Find Academic Supervisors

Hector GeffnerView profile

Professor

• Artificial Intelligence
• Machine Learning
• Automated Planning
• +5 more

Hector Geffner is an Alexander von Humboldt Professor at RWTH Aachen University, leading the Chair of Machine Learning and Reasoning. He specializes in automated planning, machine learning, and reasoning, with a focus on representation learning for acting and planning. His work bridges symbolic and model-based AI, emphasizing general policies and subgoal decomposition. Education & Background : PhD from UCLA (1989), prior roles at IBM Watson Research Center and Universidad Simón Bolívar. Former ICREA researcher and professor at Universitat Pompeu Fabra (2001–2022). Research Interests : Classical and probabilistic planning, reinforcement learning, knowledge representation, and applications in robotics. His ERC-funded RLeap project explores learning generalized policies and symbolic representations for effective decision-making. Teaching : Courses include 'Actions and Planning in AI' and 'Social and Technological Change', emphasizing interdisciplinary AI applications. Awards & Recognition : Alexander von Humboldt Professorship (2023), AAAI/EurAI Fellowships, and editor of influential works on Judea Pearl’s contributions to AI. Grants & Projects : Advanced ERC grant (2020–2025), Humboldt Foundation support, and RWTH funding for research on planning and reasoning. Labs & Teams : Heads the Chair of Machine Learning and Reasoning at RWTH, focusing on interdisciplinary research in AI, robotics, and planning algorithms.

Tibor SzabóView profile

Professor

• Combinatorics
• Extremal Graph Theory
• Random Structures and Algorithms
• +5 more

Tibor Szabó is a Professor in the Combinatorics and Graph Theory group at the Department of Mathematics, Freie Universität Berlin. He holds a PhD from The Ohio State University, advised by Ákos Seress. Prior to his current position, he held roles at McGill University, ETH Zürich, the Institute for Advanced Study (Princeton), and the University of Illinois (UIUC) as a J.L. Doob Research Assistant Professor. Research Interests: His work focuses on combinatorics and combinatorial optimization, including extremal problems, random structures and algorithms, pseudorandom graphs, positional games, and the combinatorics of linear programming. He explores tools from algebra, probability theory, and topology applied to combinatorics. Teaching: He teaches courses such as Algorithmic Combinatorics, Extremal Combinatorics, and runs the Combinatorics Seminar. His lecture notes include works on positional games and explicit constructions in extremal combinatorics. Students & Postdocs: Notable PhD advisees include Yamaan Attwa, Silas Rathke, Simona Boyadzhiyska, and Patrick Morris. Postdoctoral fellows include Olaf Parczyk and Anurag Bishnoi. His research has involved collaborations with over 50 co-authors. Funding & Grants: Supported by grants from the Swiss National Science Foundation (SNF) and German Research Foundation (DFG), focusing on topics like positional games and extremal graph theory.

Johannes CarmesinView profile

Professor

• Graph Theory
• Combinatorics
• Topology
• +3 more

Prof. Johannes Carmesin is a Professor at the Faculty for Mathematics and Computer Science, Technische Universität Bergakademie Freiberg. His research focuses on advanced topics in Graph Theory, Combinatorics, and Topology, with notable contributions to 3-dimensional Kuratowski-type theorems and the extension of Structural Graph Theory to simplicial complexes. He leads the Discrete Structures group, which explores areas including Graph Minors, Matroid Theory, and Infinite Graphs. Education and Career: He holds a PhD (2015) and Habilitation (2018) in Mathematics, with earlier work on harmonic functions in networks (Master's 2012). His academic journey includes research on infinite matroids and tree-decompositions. Research Interests: Carmesin’s work bridges discrete mathematics and topology, emphasizing structural properties of graphs and their embeddings. Key areas include 3D graph embeddings, connectivity in infinite graphs, and matroid characterizations. He has pioneered methods for analyzing 2-dimensional simplicial complexes and their topological representations. Recent Trends in Publications: His recent work extends Graph Minor Theory to temporal networks and 3D spaces, with a focus on canonical decompositions, tangle theory, and applications in large-scale network analysis. His 2023 European Prize in Combinatorics recognizes breakthroughs in 3D Kuratowski analogues. Awards: European Prize in Combinatorics (2023) Advising and Team: Supervised PhD students Emily Nevinson and Tsvetomir Mihaylov, and mentored master’s students like Will J. Turner. His research team includes Dr. Jan Kurkofka and Dr. Matthew Kroeker. Notable collaborations address infinite graph connectivity and Stallings-type theorems for finite groups. Labs/Groups: Directs the Discrete Structures research group, which hosts regular seminars and collaborates internationally. Past members include Georgios Kontogeorgiou and Lyuben Lichev as interns.

• Crystal Plasticity
• Materials Science
• Computational Materials Engineering
• +5 more

Martin Diehl is a computational materials scientist affiliated with KU Leuven (Departments of Computer Science and Materials Engineering) and the Max-Planck-Institut für Eisenforschung GmbH in Germany. His work focuses on crystal plasticity simulations, computational materials engineering, and multi-physics modeling of metallic systems. Research interests include: Crystal plasticity finite element method (CPFEM) and spectral solvers Microstructure evolution and damage mechanics Machine learning applications in materials design Development of the DAMASK simulation toolkit Multi-phase steel alloys and heterogeneous deformation Integrated computational materials engineering (ICME) Key trends in his publications since 2021 highlight advancements in: Multi-physics DAMASK framework for coupled chemo-mechanical and thermal simulations AI-driven inverse design of steel microstructures Damage modeling in dual-phase steels Collaborative software development for materials science Experimental-simulation integration for stress-strain partitioning High-resolution spectral methods for finite strain analysis He actively collaborates with institutions like Harbin Institute of Technology, University of Oxford, and research groups across Europe and Asia.

• Population Genetics
• Human Evolution
• Ancient DNA Analysis
• +3 more

Benjamin Peter is a Group Leader at the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany, leading the 'Genetic Diversity through Space and Time' group. He holds a PhD from the University of California, Berkeley (2014), an MSc from the University of Bern (2010), and a BSc in Biology from the University of Bern (2008). His research focuses on population genetics, ancient DNA analysis, and human evolutionary history. Education: PhD in Integrative Biology, UC Berkeley (2014) MSc in Ecology and Evolution, University of Bern (2010) BSc in Biology (Plant Sciences), University of Bern (2008) Research Interests: Dr. Peter specializes in reconstructing human evolutionary history through ancient DNA, population genetics, and genomic analysis. His work addresses topics such as Neanderthal admixture, Denisovan ancestry, and the genetic basis of human adaptation to high-altitude environments. He develops computational methods for analyzing ancient genetic data and interpreting population structure dynamics. Article Trends: His recent publications emphasize ancient genomic studies, including analyses of early modern human genomes, Neanderthal social organization, and Denisovan admixture in East Asian populations. His work often integrates archaeological and genetic data to elucidate human migration and evolutionary trajectories. Labs/Teams: He leads a research group focused on genetic diversity dynamics, collaborating with institutions globally. His team employs cutting-edge sequencing and computational tools to explore evolutionary questions.

• Applied Mathematics
• Computational Methods
• Control Theory
• +5 more

Sara Grundel is a leading researcher at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany. Her work focuses on computational methods in systems and control theory, particularly in model order reduction, gas network simulation, and optimization of energy systems. Education: Diplom in Mathematics, ETH Zurich (2005) PhD in Mathematics, Courant Institute of Mathematical Sciences, New York University (2011) Research Interests: Sara’s research encompasses mathematical control theory, stability analysis, and numerical methods for differential-algebraic equations. She applies these techniques to gas and energy networks, epidemic modeling, and multi-agent systems. Her interdisciplinary work bridges computational mathematics with real-world engineering and public health challenges. Recent Publications: Her 15 most recent articles (2024–2012) demonstrate expertise in parametrized PDEs, model reduction for coupled systems, and control strategies for SARS-CoV-2 containment. Key subtopics include adaptive meshing, stability-preserving algorithms, and optimization of nonlinear network dynamics. Scientific Contributions: Developed clustering-based model reduction techniques for networked systems Investigated hyperbolic discretization methods using Riemann invariants Advanced polynomial root radius optimization with affine constraints Collaborations: Sara frequently collaborates with researchers like Peter Benner and Martin Gersen on energy grid simulations and control theory. She participates in international conferences (GAMM, IEEE CDC, MTNS) and contributes to edited volumes in applied mathematics.

• Mathematical Optimization
• Nonsmooth Optimization
• Multiobjective Optimization
• +4 more

Bennet Gebken is a researcher at the Department of Mathematics , Technical University of Munich , affiliated with the Chair of Mathematical Optimization led by Prof. Ulbrich. His work focuses on nonsmooth and multiobjective optimization, particularly in PDE-constrained problems and numerical continuation methods. Research Interests: Bennet specializes in nonsmooth optimization, multiobjective optimization, and regularization techniques. His recent work explores convergence analysis in nonsmooth settings, second-order gradient sampling, and inverse optimization methods for data-driven decision criteria. Publications: His research spans topics like PDE-constrained multiobjective optimization, L1 penalty terms, and the hierarchical structure of Pareto critical sets. Key trends include reduced-order modeling, computational efficiency, and the interplay between optimization and machine learning. Contact: bennet.gebken@tum.de . Based at Boltzmannstr. 3, Garching b. München, Germany.

Marcel CampenView profile

Professor

• Computer Graphics
• Geometry Processing
• Surface Parametrization
• +4 more

Marcel Campen is a Professor at Osnabrück University specializing in Computer Graphics and Geometry Processing. His research focuses on surface parametrization, quad mesh generation, and computational geometry. He has made significant contributions to the field of geometry processing, particularly in developing algorithms for quad layout generation, surface mapping, and mesh repair. His research interests span Computer Graphics, Geometry Processing, Surface Parametrization, Quad Mesh Generation, 3D Modeling, and Mesh Repair. Campen's work addresses fundamental challenges in representing and processing complex geometric shapes, with applications ranging from animation and simulation to reverse engineering and meshing. His research often combines theoretical insights with practical implementations, resulting in algorithms that are both mathematically sound and computationally efficient. Campen's publications demonstrate a strong focus on developing robust and efficient methods for geometry processing. His work on quad layout generation, parametrization techniques, and surface mapping has resulted in several award-winning papers, including Best Paper Awards at SGP 2021 and 2022. His research often bridges theoretical concepts with practical implementations, making his contributions highly influential in both academic and industrial settings. Best Paper Award (1st place) at SGP 2022 Best Paper Award at SGP 2021 Campen has made significant contributions to the field through his doctoral thesis on quad layout generation and numerous publications in top-tier conferences including SIGGRAPH, Eurographics, and SGP. His work on directional field synthesis, similarity maps, and bijective mappings has advanced the state of the art in geometry processing. He has also contributed to practical tools like libQEx for robust quad mesh extraction, demonstrating his commitment to making theoretical advances accessible to practitioners.

Christian HeineView profile

Researcher

• Data Visualization
• Topology-Based Methods
• Scientific Computing
• +3 more

Christian Heine is a researcher at the Institute of Computer Science , University of Leipzig. His work focuses on advanced data visualization techniques, particularly those grounded in topological and geometric analysis of scalar fields, ensemble data, and high-dimensional datasets. Key Research Areas: Topological visualization, scalar field analysis, medical imaging, and uncertainty quantification. Methodologies: Bayesian inference, fiber trajectories, volume rendering, and dynamic workflows. Applications: Meteorological data analysis, medical diagnostics, and interactive visualization systems. He has published extensively on these topics, with recent work addressing spatio-temporal trends in climate data and noise-robust visualization techniques. His research often integrates interdisciplinary approaches, bridging computer science and applied sciences.

• Mathematical Methods in Signal Processing
• Data Science
• Dynamical Systems
• +5 more

Nada Sissouno is a Professor of Mathematics and Didactics of Mathematics at the Faculty of Electrical Engineering, Media and Informatics at Amberg-Weiden University of Applied Sciences since November 2023. She also serves as Vice Dean and Co-head of the Competence Center Grundlagen (CCG). Additionally, she maintains a position as a guest researcher at the Research Group: Applied and Numerical Analysis and Optimization and Data Analysis at the Technical University of Munich (TUM). Her educational background includes a Doctorate in Mathematics (Dr. rer. nat.) from TU Darmstadt (2007-2011) and a Diplom in Mathematics with a minor in psychology from TU Darmstadt (2000-2007). She has completed further education as a Diversity Manager in 2021 and holds certificates in teaching in higher education from the Bavarian Universities (2014-2016). Professor Sissouno's research focuses on mathematical methods in signal and image processing, data science, dynamical systems, numerical simulation, and approximation theory. Her work particularly emphasizes spline functions on domains, wavelets and frames, and evidence-based development of teaching methodologies. Her recent publications demonstrate strong expertise in mathematical imaging, phase retrieval problems, and approximation theory, with applications spanning ptychographic imaging, variational inpainting methods, and structural sparsity in multiple measurements. Her collaborative research bridges theoretical mathematics with practical applications in signal processing and image analysis, with a particular focus on developing robust numerical algorithms for complex data analysis problems. She has published in prestigious journals including Advances in Computational Mathematics, Journal of Fourier Analysis and Applications, IEEE Transactions on Signal Processing, and Inverse Problems. Referentin für Talentmanagement & Diversity at TUM (2022-2023) Deputy spokesperson of Research Associates' Council of the TUM (2019-2023) Gender equality officer of Department of Mathematics (2019-2022) Professor Sissouno teaches mathematics courses for engineering and computer science students, with a focus on making mathematical concepts accessible and relevant to practical applications. She has been involved in teacher training and the evidence-based development of teaching methodologies, demonstrating her commitment to both research excellence and educational innovation.

• Mathematical Optimization
• Algorithmic Game Theory
• Multi-Agent Systems
• +3 more

Max Klimm is an Assistant Professor for Discrete Optimization at Technische Universität Berlin, affiliated with the Department of Mathematics within Faculty II – Mathematics and Natural Sciences. He holds a PhD in Mathematics from TU Berlin (2012) and previously served as an Assistant Professor for Operations Research at Humboldt-Universität zu Berlin (2014–2016) and led the Junior Research Group for Optimization under Uncertainty at the Einstein-Center for Mathematics (2014–2016). His research focuses on algorithmic game theory, mathematical optimization of multi-agent systems in traffic, telecommunications, and economics. Key areas include network flows, congestion games, mechanism design, and infrastructure networks. He actively contributes to academic activities as an associate editor for journals like International Journal of Game Theory and Operations Research Forum , and has served on program committees for conferences such as AAAI, EC, and WINE. Recent work includes studies on evolutionary road network reconstruction, robustness of potential-based flows, and algorithmic solutions for knapsack and assignment problems. His research bridges theoretical foundations with practical applications in transportation and resource allocation.

Claudia ScheimbauerView profile

Professor

• Mathematical Physics
• Topological Field Theories
• Higher Category Theory
• +4 more

Prof. Claudia Scheimbauer is a tenure-track Professor at TU München’s Department of Mathematics, affiliated with the TUM School of Computation, Information, and Technology (CIT). Her research focuses on the intersection of mathematical physics and algebraic topology, particularly topological field theories (TFTs), higher category theory, and derived symplectic geometry. She has pioneered work on extended TFTs using factorization algebras and has contributed to foundational questions in categorical structures like 2-Segal spaces and dagger categories. She is a Principal Investigator in the SFB/CRC 1624 on Higher Structures and the prolonged SFB/CRC 1085 on Higher Invariants. Her group includes 1 PhD student (Anja Švraka) and 5 postdocs (Pelle Steffens, Walker Stern, Will Stewart, Jackson van Dyke, Yang Yang). She organizes workshops like Spaces of Tensor Categories (2025) and co-organized conferences on categorical symmetries and geometric topology. Her teaching spans algebraic topology, bordism theories, and category theory. Notable collaborations include work with Damien Calaque on cobordism categories and with Theo Johnson-Freyd on twisted TFTs. She is also involved in initiatives like the FACETS of mathematicians series addressing academic culture.

• Structural Optimization
• Isogeometric Analysis
• Finite Element Analysis
• +7 more

Bastian Devresse is a Researcher at the Chair of Structural Analysis at the Technical University of Munich (TUM) , where he has worked since 2022. He holds a Master's degree in Civil Engineering from TUM (2019-2022) and a Bachelor's degree in Civil and Environmental Engineering from Hamburg University of Technology (2015-2019). His research focuses on advanced shape optimization techniques, particularly in structural mechanics and wind engineering. Research Interests : Node-based shape optimization Isogeometric analysis and B-Rep modeling Wind-induced vibrations and flexible membrane structures Lightweight and additive manufacturing optimization Multidisciplinary optimization frameworks Publications highlight his work in developing innovative parameterizations for bead-like features, applying Vertex Morphing methods to thin-walled structures, and integrating sensitivity filtering for robust optimization. Recent work extends to wind engineering simulations and membrane wing design. Teaching includes courses on finite element methods and nonlinear structural analysis. He has supervised theses on topics like robust shape optimization and isogeometric subdivision surfaces. Contact: bastian.devresse@tum.de | Office: Room 0101.Z1.019

Martin MarinovView profile

Researcher

• Computer Graphics
• Geometric Modeling
• 3D Mesh Processing
• +3 more

Dr. Martin Marinov is a researcher affiliated with RWTH Aachen University, specializing in computer graphics, geometric modeling, and 3D mesh processing. His work focuses on multiresolution modeling, subdivision surfaces, and GPU-accelerated algorithms for real-time rendering. Research areas: Subdivision surfaces, quad-dominant remeshing, scattered data approximation, and GPU computing. His publications include innovative methods for mesh decimation, feature preservation, and anisotropic remeshing, with applications in computer graphics and geometric modeling. Marinov has contributed to optimizing parameter correction for subdivision surfaces and developing robust algorithms for complex 3D models.

Federico CastilloView profile

Assistant Professor

• Polytopes
• Algebraic Geometry
• Discrete Mathematics
• +3 more

Federico Castillo is an Assistant Professor in the Department of Mathematics at the Pontificia Universidad Católica de Chile . He holds a PhD from the University of California, Davis (2017), and has held postdoctoral positions at the University of Kansas (2018–2020) and the Max Planck Institute for Mathematics in the Sciences (MPI, 2020–2021). His research focuses on polytopes, Minkowski sums, polyhedral subdivisions, and lattice point enumeration, with computational applications in nonlinear algebra. His academic trajectory includes postdoctoral fellowships at MSRI (2017) and MPI Leipzig (2020–2021). He organizes the Number Theory and Algebra Seminar (SaNTAS) with Giancarlo Lucchini and David Grimm. His work spans theoretical contributions to algebraic geometry and combinatorics, with interdisciplinary applications in mathematical physics and computational algebra. Education: PhD in Mathematics, University of California, Davis (2017), advised by Prof. Fu Liu Postdoc, University of Kansas (2018–2020) Postdoc, MPI Leipzig Nonlinear Algebra Group (2020–2021) Research Interests: Castillo’s work centers on polytopes and their combinatorial/geometric properties, including Minkowski decompositions, lattice point enumeration, and computational aspects of nonlinear algebra. He explores applications in algebraic geometry, discrete mathematics, and mathematical physics, often leveraging tools from commutative algebra and combinatorics. Recent Contributions: His articles (2020–2024) address topics like permutohedra, Coxeter groups, matroid complexes, and N-representability problems in quantum chemistry. These contributions highlight his expertise in bridging abstract algebraic structures with computational methods. Awards & Grants: No explicit awards listed, though his sustained research output reflects competitive grant support for postdoctoral work and collaborations. Labs & Teams: Active organizer of events like AGREGA (Algebraic Geometry Research Group in the Andes) and Encuentro Colombiano de Combinatoria (ECCO) . Co-organizes the SaNTAS seminar, fostering interdisciplinary dialog in algebra and number theory.