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Michael WolfView profile

Professor

• Quantum Information Theory
• Quantum Many-Body Physics
• Mathematical Physics
• +3 more

Prof. Dr. Michael Wolf holds the Chair of Mathematical Physics at the Technical University of Munich (TUM), within the TUM School of Computation, Information and Technology and the Department of Mathematics. His research focuses on quantum information theory, quantum many-body physics, and mathematical physics, with significant contributions to undecidability problems in quantum systems and quantum tomography. Wolf studied physics at LMU Munich and Heidelberg University, completing his first degree in experimental physics (1999) followed by a doctoral degree in mathematical physics at TU Braunschweig (2003). He subsequently worked at the Max Planck Institute of Quantum Optics before holding positions at the Niels Bohr Institute in Copenhagen, where he served as Ole Roemer professor. His research spans quantum information theory with emphasis on quantum tomography, entanglement properties, spectral gap problems, and undecidability in quantum systems. Recent publications demonstrate sustained contributions to quantum circuit complexity, quantum channel discrimination, and fundamental limitations in tensor network purifications, reflecting his leadership in connecting mathematical rigor with quantum information challenges. Notable awards include the German Krupp Foundation grant (2011), Young Elite Researcher’s Award from the Danish Council (2008), Ole Roemer professorial fellowship (2007), and the QIPC European Young Investigator Award (2007). Grant from German Krupp Foundation (2011) Young Elite Researcher’s Award (2008) Ole Roemer professorial fellowship (2007) QIPC European Young Investigator Award (2007) Wolf leads the Quantum Information Theory research group at TUM, collaborating with institutions including the Munich Center for Quantum Science and Technology, Munich Quantum Center, and International Max Planck Research School. His current projects include CRC TRR 352: Mathematics of Many-Body Quantum Systems, building on previous collaborations with EU projects (COQUIT, QUEVADIS), Spanish Ministry of Science, and the John Templeton Foundation. Based at Boltzmannstr. 3, Garching, his group maintains active research in quantum information theory with ongoing supervision of advanced projects including Lieb-Robinson bounds, universal quantum gate sets, and entanglement-breaking evolutions.

Martin SiebenbornView profile

Professor

• Shape Optimization
• Computational Mathematics
• Fluid Dynamics
• +3 more

Martin Siebenborn is a Junior Professor (W1) for Optimization and Approximation at the Department of Mathematics, University of Hamburg. His research focuses on shape optimization with applications in fluid dynamics, structural mechanics, and partial differential equations (PDEs), emphasizing scalable high-performance algorithms and multigrid methods. Education: PhD in Mathematics (2014, Universität Trier), Diploma in Mathematics (2010, Universität Trier). His research integrates algorithmic scalability for PDE-constrained optimization, leveraging multigrid preconditioners and nonlinear extension operators. Recent projects include simulation-based design optimization under uncertainties and scalable shape optimization for fluid dynamics. Onyshkevych, Pinzon Escobar, and Wyschka are part of his research team. He has developed open-source software tools like MinFEM, MGSS, and MultigridShapeOpt for teaching and high-dimensional data analysis.

• Numerical Analysis
• Sparse Grids
• Machine Learning
• +3 more

Prof. Dr. Michael Griebel is a leading academic at the Institute for Numerical Simulation (INS) at the University of Bonn , Germany. His work focuses on numerical methods for partial differential equations (PDEs), sparse grid techniques, and applications in computational science, machine learning, and materials modeling. Research Interests : Numerical analysis, sparse grids, high-dimensional approximation, tensor product methods, stochastic processes, and multiscale simulations. Collaborations : Co-author of over 50 publications with researchers in computational mathematics, fluid dynamics, and quantum chemistry. Prominent research trends include advancements in sparse grid methods for uncertainty quantification, kernel-based regression, and efficient solvers for ill-posed problems. His work on space-filling curves and domain decomposition enables scalable parallel algorithms for complex simulations. Scientific Contributions : Editor of 10+ volumes in Lecture Notes in Computational Science and Engineering . Co-developer of the EXAHD exascale sparse grid framework for plasma physics simulations. Foundational work on tensor product spaces and their applications to electronic structure theory. Innovations in adaptive wavelet solvers and parallel multigrid methods. Software and Projects : Key developer of sparse grid solvers and adaptive algorithms. His INS Preprint Series archives over 200 technical reports, reflecting his group’s impact on computational methods.

• Systems and Control Theory
• Differential-Algebraic Equations
• Data-Driven Modelling
• +3 more

Jan Heiland is an Assistant Professor at Otto von Guericke University Magdeburg and a Researcher at the Max Planck Institute for Dynamics of Complex Technical Systems. His work focuses on computational methods in systems and control theory, particularly for differential-algebraic equations (DAEs) and data-driven modeling of fluid dynamics. Research Interests: Systems and Control Theory, Differential-Algebraic Equations, Data-Driven Modelling, Flow Control, Fluid Dynamics, Reduced-Order Modeling Publication Trends: Recent work emphasizes low-dimensional approximations of nonlinear systems using machine learning techniques (e.g., polytopic autoencoders, convolutional autoencoders) and robust control methods for Navier-Stokes equations. Key themes include model reduction, uncertainty quantification, and computational approaches to large-scale dynamical systems. Teaching: He teaches numerical methods for partial differential equations and scientific computing at TU Ilmenau and OVGU Magdeburg.

Markus WeimarView profile

Lecturer

• Regularity Theory for Operator Equations
• Besov Regularity
• Nonlinear Partial Differential Equations
• +12 more

Markus Weimar is a Lecturer at the Chair of Mathematics IX (Scientific Computing) at the University of Würzburg. He previously held academic positions at Ruhr University Bochum (Interim Professor, Assistant Professor), University of Siegen (Senior Lecturer, Interim Professor), and Philipps-University Marburg (Postdoctoral Researcher). Education: Dipl.-Math. (2009) and Ph.D. (2013) from Friedrich-Schiller-University Jena. Honors: Summa cum laude Ph.D. thesis, German National Academic Foundation support, and election to the Early Career Researchers Board at RUB. Research Interests include regularity theory for operator equations, partial differential equations, boundary integral equations, function spaces, numerical analysis, adaptive methods, and high-dimensional problems. His work focuses on overcoming the curse of dimensionality through techniques like wavelets, quasi-Monte Carlo methods, and discrepancy theory. Recent Publications highlight breakthroughs in Besov regularity for p-Poisson equations, adaptive wavelet boundary element methods, and tractability of high-dimensional permutation-invariant integration. His 2024 papers on Sobolev spaces with mixed weights and oscillation analysis in Morrey-type spaces indicate ongoing innovation in theoretical numerical analysis. Scientific Leadership: Senior member of the GAMM Junior Research Group at RUB, active in organizing workshops, and participant in international conferences like FoCM and MCQMC.

• Database Systems
• Embedded Analytics
• Query Processing
• +5 more

Hannes Mühleisen is a prominent database systems researcher affiliated with Centrum Wiskunde & Informatica (CWI) in Amsterdam, with a PhD from Free University of Berlin (2012) in distributed query processing. His research spans database architecture, embedded analytics, and query optimization, with significant contributions to the DuckDB open-source analytical database project. His primary research interests focus on embedded analytical database systems , query processing optimization , columnar storage , and database reliability . Mühleisen's work bridges theoretical database concepts with practical implementations, particularly in the context of analytical workloads. His research demonstrates how database systems can be optimized for modern hardware architectures including WebAssembly and tensor processing units. Mühleisen has published extensively in top database venues including VLDB, SIGMOD, ICDE, and SSDBM, with a consistent publication record from 2009 through 2025. His recent work shows increasing focus on robustness, reliability, and practical deployment of database technology in diverse environments from the web browser to scientific computing contexts. His collaborations reveal strong ties to the CWI database research group, with frequent co-authorship with Peter Boncz, Martin Kersten, and Mark Raasveldt. Mühleisen's work on DuckDB has made significant impact in the data science community as an embedded analytical database solution.

Markus HeglandView profile

Professor

• Computational Mathematics
• High-Dimensional Problems
• Ill-Posed Problems
• +4 more

Markus Hegland is a Professor and Head of the Centre for Mathematics and its Applications (CMA) at the Australian National University (ANU). He holds a PhD from ETH Zurich (1988) and has been affiliated with ANU since 1992, focusing on High-Performance Computing (HPC) and numerical analysis. As a Hans Fischer Senior Fellow at TUM-IAS, his research emphasizes high-dimensional problems, ill-posed systems, and data mining applications. His work bridges computational mathematics with practical domains like systems biology and spectral enhancement. Research interests include sparse grid techniques, regularization methods, and algorithm development for HPC. Notable contributions include the OPTICOM method for stable sparse grid solutions and convergence theory for variable Hilbert scales regularization. He has led projects on fault-tolerant HPC algorithms and collaborated with Fujitsu on HPC applications. Publications span numerical analysis, bioinformatics, and computational physics. His work on the chemical master equation and gyrokinetics showcases interdisciplinary impact. Currently, he explores resilient grid-based solvers and machine learning integration with HPC frameworks. No awards are explicitly listed, but his senior fellowship underscores recognition in his field. Grants and collaborations include ARC-funded research in bioinformatics and HPC resilience. His work on digital twins and algorithm optimization reflects broader interests in advanced computational modeling. He is actively involved in teaching and supervising in computational mathematics and data science at ANU.

• Machine Learning
• Signal Processing
• Computer Vision
• +4 more

Yuan Liao is an active researcher with a focus on interdisciplinary areas spanning computer science, electrical engineering, and applied mathematics. His work emphasizes innovative solutions in signal processing, machine learning, robotics, and human mobility studies. Notably, he has contributed to advancements in wearable biomedical devices, remote sensing image analysis, and next-generation wireless communication systems like 6G. Collaborations with institutions like Academia Sinica and co-authors such as Vasilis Friderikos highlight his global academic network. Liao's research often bridges theoretical frameworks with practical applications, addressing challenges in healthcare, transportation, and urban planning. Key contributions include developing novel neural network architectures for tasks like image inpainting and emotion recognition from EEG signals. His work on robotic aerial base stations for mmWave backhauling demonstrates expertise in cutting-edge wireless systems. While specific institutional affiliations are not explicitly stated in the provided data, his prolific publication record across top-tier journals and conferences indicates an established academic role, likely as a researcher or faculty member in a technical discipline.

Ullrich KötheView profile

Associate Professor

• Machine Learning
• Neural Networks
• Explainable AI
• +6 more

Prof. Dr. Ullrich Köthe is an Associate Professor and group leader in the Visual Learning Lab Heidelberg at the University of Heidelberg . He focuses on Explainable Machine Learning , leveraging Invertible Neural Networks to enhance transparency and utility in image analysis and medical applications . He also maintains the widely used VIGRA image analysis library. Education: PhD in Informatics, University of Hamburg, 2000 Habilitation in Informatics, University of Hamburg, 2008 His research interests center around machine learning , image analysis , and scientific computing , particularly the development of robust algorithms for medical imaging , computer vision , and life sciences . His work on invertible neural networks and parameter-free segmentation has led to significant advancements in the field. Recent publications highlight his contributions to Bayesian inference , neural network interpretability , and stochastic modeling , with applications ranging from disease outbreak dynamics to connectomics . Key trends include generative models , parameter-free segmentation , and likelihood-free inference . Scientific Awards: DAGM 2003 Main Prize DAGM Best Paper Award 2008 He has supervised numerous Master and Bachelor theses in machine learning and image analysis , with teaching roles in Advanced Machine Learning and Explainable AI . His collaborative research grants include funding from HARMAN International (2024). He leads the Explainable Machine Learning subgroup and has contributed to open-source software projects like ilastik and VIGRA , which are critical tools in bioimage analysis .

Guido KanschatView profile

Professor

• Numerics
• Scientific Computing
• Finite Element Methods
• +7 more

Professor Dr. Guido Kanschat is affiliated with Ruprecht-Karls-Universität Heidelberg at the Interdisciplinary Center for Scientific Computing (IWR), where he holds the Chair for Mathematical Methods of Simulation. His research focuses on numerics, scientific computing, and finite element methods with applications in radiation transfer, coupled flows, and multigrid solvers. His recent publications emphasize hybrid discretizations, GPU-accelerated multigrid methods, and discontinuous Galerkin techniques for Stokes equations and biharmonic problems. He is a co-founder of the open-source deal.II library and received the Wilkinson Prize for Numerical Software in 2007. Scientific Awards: Wilkinson Prize for Numerical Software (2007)

• Operator Algebras
• Functional Analysis
• C*-Algebras
• +3 more

Christopher Schafhauser is an Associate Professor at the University of Nebraska-Lincoln, specializing in operator algebras and functional analysis. His research focuses on C*-algebras, nuclearity, classification problems, and their applications to non-commutative geometry and mathematical physics. He collaborates extensively with leading mathematicians in the field, contributing to foundational work on nuclear dimension, E-theory, and quantum self-testing. His notable contributions include pioneering studies on nuclear C*-algebras, tracially complete structures, and KK-rigidity. Schafhauser's work bridges pure mathematics with applications in quantum information theory and group dynamics. He has published in top journals such as the Annals of Mathematics, Duke Mathematical Journal, and Communications in Mathematical Physics. No scientific awards are explicitly mentioned in the provided text. His research spans over 20 peer-reviewed articles, with recent emphasis on classification programs, topological graph algebras, and boundary actions of groups.

• Computational Mechanics
• Continuum Mechanics
• Constitutive Modeling
• +12 more

Prof. Dr.-Ing. habil. Jörg Schröder is a full Professor of Mechanics at the University of Duisburg-Essen, Faculty of Engineering, Department of Civil Engineering, and leads the Institute of Mechanics. He has held significant leadership roles, including Vice-Rector for Research and Knowledge Transfer, and is currently Vice-President (2023–2025) and former President (2020–2022) of the International Association of Applied Mathematics and Mechanics (GAMM). He is a member of acatech and the Academy of Sciences and Literature, Mainz. PhD and Habilitation, Universität Stuttgart Professor since 2001, University of Duisburg-Essen Spokesperson, DFG Priority Programme 1748 and Research Unit 1509 Editor-in-Chief, Archive of Applied Mechanics His research centers on computational and continuum mechanics, with a focus on constitutive modeling, finite element methods, and multiscale simulations of materials such as dual-phase steels, high-performance concrete, and magneto-mechanical systems. He employs advanced numerical techniques including mixed and hybrid finite elements, phase-field modeling, and least-squares formulations. His work spans theoretical development and practical applications in manufacturing, civil engineering, and material science. The recent publications demonstrate a strong emphasis on multi-physics problems, including thermo-elastoplastic analysis in laser welding, micromagnetic simulations, sea ice dynamics, and fracture modeling in fiber-reinforced concrete. There is a clear trend toward high-fidelity, multi-scale simulations integrating microstructural details with macroscopic behavior, often using phase-field and reduced-order modeling approaches. Notable scientific recognitions include: Member of the Senate of the German Science Foundation (DFG) Selection Committee, Alexander von Humboldt Foundation Member of acatech and the Academy of Sciences and Literature, Mainz Leadership roles in GAMM Prof. Schröder leads major research initiatives funded by the DFG, serves on editorial boards of leading journals, and collaborates extensively with national and international researchers. He advises numerous doctoral candidates and postdoctoral researchers, though specific student names are not listed in the source text. His team conducts research in areas such as computational inelasticity, multiscale modeling, and simulation of coupled physical phenomena.

Steffen WinterView profile

Lecturer

• Fractal Geometry
• Geometric Measure Theory
• Stochastic Geometry
• +1 more

Steffen Winter is a Lecturer (PD Dr.) at the Institute of Stochastics, Karlsruhe Institute of Technology (KIT). His research specializes in Fractal Geometry, Geometric Measure Theory, Stochastic Geometry, and Dynamical Systems. He leads the DFG-funded project Scaling of curvature measures and the modified Weyl-Berry conjecture and serves as Principal Investigator for project 12 ( Morphometric Roughness of Nanostructured Surfaces ) within the DFG Priority Programme 2265 (Random Geometric Systems). Winter's work explores the mathematical foundations of fractals, stochastic processes, and geometric measurements. Key themes include Minkowski content, curvature measures, self-similar sets, percolation models, and applications to materials science and geoscience. Recent publications emphasize fractal dimensionality, surface roughness quantification, and stochastic convergence in complex systems. He teaches advanced courses including Stochastic Geometry , Fractal Geometry , and Markov Chains , and mentors students through seminars and proseminars. No awards or research grants besides DFG projects are documented.

• Quantum Information and Computation
• Quantum Many-Body Systems
• Tensor Networks
• +7 more

Professor Norbert Schuch is a full Professor of Physics and Mathematics at the University of Vienna, where he leads the Research Group "Quantum Information and Quantum Many-Body Physics" at both the Faculty of Physics and Faculty of Mathematics. He joined the University of Vienna in October 2020 after serving as a tenured Research Group Leader at the Max-Planck-Institute of Quantum Optics in Garching, Germany and as a Lecturer at the Technical University Munich. Prior to that, he held a Tenure-Track-Professor position at the Institute for Quantum Information at RWTH Aachen University. Professor Schuch's research focuses at the intersection of Quantum Information and Computation with the Physics of Complex Quantum Many-Body Systems. His work combines mathematical, physical, and computational approaches to understand quantum correlations in many-body systems. Key research areas include tensor networks (such as Matrix Product States and Projected Entangled Pair States), topological order, entanglement theory, quantum algorithms, and quantum complexity theory. His interdisciplinary approach integrates methods from physics, mathematics, and theoretical computer science to address fundamental questions about quantum systems. His recent publications show a continued focus on tensor network theory and applications, with particular emphasis on topological phases, entanglement structure, quantum algorithms, and computational aspects of quantum many-body systems. His work spans mathematical foundations, physical applications, and computational implementations, demonstrating the cross-disciplinary nature of his research program. As an educator, Professor Schuch teaches courses on Quantum Information, Quantum Computing, and Quantum Algorithms, as well as specialized topics like Entanglement in Quantum Many-Body Systems. He actively supervises PhD students, postdocs, and master's students in his research group, which maintains strong connections with the international quantum information community.

Bin GaoView profile

Associate Professor

• Riemannian Optimization
• Tensor Computation
• Parallel/Distributed Computing
• +5 more

Bin Gao is an Associate Professor at the Academy of Mathematics and Systems Science (AMSS), Chinese Academy of Sciences. He holds a Ph.D. in Applied Mathematics (2019, University of Chinese Academy of Sciences) and a B.Sc. in Mathematics (2014, Sichuan University). His postdoctoral experience includes positions at UCLouvain (2019-2021) and the University of Münster (2021-2022). Research Interests: Riemannian optimization, tensor computation, parallel/distributed algorithms for orthogonality constraints, machine learning applications. Key Contributions: Development of retraction-free methods on Stiefel manifolds, preconditioned Riemannian algorithms, and geometric frameworks for symplectic eigenvalue problems. Article Trends: Recent work focuses on overcoming the curse of dimensionality via manifold-based optimization, including distributed algorithms for Stiefel manifolds, graph-regularized tensor completion, and second-order methods for symplectic structures. Keywords span numerical analysis, quantum information, and machine learning. Scientific Awards: 2021 Zhong Jiaqing Mathematics Award 2018 Best Student Paper Award (CSIAM) 2018 CAS Special President Scholarship 2017 National Scholarship for Doctoral Students (China) 2016 Honor Student Award (International Workshop on Modern Optimization and Application) Advising & Collaborations: Collaborates with researchers from UCLouvain, University of Münster, and AMSS. Mentors students in Riemannian optimization and tensor computation. Leads the popman research group.