Graduate School of Science and Engineering at TUM and has held professorships at Stuttgart, Darmstadt, and Berlin universities. Her research focuses on numerical simulation of partial differential equations, multiscale solvers, and coupled multi-field problems with applications in engineering. Education: Studied mathematics at TUM and Université Joseph Fourier in Grenoble, received her doctorate from TUM in 1995, and completed habilitation in Augsburg. Visiting professorships in USA, France, and Hong Kong. Research interests include discretization techniques, predictive modeling, and interdisciplinary collaboration with engineering disciplines. Notable achievements: 2012 Gottfried Wilhelm Leibniz Prize (Germany’s highest academic honor in sciences), 2005 Sacchi-Landriani Prize. Publications emphasize advanced numerical methods in fluid dynamics, geophysics, and biomedical engineering. Active in editorial roles for international journals and scientific committees across Europe and USA. Elected member of Bavarian and European Academies of Sciences. Key contributions include: Development of robust numerical algorithms for exascale simulations Pioneering work in coupled multi-physics modeling Innovative methods for computational contact mechanics Leadership in graduate education initiatives

Benjamin StammView profile

Professor

• Numerical Analysis
• Scientific Computing
• High-Performance Computing (HPC)
• +7 more

Prof. Benjamin Stamm is a Professor of Numerical Mathematics at the University of Stuttgart, leading the Chair of Numerical Mathematics for High Performance Computing within Faculty 08. He holds a Ph.D. and master's degree in mathematics from École Polytechnique Fédérale de Lausanne (EPFL) and has previously worked at RWTH Aachen University, Sorbonne Université UPMC Paris 6, UC Berkeley, and Brown University. His research focuses on numerical analysis, scientific computing, and simulations, particularly efficient discretizations for PDEs, eigenvalue problems, error certification, reduced basis methods, and HPC implementations. He develops scalable numerical methods for problems in computational chemistry and physics, emphasizing accuracy, efficiency, and interdisciplinary collaboration with chemists, physicists, and materials scientists. Prof. Stamm’s work includes contributions to domain decomposition methods, polarization energy calculations, and software development like the ddX library. His publications span topics such as model order reduction, quantum simulations, and molecular dynamics. Collaborations and software tools underscore his commitment to bridging computational methods with real-world scientific challenges.

Benjamin KlusemannView profile

Professor

• Mechanics
• Process Simulation
• Material Modeling
• +3 more

Benjamin Klusemann is Professor of Materials Mechanics at the Institute for Production Engineering and Systems, Leuphana University of Lüneburg. He holds leadership positions including Chairman of the School of Management and Technology (2024), Chairman of the Masterprogramme, and Chairman of the Graduate School (since 2017), demonstrating his significant academic standing and administrative responsibilities within the university. His research spans multiple engineering disciplines with a strong focus on mechanics, process simulation, and material modeling. Professor Klusemann specializes in continuum mechanics and the finite element method, applying computational approaches to solve complex problems in materials science and manufacturing engineering. His work bridges theoretical modeling with practical applications in advanced manufacturing processes, particularly in friction-based joining techniques and material behavior analysis. Professor Klusemann's extensive publication record (224 publications) reveals a consistent research trajectory focused on advanced manufacturing techniques, particularly friction-based joining processes, material modeling, and simulation. His recent work emphasizes laser shock peening applications, intermetallic compound evolution in solid-state joining, and the mechanical behavior of nanocrystalline materials. His research demonstrates a strong interdisciplinary approach combining materials science, mechanical engineering, and computational modeling to address industrial challenges in lightweight materials processing. His notable scientific achievements include: Professor O.C.Zienkiewicz Award NUMIFORM 2023 Auszeichnung für herausragende Leistungen in der Forschung (Recognition for outstanding research achievements) ESAFORM Scientific Prize Professor Klusemann actively contributes to academic governance and the international research community. He has organized and participated in numerous conferences including ESAFORM, GAMM meetings, and specialized workshops on computational mechanics. His leadership extends to research projects focused on aluminum processing, material flow analysis, and data-driven design of recycled materials, demonstrating his commitment to both fundamental research and practical applications in manufacturing technology.

Mira SchedensackView profile

Assistant Professor

• Numerical Analysis
• Machine Learning
• Scientific Computing
• +3 more

JProf. Dr. Mira Schedensack is a faculty member at the Institute for Analysis and Numerics within the Department of Mathematics and Computer Science , University of Münster. Her expertise lies in Numerical Analysis, Machine Learning, and Scientific Computing , with a focus on finite element methods and numerical solutions for partial differential equations. Research Interests: Numerical methods for PDEs, mixed finite element formulations, adaptive algorithms, and thermo-optical interactions in computational physics. Teaching: Offers courses in Numerical Partial Differential Equations and Adaptive Finite Element Methods. Students: Mentors doctoral student Jonas Ketteler . Publications: Key contributions to non-conforming FEM, Stokes equations, and gradient elasticity.

Joscha GedickeView profile

Professor

• Numerical Analysis
• Computational Mathematics
• Finite Element Methods
• +3 more

Joscha Gedicke is a Professor at the Institute for Numerical Simulation (University of Bonn), specializing in Numerical Analysis , Finite Element Methods , and Scientific Computing . His research focuses on adaptive algorithms, error estimation, and computational methods for partial differential equations (PDEs) and optimal control problems. Contact: gedicke@ins.uni-bonn.de | +49 228 73-69835 Teaching: Lectures on Hybrid High-Order Methods (V5E1), Adaptive Finite Element Methods (S4E1), and Discontinuous Galerkin Methods (V5E5). Research Trends: Gedicke's work spans Numerical Methods for PDEs , Adaptive Finite Element Analysis , Mixed and Discontinuous Galerkin Formulations , and Error Estimation . His recent publications emphasize Virtual Element Methods and Robust Discretizations for magnetostatic and optimal control problems. Collaborative Networks: He collaborates with researchers in computational mathematics, including institutions like TU Munich, University of Milano-Bicocca, and the University of Bonn's research seminar on Mathematics of Computation .

Michael OrtizView profile

Professor

• Cardiac Mechanics
• Computational Mechanics
• Biomedical Engineering
• +3 more

Michael Ortiz is the Dotty and Dick Hayman Professor of Aeronautics and Mechanical Engineering at the California Institute of Technology (Caltech). He holds the Hans Fischer Senior Fellowship at the Technical University of Munich's Institute for Advanced Study (TUM-IAS) since 2010. His research focuses on developing the Advanced Cardiac Mechanics Emulator (ACME) to model human heart function in healthy and diseased states. He has a BS from the Polytechnic University of Madrid, and MS/PhD from UC Berkeley. Previously, he was at Brown University (1984–1995). Professor Ortiz leads Caltech’s DoE/PSAAP Center on High-Energy Density Dynamics of Materials. His honors include the IUTAM Rodney Hill Prize (2008), election to the American Academy of Arts & Sciences (2007), and Humboldt Research Award (2002). He has advised national labs like Lawrence Livermore and Sandia on predictive science and engineering review panels. His work spans computational mechanics, materials science, and multiscale modeling. Recent research emphasizes cardiac biomechanics, fracture mechanics, and quantum materials simulations. Over 100 highly cited papers showcase contributions to meshfree methods, variational fracture, and orbital-free DFT.

Andrea BarthView profile

Professor

• Stochastic Partial Differential Equations
• Uncertainty Quantification
• Monte Carlo Methods
• +4 more

Andrea Barth is a W3-Professor of Computational Methods for Uncertainty Quantification at the University of Stuttgart, leading the Research Group for Computational Methods for Uncertainty Quantification within the Excellence Cluster for Simulation Technology. She holds a Ph.D. from the University of Oslo (2009) and has held positions at ETH Zürich and the University of Stuttgart. Her work focuses on stochastic partial differential equations, numerical methods for uncertainty quantification, and applications in engineering and natural sciences. Education: Ph.D. in Mathematics, University of Oslo (2006–2009) Lecturer/Postdoc at ETH Zürich (2010–2013) Junior Professor at University of Stuttgart (2013–2017) Research Interests: Stochastic PDEs, uncertainty quantification, Monte Carlo methods, Bayesian inverse problems, and numerical analysis of random fields. Her work bridges stochastic analysis and numerical simulations, addressing challenges in modeling and simulating complex systems with uncertainties. Grants & Funding: Principal Investigator in projects like 'Data-Integrated Simulation Science' (ExC 2075) and 'Quantitative Methods for Visual Computing' (SFB/TRR 161). Her research also explores applications in porous media, carbon dioxide storage, and optical flow analysis. Supervision: Advised PhD students including Oliver König, Fabio Musco, and Robin Merkle. Current students focus on topics like deep learning for stochastic PDEs and continuous level Monte Carlo methods.

Lars GrasedyckView profile

Professor

• Numerical Analysis
• Hierarchical Matrices
• Tensor Approximation
• +7 more

Lars Grasedyck is a Professor of Numerical Analysis at RWTH Aachen University. His research focuses on hierarchical matrices, tensor approximation, and numerical methods for partial differential equations and matrix equations. He has contributed to applications in biomedical engineering, particularly EEG/MEG inverse problems, and is involved in software development (HLIB, HLIB-pro). Education: Diploma and Ph.D. in Mathematics at Christian-Albrechts-Universität zu Kiel (1998, 2001), Postdoctoral work at Max Planck Institute, Leipzig (2002-2010). Research: Specializes in high-dimensional numerical methods, low-rank matrices, and tensors with applications in PDEs, uncertainty quantification, and biomedical modeling. Projects: Leads DFG-funded initiatives on adaptive tensor networks for parametric PDEs and tumor progression modeling. Advising: Supervises doctoral students including Thong Le, Maren Klever, and Dieter Moser. Software: Developed HLib and HLib-pro for hierarchical matrix computations. Conferences: Active in GAMM Fachausschuss Numerische Analysis, organizing workshops and symposia globally.

Felix FritzenView profile

Professor

• Data Analytics in Engineering
• Computational Mechanics
• Material Modeling
• +7 more

Prof. Felix Fritzen is a Heisenberg Professor (W3) for Data Analytics in Engineering at the University of Stuttgart's Institute of Applied Mechanics (MIB). His work is embedded in the Cluster of Excellence Data-Integrated Simulation Science (SimTech). He leads the EMMA Emmy Noether group (2015–2020) and previously headed the KIT Young Investigator Group CAMM. His research focuses on data-driven surrogate models, uncertainty quantification, and computational mechanics of materials, with emphasis on nonlinear model reduction and multiscale simulations. Education: Ph.D. (Dr.-Ing., summa cum laude) from KIT (2011), Dipl.-Math. techn. (2007), Dipl.-Ing. (2006). Research interests include machine learning integration with mechanics, microstructure-property relations, and high-performance simulation techniques. Notable contributions involve FFT-based homogenization, reduced order modeling, and GPU-accelerated methods. He has authored over 60 peer-reviewed papers, including works on surrogate models for microstructure forecasting and thermoelastic material analysis. Awards include the KIT PhD Award (2012) and recognition as a GAMM Junior (2012–2014). His teaching includes courses on data processing for engineers and model order reduction. Funded projects include DFG grants (EXC-2075, HE 7919/1) and the Heisenberg Professorship (FR2702/8).

• High Performance Computing
• Performance Engineering
• Performance Modeling
• +7 more

Gerhard Wellein is a Professor for High Performance Computing at the Department of Computer Science of Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). He is the head of NHR@FAU (Erlangen National Center for High Performance Computing) and a member of the board of directors of the German NHR-Alliance. Since 2024, he has also served as a Visiting Professor for HPC at the Delft Institute of Applied Mathematics, Delft University of Technology. He holds a PhD in theoretical physics from the University of Bayreuth and has over two decades of experience in HPC education and research. Research Interests: His research focuses on performance modeling and engineering, architecture-specific code optimization, novel parallelization techniques, and the development of hardware-efficient building blocks for sparse linear algebra and stencil solvers. His work bridges computer science, applied mathematics, and computational physics, aiming to maximize efficiency on current and future HPC architectures, including exascale systems. Publication Trends: His recent publications emphasize analytical performance modeling (e.g., Roofline, oscillator models), energy efficiency, GPU optimization, and scalable linear algebra. They reflect a strong focus on both theoretical modeling and practical implementation, with applications in CFD, quantum physics, and molecular dynamics. Scientific Awards: 2011 Informatics Europe Curriculum Best Practices Award (shared with Jan Treibig and Georg Hager) for outstanding teaching contributions in HPC. Grants and Advising: He has led numerous third-party funded projects from the EU, BMBF, and DFG, including EoCoE-III, ESSEX, EXASTEEL, and ProPE. These projects focus on exascale software, performance engineering, fault tolerance, and multiscale simulation. He has mentored multiple researchers and students, contributing to the development of tools such as LIKWID, ClusterCockpit, and GEOPM. Labs and Teams: He leads the HPC research group at FAU and is deeply involved in national and international HPC initiatives. His team collaborates extensively on open-source HPC software and performance tools, fostering a strong community-driven approach to performance engineering.