جست‌وجوی استاد و پژوهشگر

Hans-Joachim Bungartzمشاهده پروفایل

استاد

• Scientific Computing
• Fluid Mechanics
• Plasma Physics
• +۴ مورد دیگر

Prof. Hans-Joachim Bungartz is a Full Professor of Scientific Computing at the Technical University of Munich (TUM), leading the Department of Computer Science. He holds the TUM School of Computation, Information and Technology affiliation. His career includes roles at the University of Augsburg and Stuttgart, and he has been at TUM since 2004. He specializes in scientific computing, focusing on numerical algorithms, HPC software, and applications in fluid mechanics, plasma physics, and quantum simulations. Education: Bachelor/Master in Mathematics, Informatics, and Economics (TUM, 1982–1989) PhD (1992) and Habilitation (1998) in Sparse Grids and Numerical Methods (TUM) Research Interests: His work spans adaptive grids, parallel computing frameworks (e.g., Peano), and interdisciplinary applications in computational engineering. He emphasizes bridging modeling, algorithms, and HPC infrastructure. Awards: ISC PRACE Award (2013) Bavarian Habilitation Award (1994) His contributions include over 150 publications and leadership roles in institutions like the Leibniz Supercomputing Center and the TUM Graduate School. Leadership: As Dean of the Informatics Department (since 2013) and Director of the TUM Graduate School, he shapes academic policies. He chairs key national/international bodies like the German Research Network (DFN) Executive Board (2011–2020).

Lukas Luftمشاهده پروفایل

پژوهشگر

• Robot Localization and Mapping
• Causality and Foundations of Quantum Physics
• Probabilistic Methods
• +۳ مورد دیگر

Lukas Luft is a postdoctoral researcher at the Autonomous Intelligent Systems group within the Department of Computer Science at the University of Freiburg. His work spans robotics and quantum physics, focusing on probabilistic methods for robot localization, multi-robot systems, and causal inference. Post Doc (2020–present) PhD in Computer Science, University of Freiburg (2020) Master and Bachelor in Physics, RWTH Aachen and University of Freiburg His research in Robot Localization and Mapping includes advanced probabilistic techniques like Bayes filters, decentralized algorithms for multi-robot systems, and change detection in environments using full posterior distributions. He also explores Causality and Foundations of Quantum Physics , applying entropic inequalities and information theory to causal discovery and non-locality. The articles highlight his contributions to robotics, particularly in sensor modeling for Lidar, simultaneous localization and mapping (SLAM), and efficient probabilistic methods. In quantum physics, his work addresses causal structures and entropic information, bridging AI with foundational physics. Luft has collaborated with leading researchers, including Prof. Wolfram Burgard and Bernhard Schölkopf, and contributed to key conferences like Robotics: Science and Systems (RSS) and IEEE IROS.

Peter Münchمشاهده پروفایل

پژوهشگر

• High-performance computing
• Matrix-free computations
• Iterative solvers
• +۷ مورد دیگر

Peter Münch is a postdoctoral researcher at the Chair of Numerical Methods for Partial Differential Equations within the Institute of Mathematics at Technical University of Berlin (TU Berlin), Faculty II - Mathematics and Natural Sciences. He has held research positions at Uppsala University, University of Augsburg, Helmholtz-Zentrum Hereon, and Technical University of Munich. Dr. Münch's research focuses on high-performance scientific computing with expertise in matrix-free computations, dynamic sparse communication patterns, node-level optimization, iterative solvers including multigrid and block preconditioners, and efficient algorithms for high-dimensional partial differential equations. His work spans discontinuous Galerkin methods, computational fluid dynamics, and simulation of additive manufacturing processes including solid-state sintering and melt-pool modeling. He is one of the principal developers of the deal.II finite-element library, which won the SIAM/ACM Prize in Computational Science and Engineering in 2025. His recent publications demonstrate significant contributions to matrix-free finite element methods, multigrid solvers, and applications in computational fluid dynamics and materials science. The research shows a strong trend toward high-performance implementations of numerical methods for extreme-scale computing, with particular emphasis on matrix-free approaches that avoid explicit storage of large sparse matrices. SIAM/ACM Prize in Computational Science and Engineering 2025 (for deal.II) Dr. Münch has supervised numerous student projects including Master's theses, Bachelor's theses, and term papers on topics ranging from immersed boundary methods to high-order discontinuous Galerkin methods. His teaching activities include courses on Numerical Methods for ODEs, PDEs, and High-Performance Parallel Computing. He has contributed to multiple deal.II tutorial programs (steps 19, 68, 75, 76, 87) demonstrating advanced finite element techniques. As a principal developer of the deal.II finite element library, Dr. Münch is actively involved in the open-source scientific computing community, contributing to one of the most widely used finite element frameworks in computational science and engineering. His GitHub profile shows consistent contributions to deal.II and related projects, with significant activity in 2025.

Miriam Schulteمشاهده پروفایل

استاد

• High-Performance Computing (HPC)
• Parallel and Distributed Systems
• Scientific Computing
• +۵ مورد دیگر

Miriam Schulte is a Professor at the Institute for Parallel and Distributed Systems (IPVS) at the University of Stuttgart . As Dean of Studies SimTech , she leads academic programs in simulation technology. Her research focuses on high-performance computing , multi-physics simulations , and scientific software development , with significant contributions to coupling libraries like preCICE and biophysical frameworks like OpenDiHu . Key Research Areas: High-Performance Computing (HPC) Multi-physics and Fluid-Structure Interaction (FSI) Sparse Grids and Hierarchical Numerical Methods Machine Learning in Simulation Software Parallel and GPU-Accelerated Algorithms Advising: Guided student projects on quantum neural networks , GPU-optimized sparse grids , and SYCL-based HPC frameworks . Coordinated SimTech Research Modules and IPVS/SGS team initiatives. Software Leadership: Maintains preCICE (coupling library for multi-physics) Develops OpenDiHu (neuromuscular simulations) Advances PLSSVM (parallel SVM library) and SG++ (sparse grids) Her recent publications (2022–2025) emphasize machine learning integration with multi-physics simulations , including groundwater heat pumps , brain tumor modeling , and neuromuscular EMG prediction . She actively promotes open-source software sustainability and collaborative research infrastructure at the University of Stuttgart.

• Causal Machine Learning
• Mechanistic Machine Learning
• Dynamical Systems
• +۶ مورد دیگر

Niki Kilbertus is a Professor in the Department of Informatics at the Technical University of Munich and a group leader at Helmholtz AI (Helmholtz Munich). They are also affiliated with MCML, the Konrad Zuse School relAI, and the Munich Unit of ELLIS. Since 2024, they have been a member of the Junge Akademie and received the Leopoldina Prize for Young Scientists. In 2025, they were awarded an ERC Starting Grant and achieved tenure at TUM. Professor Kilbertus's research focuses on causal machine learning, mechanistic ML, dynamical systems, and AI for science. Their work spans theoretical foundations of causal inference and practical applications across scientific domains. They have made significant contributions to causal effect estimation, causal discovery in stochastic processes, learning differential equations, and fair machine learning. Their research often bridges computer science with physics, biology, and climate science, demonstrating the interdisciplinary nature of their work. Professor Kilbertus has published extensively in top machine learning venues including NeurIPS, ICML, and ICLR, with numerous publications in 2024-2025. Their recent work shows a strong trend toward causal discovery in continuous-time systems, intervention modeling, and physics-informed machine learning applications. Scientific Awards: Leopoldina Prize for Young Scientists (2024) ERC Starting Grant (2025) Professor Kilbertus actively supervises multiple PhD students and collaborates with researchers across institutions including Max Planck Institutes and Helmholtz centers. They serve as an Action Editor for TMLR and regularly review for major ML conferences. The research group is well-funded through the ERC grant and institutional support from TUM and Helmholtz AI, enabling active recruitment of new PhD students and postdocs. Based at Technical University of Munich and Helmholtz AI, Professor Kilbertus's team works at the intersection of theoretical machine learning and scientific applications, with particular strengths in causal reasoning for complex dynamical systems.