Find Academic Supervisors

• Algebraic Geometry
• Number Theory
• Representation Theory
• +3 more

Andreas Bode is a Research Fellow at the University of Wuppertal, where he is part of the group led by Sascha Orlik in the Department of Mathematics. His research focuses on D-modules on rigid analytic spaces, integrating geometric representation theory and nonarchimedean geometry. His research interests span several areas of algebraic geometry and number theory, including the study of p-adic differential operators, nonarchimedean geometry, and representation theory. He explores the interplay between D-modules and rigid analytic spaces, contributing to the understanding of holonomicity, Auslander regularity, and the application of almost mathematics in p-adic contexts. His work bridges geometric and analytic approaches to problems in nonarchimedean settings. Bode's recent publications include advancements in the theory of Auslander regularity for p-adic structures, contributions to the holonomicity of D-cap-modules on rigid analytic spaces, and explorations into locally analytic representations of p-adic groups. His work often intersects with homological algebra and functional analysis, reflecting a deep engagement with both theoretical and methodological challenges in nonarchimedean geometry. He has not been mentioned as having received any scientific awards in the provided texts. Bode's current role as a postdoctoral researcher does not involve formal academic advising of students. No grants are specifically mentioned in the provided information. He is affiliated with the Algebra and Number Theory group at the University of Wuppertal, collaborating with colleagues such as Sascha Orlik and contributing to the broader research community in nonarchimedean geometry.

Jan von DelftView profile

Professor

• Correlated electron systems
• Quantum impurity models
• Quantum criticality
• +7 more

Jan von Delft is a Professor (chair) at Ludwig-Maximilians-University (LMU) Munich, working in the Faculty of Physics within the Chair of Theoretical Solid State Physics. His research group consists of postdocs, PhD students, and master's students working on various aspects of strongly correlated electron systems, with physical space located at Theresienstr. 37 (Room A420) in Munich. von Delft's research focuses on correlated electron and spin systems, with particular interest in dynamical and transport properties, quantum impurity models, Hund metals, unconventional superconductors, quantum magnets, and quantum criticality. His methodological expertise includes many-body field theory, parquet formalism (FRG), DMFT, and tensor networks (NRG, DMRG, PEPS, XTRG, etc.). His work bridges theoretical concepts with computational approaches to understand complex quantum phenomena in condensed matter systems. He has developed a distinctive emphasis on real-frequency calculations and numerical methods for studying quantum critical phenomena. Analysis of von Delft's recent publications reveals a strong focus on developing and applying advanced computational methods to study strongly correlated electron systems. His group has made significant contributions to numerical renormalization group techniques, tensor network methods, and the parquet formalism for calculating real-frequency correlation functions. His research shows increasing sophistication in handling quantum criticality, particularly in heavy-fermion systems, and exploring unconventional superconductivity mechanisms. Notably, his group has developed specialized computational libraries like KeldyshQFT to make these advanced methods more accessible to the broader physics community. von Delft actively mentors a substantial research group consisting of one postdoc (Markus Scheb), eleven PhD students (Anxiang Ge, Sasha Kovalska, Mathias Pelz, Marc Ritter, Nepomuk Ritz, Changkai Zhang, Markus Frankenbacher, Felipe Picoli, Simone Fodera, Ming Huang), and two master's students (Ester Pages, Gianluca Grosso). His detailed Style Guide for scientific communication demonstrates his commitment to high-quality research presentation. The group appears well-funded with ongoing research activities spanning theoretical development, computational implementation, and physical interpretation of complex quantum phenomena.

Frank PollmannView profile

Professor

• Condensed Matter Theory
• Quantum Information Theory
• Correlated Electrons
• +6 more

Prof. Dr. Frank Pollmann is a Full Professor (W3) at the Department of Physics PH-I, Technical University of Munich (TUM), leading the Chair of Theoretical Solid-State Physics since 2022. His research focuses on condensed matter theory and quantum information concepts , particularly in systems of correlated electrons and quantum many-body dynamics . PhD: Max Planck Institute for the Physics of Complex Systems / TU Ilmenau (2006) Postdoc: UC Berkeley (2008-2010) Group Leader: MPIPKS Dresden (2011-2016) Associate Professor: TUM (2017-2022) His work spans topological phases , frustrated spin systems , and non-equilibrium quantum dynamics , utilizing tensor network methods and quantum information theory to study phenomena like many-body localization and Hilbert space fragmentation . His publications demonstrate trends in quantum scar states , Kardar-Parisi-Zhang hydrodynamics , and quantum transport anomalies . Scientific Awards : ERC Consolidator Grant (2017) Walter Schottky Prize (2015) Otto-Hahn Medal (2007) He teaches courses including Advanced Methods in Quantum Many-Body Theory , Solid State Theory , and Topology in Condensed Matter , while leading the Pollmann Group under the TUM School of Natural Sciences.

• Scientific Machine Learning
• Numerical Linear Algebra
• Numerical Multilinear Algebra
• +7 more

Peter Benner is a Professor and Director at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, where he leads the Computational Methods in Systems and Control Theory group. He also holds an Honorarprofessor position for Mathematics at Otto-von-Guericke Universität Magdeburg since 2011. Benner has previously served as Managing Director of the Max Planck Institute during multiple periods (2013-2014, 2021-2022, and 2025-2026), demonstrating his leadership in the field. Benner's research focuses on Scientific Machine Learning, Numerical Linear and Multilinear Algebra, Model Order Reduction and Reduced-order Modeling, Numerical Methods in Systems and Control Theory, PDE Constrained Optimization, High-performance and Power-aware Computing, and Mathematical Software development. His work bridges theoretical mathematics with practical engineering applications, particularly addressing challenges in large-scale dynamical systems. Analysis of his recent publications reveals a strong emphasis on developing efficient computational methods for complex systems. Benner has pioneered approaches combining model order reduction with tensor methods to tackle high-dimensional problems in uncertainty quantification and PDE-constrained optimization. His work shows a consistent trend toward integrating data-driven techniques with traditional model-based approaches, particularly for nonlinear and parametric systems. Throughout his career, Benner has actively mentored students and collaborated with researchers worldwide, delivering numerous invited talks at prestigious institutions and conferences across Europe, North America, and Asia. His research has received significant funding, supporting the development of mathematical software and computational methods for industrial applications. Benner leads the Computational Methods in Systems and Control Theory department at the Max Planck Institute, which focuses on developing and implementing advanced numerical methods for large-scale dynamical systems. The group maintains strong connections with both theoretical mathematics and practical engineering applications, particularly in fluid dynamics, energy systems, and control theory.

• Computer Architecture
• Non-Volatile Memory Systems
• Deep Learning Systems
• +3 more

Jishen Zhao is an Assistant Professor in the Department of Computer Science and Engineering at the University of California, San Diego (Jacobs School of Engineering). His research focuses on computer architecture, non-volatile memory systems, and deep learning acceleration. Dr. Zhao has published extensively in top venues including ISCA, MICRO, ASPLOS, and IEEE Transactions. He collaborates with researchers at UCSD and beyond to advance systems for emerging applications in AI and autonomous vehicles. Dr. Zhao's primary research areas include persistent memory systems, hardware/software co-design for deep learning, and safety-critical computing. He develops techniques for crash consistency, memory disaggregation, and efficient neural network deployment. His work on autonomous vehicles addresses scenario generation and perception-aware system design. Recent projects explore LLM applications for software engineering and hardware verification. Analysis of Dr. Zhao's 2024-2025 publications reveals a strong shift toward AI-integrated systems research. He applies large language models to tasks like RTL verification and software issue localization while continuing to innovate in memory systems for serverless computing. There is growing emphasis on safety-critical systems for autonomous vehicles and energy-efficient neural network training using novel hardware architectures. Information about Dr. Zhao's scientific awards, advising activities, grants, and laboratory facilities was not available in the provided documentation.

Markus HausmannView profile

Professor

• Topology
• Equivariant Homotopy Theory
• Algebraic Topology
• +3 more

Markus Hausmann is a Professor of Topology at the University of Bonn since 2023. His research focuses on equivariant homotopy theory , with groundbreaking work on bordism theory and symmetry of spaces, including a 2022 publication in the Annals of Mathematics . He received the prestigious Minkowski Medal 2025 from the German Mathematical Society (DMV) for his outstanding contributions to mathematics. Current affiliation: University of Bonn (2023–present) Former roles: University Lecturer at Stockholm University (2021–2023), Postdoc at University of Copenhagen and Bonn Education: Mathematics studies at University of Bonn with a semester abroad at MIT His ERC Starting Grant 'BorSym' explores bordism of symmetries using algebraic methods, funded for five years. His work intersects topology , algebraic structures , and equivariant cohomology , with recent publications addressing symmetric spectra, global group laws, and subgroup lattices. The Minkowski Medal recognizes his international acclaim as a young mathematician. Scientific Awards : Minkowski Medal 2025 (DMV) Markus Hausmann's research bridges equivariant homotopy theory with applications to derived orbifolds and global symmetries , advancing foundational understanding in these areas.

Markus BachmayrView profile

Professor

• Numerical Analysis
• Partial Differential Equations
• High-dimensional Problems
• +4 more

Prof. Dr. Markus Bachmayr is a full professor at the Institute for Geometry and Practical Mathematics, RWTH Aachen University, holding the chair for Applied Mathematics. His research focuses on nonlinear approximation, high-dimensional partial differential equations (PDEs), uncertainty quantification, and numerical methods in quantum chemistry. He leads the ERC Consolidator Grant project Computational Complexity of Highly Nonlinear Approximations (COCOA) and contributes to CRC 1481 Sparsity and Singular Structures, and RTG 2326 Energy, Entropy, and Dissipative Dynamics. His recent work emphasizes adaptive low-rank and sparse approximation techniques for parametric and stochastic PDEs, including applications in radiative transfer and poroviscoelastic flow modeling. He serves as Editor-in-Chief of Foundations of Computational Mathematics and Associate Editor for multiple journals. Scientific Awards: John Todd Award 2013 Borchers Plakette 2014 Erwin Wenzl Preis 2007 He has taught courses such as Numerische Analysis I/II, Numerische Mathematik für Elektrotechniker, and seminars on numerical methods and approximation theory.

• Geomechanics
• Geophysics
• Numerical Modeling
• +5 more

Dr. Moritz Ziegler is a geomechanics researcher affiliated with the Technical University of Munich (TUM) and the Assistant Professorship of Geothermal Technologies . He previously worked at GFZ Potsdam (2017–2023) and earned his PhD in Geophysics from the University of Potsdam (2014–2017). His research focuses on geomechanical numerical modeling , uncertainty quantification , and 3D stress field analysis , with applications to geothermal energy, seismic hazard, and rock mechanics. Education: Bachelor of Science in Geosciences (2008–2011), Freie Universität Berlin Master of Science in Geophysics (2011–2014), University of Potsdam PhD in Geophysics (2014–2017), University of Potsdam & GFZ Potsdam His work integrates advanced computational methods ( Altair Hypermesh , Dassault Systèmes Abaqus , Python , Matlab ) to address complex geomechanical problems. Recent publications analyze stress gradients in the North Alpine Foreland Basin, fault-stress interactions, and physics-based machine learning in geomechanics. He has contributed to software development (DOuGLAS v1.0) and calibration tools (FAST Calibration v2.4). Scientific awards or honors are not explicitly mentioned in the provided text. However, his research has been published in leading journals such as Geophysical Journal International , Solid Earth , and Pure and Applied Geophysics .

Joachim WeickertView profile

Professor

• Image Processing
• Computer Vision
• Mathematical Modeling
• +4 more

Joachim Weickert is a Professor of Mathematics and Computer Science at Saarland University where he heads the Mathematical Image Analysis Group since 2001. He received his diploma and Ph.D. in mathematics from the University of Kaiserslautern (1991, 1996), and a habilitation degree in computer science from the University of Mannheim (2001). Prior to his current position, he worked as a research assistant at the University of Kaiserslautern, as a post-doctoral researcher at the universities of Utrecht and Copenhagen, and as an assistant professor at the University of Mannheim. His research focuses on image processing, computer vision, and scientific computing, with special emphasis on techniques based on partial differential equations, variational principles, wavelets, morphological and nonlocal methods, as well as neuroexplicit approaches. He has developed mathematical models and efficient numerical algorithms for image restoration, enhancement, segmentation, compression, optic flow computation, stereo reconstruction, shape from shading, and signal processing methods for tensor fields. These ideas have been successfully applied in industry, biomedical image analysis, and other fields. Analysis of his recent publications reveals a strong trend toward combining traditional PDE-based methods with modern deep learning approaches, particularly in the areas of image inpainting and compression. His work increasingly explores the connections between numerical algorithms for partial differential equations and neural network architectures, demonstrating how mathematical foundations can inform cutting-edge AI techniques while maintaining strong theoretical guarantees. Gottfried Wilhelm Leibniz Prize (2010), considered the most important research award in Germany ERC Advanced Grant (2017) for "Inpainting-based Compression of Visual Data" Elected member of Academia Europaea - The Academy of Europe Jan Koenderink Prize for Fundamental Contributions in Computer Vision (2014) Multiple DAGM Prizes and Best Paper Awards throughout his career AAIA Fellow (2021) and Highly Ranked Scholar (2024) distinctions Professor Weickert has supervised over 250 bachelor's and master's theses and initiated the Master Programme in Visual Computing at Saarland University, the first of its kind in Germany taught in English. He has established numerous interdisciplinary collaborations with colleagues from medicine, bioinformatics, pharmacy, physics, mechatronics, and mechanical engineering. As Principal Investigator for Visual Computing within the Multimodal Computing and Interaction Cluster of Excellence, and former dean of the Faculty of Mathematics and Computer Science (2008-2010), he has played a significant leadership role in advancing visual computing research and education. He heads the Mathematical Image Analysis Group, which has been at the forefront of developing mathematical methods for image analysis. The group maintains strong connections with both theoretical mathematics and practical applications, bridging the gap between fundamental research and real-world implementation across various domains including medical imaging, industrial inspection, and multimedia processing.

Hans-Bert RademacherView profile

Professor

• Differential Geometry
• Pseudo-Riemannian Geometry
• Finsler Geometry
• +7 more

Hans-Bert Rademacher is a Professor of Differential Geometry at the University of Leipzig's Faculty of Mathematics and Computer Science, where he has held the chair since 1995. He completed his Habilitation in 1991 and earned his PhD (Dr.rer.nat., summa cum laude) in 1986 from Universität Bonn, following a Diploma in Mathematics (1983) from the same institution. Research Interests: His work focuses on differential geometry, including pseudo-Riemannian geometry, Finsler geometry, conformal geometry, Dirac operators and twistor spinors, Morse theory and closed geodesics, topology of free loop spaces, and discrete curve shortening. His research bridges geometric analysis and topology, particularly in the study of geodesic systems and curvature properties. Publication Trends: Recent articles center on closed geodesics in various geometric settings (spheres, Finsler manifolds, 3-manifolds), solitons in geometric flows, homology of loop spaces, and conformal geometry. His work frequently applies Morse theory to solve problems in global analysis and topology. Awards and Honors: University Medal (2021) Full Member, Saxon Academy of Sciences and Humanities (since 2010) Heisenberg Fellowship (1992-1995) Felix Hausdorff Memorial Prize (1985) National Mathematics Competition Winner (1978) Academic Service: He has supervised 12 PhD students and served as Dean (2011-2014) and Vice-Dean (2005-2008) of his faculty. He coordinated the Research Training Group "Analysis, Geometry and their interaction with the sciences" (2000-2010) and serves on editorial boards for several mathematics journals.

Jan PetersView profile

Professor

• Robot Learning
• Reinforcement Learning
• Imitation Learning
• +5 more

Jan Peters is a full professor (W3) at the Computer Science Department of Technische Universität Darmstadt and serves as the department head of the Systems AI for Robot Learning (SAIROL) at the German Research Center for AI (DFKI) . He is also a founding faculty member of the Hessian Centre for Artificial Intelligence . Peters holds a Ph.D. in Computer Science from the University of Southern California (2007) and dual master’s degrees in Computer Science and Electrical Engineering from USC and TU Munich respectively. Research Themes : Robot Learning, Reinforcement Learning, Imitation Learning, Tactile Sensing, Human-Robot Interaction, and Safe AI. Recent Article Trends : Focus on deep reinforcement learning (Iterated Q-Networks, Adaptive Q-Networks), safe robot foundation models , tactile-enhanced imitation learning , and physics-informed machine learning . Scientific Recognition : Recipient of the Dick Volz Best PhD Thesis Award , ERC Starting Grant , IEEE Fellow , and Amazon Research Award . Leadership : Founder of the IEEE RAS Technical Committee on Robot Learning and editor for journals including Autonomous Robots and IEEE Transactions on Robotics .

Örs LegezaView profile

Visiting Professor

• Quantum Physics
• Condensed Matter Physics
• Computational Physics
• +5 more

Örs Legeza is a physicist and scientific advisor at the Wigner Research Centre for Physics of the Hungarian Academy of Sciences in Budapest, leading the Strongly Correlated Systems Research Group. He holds a visiting professorship at Philipps University Marburg, Germany, and has held fellowships at institutions like ETH Zurich and LMU Munich. His research focuses on developing tensor network state (TNS) methods for strongly correlated quantum systems, with applications in condensed matter physics, quantum chemistry, and nuclear structure calculations. Education: PhD from Budapest University of Technology and Economics (1997). He has collaborated with European institutions such as FAU Erlangen-Nuremberg and has been an Alexander von Humboldt awardee. His work bridges quantum information theory and computational mathematics to advance simulations of complex quantum systems. Research interests include quantum phase transitions, magnetic properties in solids, and ultracold atomic systems. His methods push computational boundaries for larger systems, integrating techniques like density matrix renormalization group (DMRG) and matrix product states (MPS). Notable awards include the 2021 Academy Prize and 2018 Humboldt Research Award. Recent articles explore quantum crystal imaging, tensor network algorithms, and nuclear structure calculations. His work emphasizes interdisciplinary approaches to quantum many-body problems.

Jürgen Richter-GebertView profile

Professor

• Geometry
• Visualization
• Computer Science
• +7 more

Professor Jürgen Richter-Gebert is a full professor of Geometry and Visualization at the Technical University of Munich (TUM), working within the TUM School of Computation, Information and Technology. Born in 1963, he has been at TUM since 2001, following positions at ETH Zurich (1997-2001) and TU Berlin (1994-1997). His educational background includes studies at TU Darmstadt (1983-1988) and dual PhDs from TU Darmstadt and KTH Stockholm (1991-1992). Richter-Gebert's research spans combinatorial and computer-oriented geometry, with particular expertise in polytope theory and mathematical visualization software. He develops processes for the automatic generation of geometric problem solutions and is actively involved in raising the public profile of mathematics. Richter-Gebert's publications and research focus on the intersection of mathematics and computer science, with particular emphasis on projective geometry, dynamic geometry, polytope theory, and combinatorial geometry. His work demonstrates how mathematical structures can be made accessible through computerized interactive visualizations. His most notable publications include "Perspectives on Projective Geometry" (2011) and "Geometriekalküle" (2009), along with numerous papers on dynamic geometry systems. Ars Legendi Prize for excellent university teaching (2011) Karl Max von Bauernfeind Medal of the TUM (2010) MedidaPrix - media didactic university prize (2008) EASA - European Academic Software Award (2000) Communicator Preis for science communication (2021) As founder and director of the ix-quadrat mathematics exhibition at the Garching Campus, Richter-Gebert has made significant contributions to mathematics education and outreach. He has developed influential mathematical visualization tools including Cinderella, CindyJS, and iOrnament, which have received multiple awards for educational software excellence. His research group focuses on mathematical foundations, authoring systems, and mathematical visualizations with applications in education and public scenarios.

• Quantum Computing
• Quantum Foundations
• ZX-calculus
• +7 more

John van de Wetering is an Assistant Professor at the Theoretical Computer Science group of the Informatics Institute, University of Amsterdam, working with the QuSoft research center. He co-authored the open-access book Picturing Quantum Software and developed the PyZX quantum compiler. His research spans quantum computation and quantum foundations, focusing on diagrammatic methods like the ZX-calculus and ZH-calculus. Quantum circuit optimization and verification Quantum foundations via algebraic/compositional methods Co-creator of PyZX His recent publications explore multi-qutrit systems, completeness of graphical calculi, and quantum state representations. Supervises students in quantum computing, including Lia Yeh and Sarah Li. Directs the new Master's program in Quantum Computer Science at UvA. Actively contributes to open-source projects and international conferences. Notable collaborations include Aleks Kissinger, Neil J. Ross, and QuSoft researchers. Uses GitHub for DiZX development (qudit extension of PyZX). No explicit scientific awards mentioned.

Daniel KeeserView profile

Research Fellow

• Multimodal Neuroimaging
• Neuromodulation
• Neurophysiology
• +4 more

Dr. Daniel Keeser is a Research Fellow and Research Group Leader at the Department of Psychiatry and Psychotherapy of the University of Munich (LMU) and affiliated with the NeuroImaging Core Unit Munich (NICUM). His work focuses on clinical deep phenotyping and multimodal neuroimaging, integrating advanced MRI, EEG, and non-invasive brain stimulation methods to study severe mental and neurological disorders. Research Interests: Elucidating neurobiological mechanisms of schizophrenia, major depressive disorder, and Alzheimer's disease through multimodal neuroimaging and neuromodulation. His recent publications highlight methodologies like resting-state fMRI, diffusion tensor imaging, and transcranial magnetic stimulation combined with MRI, emphasizing personalized treatment strategies. Collaborative affiliations include the University Hospital of LMU Munich and the Clinical Deep Phenotyping (CDP) Working Group. Affiliations: NeuroImaging Core Unit Munich (NICUM) Department of Psychiatry and Psychotherapy, University of Munich (LMU)