Find Academic Supervisors

Moritz KerzView profile

Professor

• Arithmetic Geometry
• Algebraic K-Theory
• Number Theory
• +1 more

Moritz Kerz is a Professor of Mathematics at the University of Regensburg's Faculty of Mathematics. His research spans arithmetic geometry and algebraic K-theory, with significant contributions to class field theory and cohomological methods. He leads a research group including postdoctoral scholars and doctoral candidates. Research Focus: Kerz's investigations center on: Non-archimedean K-theory and its applications to geometric problems Arithmetic invariants in positive characteristic Higher-dimensional class field theory constructions Monodromy representations and density theorems Publication Trends: Recent work demonstrates a consistent focus on K-theoretic invariants in arithmetic contexts, particularly through: Innovative applications to rigid analytic geometry Interactions between étale cohomology and representation theory Non-commutative generalizations of class field theory Awards: Minkowski Medal (2020) K-theory Prize (2014) Carus Medal (2011) Heinz Maier-Leibnitz Prize (2011) Cultural Prize of Bavaria (2009) Research Group: Current team members include Carolyn Echter, Lukas Krinner, Andrea Panontin, Yanshuai Qin, Yuenian Zhou, and Paul Ziegler, with research spanning arithmetic geometry and K-theory applications.

• Mathematical Analysis
• Partial Differential Equations
• Stochastic Homogenization
• +4 more

Felix Otto is a Director at the Max Planck Institute for Mathematics in the Sciences and an Honorary Professor for Analysis and Mathematical Modeling at the University of Leipzig. His research focuses on pattern formation, energy landscapes, and scaling laws, with contributions to stochastic homogenization, PDEs, and optimal transport. He has held academic positions at the University of California and the Hausdorff Center for Mathematics. Notable grants include projects on microstructure formation in thin coatings and stochastic homogenization. He has organized conferences such as the ICM satellite conference on Probability and Mathematical Physics. His work bridges pure and applied mathematics, with implications for materials science and fluid dynamics. Education: Diplom (1990), PhD (1993) from Bonn University. Postdoctoral roles at Bonn, Courant Institute, and CMU. Tenured faculty roles at UCSB (1998–2010) and Bonn (1999–2010). Research interests span stochastic PDEs, calculus of variations, and the mathematical theory of materials. Grants include BMBF and DFG projects on microstructure modeling and homogenization.

Davide TateoView profile

Visiting Professor

• Reinforcement Learning
• Robotics
• Safe and Reliable Robot Learning
• +3 more

Davide Tateo is a postdoctoral researcher and visiting professor at TU Darmstadt, leading the Safe and Reliable Robot Learning Research Group within the Intelligent Autonomous Systems group of the Computer Science Department. His research focuses on developing safe and efficient reinforcement learning algorithms for real-world robotics applications. His work spans Reinforcement Learning (Safe RL, Deep RL) and Robotics (fast motion planning, locomotion). He is involved in multiple funded projects including KIARA (advanced manipulation in risky scenarios), DeepWalking (human gait learning), and INTENTION (active perception for legged robots). Recent publications highlight his expertise in Safe RL (inductive biases, collision probability fields), Locomotion (multi-embodiment, morphology-aware policies), and Optimization (contact planning, trajectory distillation). He collaborates with the PEARL lab at TU Darmstadt and has contributed to key workshops like CoRL 2024 and RSS 2024. Contact details: Email: davide.tateo@tu-darmstadt.de Room E303, Building S2|02, Hochschulstr. 10, Darmstadt Phone: +49-6151-16-20811

Zoe SanderView profile

Research Fellow

• Educational Psychology
• Student Motivation
• Teaching Didactics
• +5 more

Zoe Sander is a Research Fellow and PhD student at the Department of Educational Psychology (Heidelberg University, headed by Prof. Dr. Birgit Spinath). She concurrently serves as Journal Managing Editor for Psychology Learning & Teaching . Her work focuses on student motivation, teaching methodologies, and longitudinal development in educational contexts. Education: Bachelor of Science (Psychology), Heidelberg University (2017–2020) Master of Science (Developmental & Clinical Psychology), Heidelberg University (2020–2022) Research Interests: Motivation as a prerequisite for academic success, criteria of effective teaching, and assessment strategies. Her studies explore pandemic impacts on learning, self-regulated learning strategies, and the alignment between teaching methods and student motivation. Publications & Presentations: Recently published work examines motivational differences between regular and pandemic online semesters, and the role of constructive alignment in fostering intrinsic motivation. She has presented at major conferences including EARLI SIG, DGPs, and ESPLAT, focusing on longitudinal analyses of motivation and learning behaviors. Affiliations: Active researcher in educational psychology with roles in editorial work and academic advising. Collaborates closely with Prof. Spinath's research group.

Christoph GilleView profile

Professor

• Social Work
• Poverty and Social Exclusion
• Civil Society Engagement
• +4 more

Christoph Gille is a Professor of Social Work in Contexts of Poverty and Exclusion at the University of Applied Sciences Düsseldorf since 2022. He holds leadership roles as co-leader of the FNuST Research Centre and study program director for the Bachelor's in Social Work. His work focuses on poverty research, civil society engagement, and transnational social policy. Research Interests : Poverty and social exclusion, civil society engagement, international social work, housing and drug policy, participatory research methods, far-right influence on social services. Recent Projects : Open Drug Scene Survey NRW 2024 , Engagement Against Exclusion (ESA) , and Discrimination in Housing Markets (DiWo) . Publications : Co-author of Offene Drogenszenen in NRW (2025) and Selbstvertretung in der Armutsbekämpfung (2024), among others in open-access formats. Teaching : Courses on unemployment, social policy theories, and transnational social work, including international collaborations with Antwerp and The Hague universities. Advocacy : Regular speaker on far-right challenges to social work, poverty participation structures, and housing rights at national and international conferences.

Marc TeboulleView profile

Professor

• Continuous Optimization
• Convex Optimization
• Complexity Analysis of Optimization Algorithms
• +5 more

Marc Teboulle is a distinguished Professor holding The Eric and Sheila Samson Chair of Optimization in the School of Mathematical Sciences at Tel Aviv University. With a career spanning over three decades, he has established himself as a leading figure in optimization theory and applications. His work bridges theoretical foundations with practical implementations across multiple scientific domains. Professor Teboulle's research focuses on continuous optimization, with particular emphasis on convex optimization, complexity analysis of algorithms, Lagrangian and dual decomposition methods, variational inequalities, and nonconvex nonsmooth large-scale optimization. His work has significant applications in engineering science, machine learning, and finance, demonstrating the interdisciplinary impact of optimization techniques. He has developed novel frameworks for center-based clustering algorithms and contributed to the theoretical understanding of first-order methods beyond traditional Lipschitz gradient continuity assumptions. His publication record shows a clear evolution toward increasingly sophisticated optimization frameworks, with recent work focusing on nonconvex composite optimization, Lagrangian-based methods, and complexity analysis of gradient-based algorithms. The trend indicates growing interest in non-Euclidean geometries for optimization and applications to high-dimensional data problems, reflecting the evolving challenges in modern optimization. As an educator and mentor, Professor Teboulle has supervised numerous PhD and MSc students since 1990, including prominent researchers like Amir Beck, Ron Shefi, and Yoel Drori. His graduate courses include Convex Analysis and Optimization, Advanced Topics in Modern Optimization, Algorithms for Continuous Optimization, and Advanced Seminar in Continuous Optimization. He has been exceptionally active in the academic community, delivering invited lectures at major international conferences from 2003 through 2024 across Asia, Europe, and North America. His book 'Asymptotic Cones and Functions in Optimization and Variational Inequalities' (co-authored with A. Auslender) has become a standard reference in the field, while his edited volume 'Grouping Multidimensional Data: Recent Advances in Clustering' has influenced data science applications.

• Wireless Communication
• Machine Learning
• Signal Processing
• +4 more

Prof. Dr. Haris Gačanin is a faculty member at RWTH Aachen University, affiliated with the Institute for Distributed Signal Processing under the College of Electrical Engineering. His research focuses on integrating machine learning with wireless communication systems, particularly in industrial IoT, edge computing, and network optimization. Current academic rank: Professor Contact: harisg@dsp.rwth-aachen.de Research Interests: Wireless systems, machine learning, signal processing, and network optimization. Key contributions include: Adaptive resource allocation in IIoT and vehicular networks AI-driven channel estimation and feedback mechanisms Security-oriented emitter identification via metric learning Federated/transfer learning for edge environments Hardware-efficient deep learning models for mmWave and THz communications Methodological Focus: Combines reinforcement learning, attention mechanisms, and robust neural architectures with practical implementations on FPGA and vehicular systems.

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.

• Algebraic Methods in Dynamical Systems
• Difference and Differential Algebra
• Galois Theory of Differential Equations
• +4 more

Michael Wibmer is a Lecturer in Pure Mathematics at the University of Leeds since 2023. Previously, he held positions at Graz University of Technology, the University of Notre Dame, the University of Pennsylvania, and RWTH Aachen University. He earned his PhD from the University of Heidelberg under the supervision of B.H. Matzat. His research focuses on algebraic, algorithmic, and arithmetic aspects of differential and difference equations, including Galois theory and algebraic groups. Key areas include algebraic methods in dynamical systems, symbolic computation, and connections to model theory and number theory. He organizes events like the Online Kolchin Seminar in Differential Algebra and co-organized workshops such as DART XI (2023) and the MSRI Summer School on Differential Equations (2022). Education: PhD in Mathematics, University of Heidelberg (2010), Habilitation in Mathematics, RWTH Aachen University (2015) Research Interests: Galois theory of differential equations, proalgebraic groups, difference algebraic groups, number theory, and symbolic computation Recent Activities: Organized the Algebraic Theory of Differential and Difference Equations workshop at Leeds (2024) His publications span topics like torsors under affine group schemes, regular singular differential equations, and étale difference algebraic groups, reflecting his expertise in algebraic structures and their applications to differential systems.

Dimitrios KarampinosView profile

Professor

• Magnetic Resonance Imaging (MRI)
• Biomedical Engineering
• Medical Imaging Technology
• +6 more

Prof. Dimitrios Karampinos is a Professor at the Technical University of Munich (TUM), leading the Experimental Magnetic Resonance Imaging group within the TUM School of Medicine and Health. He specializes in developing novel MRI techniques for quantitative biomarker discovery, focusing on musculoskeletal, metabolic, and oncological applications. His career includes a PhD from the University of Illinois (2008), postdoctoral research at UCSF (2009–2012), and leadership roles at TUM since 2012. Prof. Karampinos has pioneered advancements in MRI reconstruction, signal modulation, and biomarker validation for clinical translation. Educations: BSc in Mechanical Engineering (National Technical University of Athens, Greece), PhD in Biomedical Engineering (University of Illinois, Urbana-Champaign, 2008). Research Interests: Development of MRI measurement techniques, quantitative biomarkers for disease diagnosis, and improving therapy monitoring. Key areas include musculoskeletal disease imaging, metabolic disorder assessment, and oncology applications. His work emphasizes translating research into clinical practice through innovations like accelerated imaging, artifact correction, and AI-driven analysis. Awards: ERC Starting and Proof of Concept Grants (2015, 2019), TUM Supervisory Award (2020), ISMRM Junior Fellow (2011). Grants: Multiple ERC grants for MRI method development. Labs/Teams: Leads the Experimental Magnetic Resonance Imaging group at TUM, collaborating on clinical and technical MRI advancements.

Wolfgang ReichelView profile

Professor

• Nonlinear Partial Differential Equations
• Mathematical Physics
• Wave Phenomena
• +4 more

Prof. Dr. Wolfgang Reichel is a faculty member at the Karlsruhe Institute of Technology (KIT) , affiliated with the Department of Mathematics and leading the Workgroup Nonlinear Partial Differential Equations . He serves as deputy speaker of the Collaborative Research Center (CRC) 1173 "Wave Phenomena: Analysis and Numerics", and is a speaker for the KIT-Center MathSEE "Mathematics in Sciences, Engineering, and Economics". His research focuses on nonlinear partial differential equations, mathematical modeling, and numerical analysis of wave phenomena. His workgroup includes postdocs, doctoral researchers, and master students, and he collaborates with colleagues like Thomas Bartsch, Bernd Kawohl, Michael Plum, Guido Sweers, and Tobias Weth. Regular events organized by Prof. Reichel include the Karlsruhe PDE-Seminar and annual Nonlinear PDE Days summer/winter schools. He has taught courses such as Rand- und Eigenwertprobleme, Functional Analysis, and Analysis of PDEs across multiple semesters. Scientific publications reveal a focus on nonlinear wave equations (e.g., Maxwell's, Klein-Gordon, Lugiato-Lefever), soliton dynamics, frequency comb generation, and symmetry properties in elliptic PDEs. Articles often address existence proofs, stability analysis, and numerical methods for wave phenomena in periodic media and nonlinear optics. Collaborative work spans institutions like MacMaster University, University of Stuttgart, and University of Connecticut.

Tobias KießlingView profile

Researcher

• Computational Quantum Materials
• Theoretical Physics
• Experimental Physics
• +3 more

Dr. Tobias Kießling is a Researcher and Group Leader at the Physics Institute within the Faculty of Physics and Astronomy at the University of Würzburg. His research focuses on Computational Quantum Materials, Theoretical Physics, and Experimental Physics, with affiliations in Astronomy. He leads the Kießling Group, which collaborates across multiple experimental and theoretical physics divisions. His team includes researchers such as Hauke Brand, Christian Berger, and others. Contact details are available at Am Hubland Süd (Physics), Building P1, Room B-019. Research interests emphasize advanced computational methods in quantum materials and interdisciplinary theoretical studies. The group’s work bridges experimental and computational approaches in physics, contributing to advancements in condensed matter and quantum systems. No specific articles or grants are detailed in the provided text, though the group maintains active research publications. No awards are explicitly mentioned. The group’s activities include regular seminars and collaborations with other research groups like the Palffy-Buß Group.

• Mathematical Data Science
• Machine Learning
• Compressed Sensing
• +5 more

Johannes Maly is an Assistant Professor at the Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence at LMU Munich. He previously held postdoctoral positions at Catholic University of Eichstaett-Ingolstadt and RWTH Aachen University, and completed his PhD at TUM Munich under Prof. Massimo Fornasier. PhD in Mathematics (2019, TUM Munich) M.Sc. in Mathematics (2015, TUM Munich) B.Sc. in Mathematics (2013, TUM Munich) His research focuses on mathematical data science and machine learning, specifically addressing: Robust covariance estimation under quantization Neural network approximation properties Implicit bias in gradient descent training Multi-structured signal recovery Quantization effects in deep learning and compressed sensing His recent publications analyze dithered quantization in covariance estimation, implicit regularization in overparameterized models, and multi-structured data recovery. He applies mathematical rigor to practical challenges in wireless communications (e.g., MIMO systems) and neural network training. Scientific recognition includes: relAI Fellow MCML Associate He supervises code/toolbox development for reproducibility and teaches graduate courses in convex optimization, high-dimensional probability, and mathematical data science. His work bridges theoretical mathematics and applied signal processing.

Andrés BruhnView profile

Professor

• Computer Vision
• Optical Flow
• Scene Flow
• +5 more

Andrés Bruhn is a Professor for Intelligent Systems and Dean of Computer Science Studies at the University of Stuttgart, where he leads research in the Institute for Visualization and Interactive Systems (VIS). His academic career spans over a decade with significant contributions to computer vision, particularly in optical flow, scene flow, and motion estimation. As Dean of Studies, he oversees academic programs while maintaining an active research agenda focused on cutting-edge computer vision problems. Bruhn's research interests center around computer vision with emphasis on optical flow estimation, scene flow, motion analysis, and adversarial machine learning. His work bridges theoretical foundations with practical applications, developing algorithms that address real-world challenges in motion estimation, image processing, and visual understanding. His research group has pioneered approaches that combine variational methods with deep learning, creating robust systems for motion analysis that can withstand adversarial attacks and challenging environmental conditions. The publication record demonstrates a strong focus on advancing the state-of-the-art in motion estimation, with recent work exploring adversarial attacks on optical flow systems, high-resolution datasets for benchmarking, and multi-frame fusion techniques. His research shows consistent innovation, moving from traditional variational methods to modern deep learning approaches while maintaining mathematical rigor. The work spans both theoretical contributions and practical implementations with real-world applicability. Bruhn has mentored numerous researchers who appear as first authors on publications, including Jenny Schmalfuss, Lukas Mehl, and Azin Jahedi, indicating his commitment to developing the next generation of computer vision researchers. His leadership role as Dean of Studies demonstrates institutional recognition of his expertise and administrative capabilities.

• Economics
• Macroeconomics
• Labor Economics
• +4 more

Christian Bayer is a Professor of Economics at the University of Bonn, affiliated with the Department of Economics and the Institute for Macroeconomics and Econometrics. His research spans macroeconomic dynamics, labor economics, and heterogeneous agent modeling, with a focus on inequality, fiscal policy, and business cycles. Research Interests: Macroeconomics, labor economics, income risk, computational economics, fiscal and monetary policy, business cycles. Collaborations: Benjamin Born, Ralph Luetticke, Moritz Kuhn, and others. Recent work explores HANK models , pandemic consumption , and energy shock responses , with applications to policy design. Key trends include integrating heterogeneous agent frameworks and numerical methods to analyze shock propagation and market frictions. Press Contributions: Regular op-eds in Frankfurter Allgemeine Zeitung on wealth distribution, CO₂ pricing, and gas dependency. Projects: Active revisions include "Which Ladder to Climb?" (2019) and "Monopsony Makes Firms..." (2022). He is associated with the Institute for Macroeconomics and Econometrics at the University of Bonn, contributing to policy-focused research and methodological advancements in economic modeling.