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Jörn BehrensView profile

Professor

• Numerical Methods in Geosciences
• Climate Modeling
• Tsunami Simulation
• +5 more

Prof. Jörn Behrens is a Professor of Numerical Methods in Geosciences at the University of Hamburg's Department of Mathematics. His work focuses on computational geoscience, including adaptive numerical methods, tsunami modeling, and multiscale simulations. He leads the Numerical Methods in Geosciences research group and is affiliated with the Climate, Climatic Change, and Society (CLICCS) cluster of excellence. Education & Positions: Since 2009: Full Professor at University of Hamburg 2012: Visiting Scientist at Isaac Newton Institute, Cambridge 2007–2009: Privatdozent at University of Bremen Extensive experience at Alfred-Wegener-Institut and TU Munich Research Interests: Behrens develops numerical algorithms for geophysical flows, emphasizing adaptive mesh refinement, discontinuous Galerkin methods, and tsunami hazard modeling. His work bridges computational mathematics and geoscience applications, with a focus on environmental challenges like climate modeling and disaster risk assessment. Recent efforts include cloud computing for instant hazard simulations and interdisciplinary projects like the Global Tsunami Model (GTM). Key Publications: Recent work includes bathymetry reconstruction via PDE-constrained optimization, low-rank preconditioning for fluid flow, and tsunami hazard frameworks within EPOS. These reflect his expertise in computational methods and geophysical problem-solving. Grants & Projects: Lead on EU-funded DT-GEO (Digital Twin for Geophysical Extremes) Co-chair of COST Action AGITHAR (Tsunami Hazard Research) Contributions to CLICCS and EPOS infrastructure development Labs & Teams: Leads the Numerical Methods in Geosciences group, collaborating internationally on tsunami modeling, climate systems, and high-performance computing.

Chinmoy BhattacharjeeView profile

Professor

• Stochastic Geometry
• Distributional Approximations
• Stein's Method
• +4 more

Prof. Chinmoy Bhattacharjee is a Professor of Mathematical Stochastics at the University of Hamburg's Department of Mathematics, part of the Faculty of Mathematics, Computer Science and Natural Sciences. His research focuses on stochastic geometry, distributional approximations via Stein's method, stochastic analysis, random graphs, continuum percolation, and nonparametric inference. He holds a PhD from the University of Southern California and previously held postdoctoral positions at the University of Luxembourg and University of Bern. His work is supported by the German Research Foundation (DFG) through Project No. 531540467. Education: PhD in Applied Mathematics, University of Southern California (2013–2018) M.Stat. and B.Stat. (Hons.), Indian Statistical Institute, Kolkata (2008–2013) Teaching: Recent courses include Stochastic Processes, Probability and Statistics, and seminars on percolation theory and probabilistic methods. Research Highlights: Prof. Bhattacharjee's work bridges stochastic geometry and Stein's method, with applications in spatial random structures, non-Gaussian limit theory, and noise sensitivity in percolation models. Key contributions include Gaussian and Dickman approximation techniques, spectral analysis of Poisson functionals, and statistical applications in random forests. Upcoming Events: Co-organizing session on Spatial Stochastics at German Probability and Statistics Days (2025) Invited talks at Aarhus University and workshops in Germany/Croatia (2025)

Ralf MöllerView profile

Professor

• Artificial Intelligence
• Humanities-Centered AI
• Causal and Probabilistic Models
• +5 more

Prof. Ralf Möller is a Professor of Artificial Intelligence in Humanities at the University of Hamburg's Faculty of Humanities, Department of Philosophy. He leads the Institute of Humanities-Centered Artificial Intelligence (CHAI) and serves as spokesperson for the 'Data Linking' research field within the Cluster of Excellence ‘Understanding Written Artefacts’ (UWA, 2019–2025). His research focuses on intellectics, causal and probabilistic-relational models, and AI applications in humanities, emphasizing sustainable data management and multimodal foundation models. He heads the Data Linking Lab and the CHAI Institute, overseeing projects like the TAMAR initiative for manuscript research. His work bridges technical AI advancements with humanities needs, addressing challenges in data curation, federated information systems, and ethical AI integration. He contributes to interdisciplinary collaborations, including ethics committees and cultural heritage preservation initiatives. Key research themes include lifted inference in probabilistic graphical models, temporal data prediction, and synergistic OCR-LLM systems for damaged documents. His projects emphasize scalability, sustainability, and human-centered design principles. Supervising doctoral candidates in AI and humanities intersections, he advocates for AI systems grounded in cultural and ethical considerations. Labs/Teams: CHAI Institute, Data Linking Lab. Current Projects: UWA Cluster (2019–2025), Data Linking Infrastructure development, Humanities-Centered AI applications. Grants include leadership roles in institutional and collaborative research funding.

Wolfgang MulzerView profile

Professor

• Theoretical Computer Science
• Computational Geometry
• Algorithms
• +3 more

Wolfgang Mulzer is a Professor in the Department of Computer Science at Freie Universität Berlin, within the Faculty of Mathematics and Computer Science. He leads the Theoretical Computer Science research group (AG Theoretische Informatik) and holds a PhD from Princeton University (2010), advised by Bernard Chazelle. His research focuses on computational geometry, algorithms, and discrete mathematics, with notable contributions to geometric algorithms, data structures, and combinatorial optimization. Education: PhD in Computer Science, Princeton University (2010) Advisor: Bernard Chazelle Research Interests: Mulzer's work spans theoretical computer science with an emphasis on computational geometry. Key areas include geometric algorithms (e.g., Voronoi diagrams, Fréchet distance), dynamic graph algorithms (e.g., disk graphs, connectivity problems), and algorithmic complexity. His research also addresses discrete geometry challenges like Tverberg theorems and geometric intersection graphs. Grants & Labs: Leading the Theoretical Computer Science group at FU Berlin Active in collaborative projects on geometric algorithms and algorithmic foundations Professional Activities: Mulzer has authored or co-authored over 100 peer-reviewed papers, with work appearing in top venues like Discrete & Computational Geometry , SIAM Journal on Computing , and Algorithmica . His research bridges theoretical foundations with practical algorithm design.

Michael EngelView profile

Professor

• Nanotechnology
• Soft Matter Physics
• Self-Assembly
• +3 more

Prof. Michael Engel holds a professorship in Modeling of Self-organization Processes at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), within the Institute for Multiscale Simulation. His research focuses on nanoscale and soft matter systems, emphasizing self-assembly and bottom-up processes to design complex nanomaterials. Key areas include colloidal self-assembly, nanocrystal engineering, and quasicrystal formation. He leads the Engellab, a computational/theoretical group exploring emergent phenomena in particulate and atomic systems. Research interests span nanoparticle etching, ligand-directed assembly, and structural dynamics of nanomaterials. Recent work highlights entropic trapping effects, geometric frustration, and kinetic pathways in nanocrystal formation. Collaboration with institutions like the Indian Institute of Science underscores his international engagement in nanotechnology and materials science. Labs/Teams: Engellab serves as a hub for computational modeling and theoretical studies, addressing challenges in self-organizing systems. Current projects include designing durable nanocatalysts and understanding phase transitions in confined nanoparticle systems.

Volker MehrmannView profile

Professor

• Applied and Numerical Linear Algebra
• Analysis and Numerics of Differential-Algebraic Equations
• Numerical Solution of Differential Equations
• +3 more

Volker Mehrmann is a Professor of Mathematics (Numerical Mathematics) at Technische Universität Berlin since 2000. He holds a C4-Professorship and has held leadership roles in major research initiatives such as the DFG Research Center MATHEON and the Einstein Center ECMath. His research focuses on applied and numerical linear algebra, differential-algebraic equations, control theory, and industrial mathematics. Mehrmann earned his Ph.D. from Universität Bielefeld (1982) and habilitation (1988). He has authored influential textbooks, including Lineare Algebra , and serves as Editor-in-Chief of Linear Algebra and its Applications . He is a SIAM Fellow, ERC Advanced Grant recipient, and member of prestigious academies like acatech and Academia Europaea. Education: Diplom (1979), Ph.D. (1982), Habilitation (1988) from Universität Bielefeld. Key Roles: Speaker of DFG Research Center MATHEON (2008–2014) President of GAMM (2011–2016) and EMS (2017–2022) Research Highlights: Model reduction, port-Hamiltonian systems, and energy-based modeling Control of complex systems and industrial applications His recent work emphasizes interdisciplinary projects in energy systems (e.g., BMBF-EiFer, Hybrid Energy Networks) and numerical methods for large-scale systems. Mehrmann’s contributions span theoretical advancements and practical implementations, bridging fundamental mathematics with real-world challenges.

Georg LohoView profile

Visiting Professor

• Discrete Geometry
• Combinatorics
• Optimization
• +3 more

Georg Loho is a Visiting Professor at Freie Universität Berlin (FU Berlin), currently acting as head of the Discrete Geometry and Topological Combinatorics group, substituting for Günter Ziegler. He is on leave from his position as an Assistant Professor at the University of Twente in the Discrete Mathematics & Mathematical Programming group. His academic journey includes postdoctoral roles at the London School of Economics (LSE) and EPFL, supported by an ERC Starting Grant, and a PhD from TU Berlin under Michael Joswig. Research Interests: Loho focuses on the interplay between discrete geometry, combinatorics, and optimization, with applications in machine learning, data science, and sustainability. His work bridges theoretical foundations and practical applications, including tropical geometry, algorithm design, and mathematical programming. Articles Trends: Recent publications explore stochastic games, tropical geometry in neural networks, oriented matroids, and optimization algorithms. His work often intersects algebraic geometry, combinatorial optimization, and computational complexity. Grants and Awards: He secured an ERC Starting Grant (2019–2020) and has been involved in projects like the HIM Trimester Program in Bonn. No explicit awards are listed, but his research contributions are widely recognized in discrete mathematics and optimization. Teaching: Loho has taught courses on discrete geometry, sustainability in mathematics, and AI applications. He emphasizes innovative pedagogy, including free open-source course materials and sustainability-focused curricula. Labs/Teams: He leads the Discrete Geometry and Topological Combinatorics group at FU Berlin and collaborates on projects like the MatchTheNet educational game, promoting interactive learning in geometry.

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)

• Statistics
• Probability Theory
• Mathematical Modeling
• +3 more

Ariane Hanebeck is affiliated with the Department of Mathematics at the Technical University of Munich (TUM). Her research focuses on statistical methodologies, probability theory, and their applications in risk analysis and data science. She has contributed to advancements in distribution function estimation, observability of Gaussian models, and vine copula-based modeling. Her work integrates mathematical rigor with practical applications, such as improving flight safety through model correction and addressing challenges in nonlinear system observability. Notable collaborations include projects with the TUM-ICL Mathematical Sciences Hub and participation in research groups like Data Science and Probability Theory. Publications include contributions to the Annals of the Institute of Statistical Mathematics and the International Conference on Information Fusion . Though inactive in certain platforms, her research remains impactful in areas like statistical simulation and risk assessment.

Arnulf JentzenView profile

Professor

• Numerical Analysis
• Machine Learning
• Scientific Computing
• +4 more

Prof. Arnulf Jentzen is a Professor at the University of Münster (Germany) and the Chinese University of Hong Kong, Shenzhen (China). He holds positions in the Faculty of Mathematics and Computer Science (Münster) and the School of Data Science & Shenzhen Research Institute of Big Data (CUHK-Shenzhen). His expertise spans numerical analysis , machine learning , and stochastic processes . Education: PhD in Mathematics (summa cum laude) from Goethe University Frankfurt (2007–2009), Diploma in Mathematics (2004–2007). Former roles include Assistant Professor at ETH Zurich (2012–2019) and Postdoc fellowships at Princeton University (2011–2012) and Bielefeld University (2009–2010). Research focuses on deep learning (optimization landscapes, convergence of SGD), high-dimensional PDEs (overcoming the curse of dimensionality), and stochastic differential equations (numerical approximations). His work bridges computational finance , dynamical systems , and gradient flows . Recent achievements include the ICBS Frontier of Science Award (2024), ERC Consolidator Grant (2022), and the Felix Klein Prize (2020). He leads a research group with over 20 current and former members, including PhD students and postdocs. Publications emphasize Adam optimizer convergence , gradient flow analysis , and deep learning for PDEs . He serves on editorial boards for journals like Communications in Computational Physics and SIAM Journal on Numerical Analysis .

• Numerical Analysis
• Machine Learning
• Scientific Computing
• +3 more

Benno Kuckuck is an Investigator in Mathematics Münster at the University of Münster, affiliated with the Department of Applied Mathematics within the Faculty of Mathematics and Computer Science. His research focuses on numerical analysis, machine learning, and scientific computing, with a particular emphasis on deep learning methods for solving partial differential equations (PDEs) and addressing high-dimensional problems. He is involved in the project T10: Deep learning and surrogate methods under the Topics in Mathematics Münster initiative. His recent publications explore the application of neural networks to overcome the curse of dimensionality in PDEs, analyze error bounds in deep learning approximations, and investigate stochastic differential equations' regularity properties. Collaborations with prominent researchers like Arnulf Jentzen highlight his contributions to theoretical foundations of deep learning and numerical methods. Benno Kuckuck’s work bridges mathematical rigor with computational innovation, advancing both the theoretical understanding and practical implementation of machine learning techniques in scientific computing. His research has implications for fields requiring scalable solutions to complex, high-dimensional problems, such as physics, engineering, and finance.

• Asymptotic Geometric Analysis
• Geometric Probability
• Information-based Complexity
• +3 more

Mathias Sonnleitner is an Erwin Schrödinger Research Fellow at the Institute of Financial Mathematics and Applied Number Theory, Johannes Kepler University Linz, hosted by Friedrich Pillichshammer. Previously, he held postdoctoral positions at the University of Alberta (hosted by Alexander Litvak) and the University of Münster (hosted by Zakhar Kabluchko). He earned his Dr. rer. nat. (PhD) in Mathematics from the University of Passau in 2022 under the supervision of Joscha Prochno and Aicke Hinrichs. His research focuses on asymptotic geometric analysis, geometric probability, and information-based complexity, with applications to convex geometry, stochastic processes, and high-dimensional approximation. Education includes a Dipl.-Ing. (MSc) in Mathematics from Johannes Kepler University Linz (2019) and a BSc in Technical Mathematics from the same institution (2018). His work bridges functional analysis, probability, and numerical methods, with notable contributions to entropy numbers, convex body approximation, and optimal recovery problems. Recent publications address minimal dispersion on spheres, shadows of ℓ p -balls, and random approximation in Hausdorff distance. He has received prestigious awards including the Joseph F. Traub Young Researcher Award (2022) and the Sparda-Bank Dissertation Prize (2023). His teaching includes courses on information-based complexity and convex geometry at the University of Passau and Johannes Kepler University Linz.

• Numerical Mathematics
• Complex Analysis
• Approximation Theory
• +4 more

Dr. Olivier Sète is a Professor in the Department of Mathematics at Ulm University, Germany, serving in the Academic Affairs Committee for Mathematics. His research focuses on numerical mathematics, complex analysis, and their applications in astrophysics and natural sciences. Key areas include approximation theory, Helmholtz equation methods, conformal mapping, potential theory, and gravitational lensing modeling. He has authored/co-authored over 25 publications since 2013, with notable contributions to rational approximation algorithms (e.g., the AAA algorithm), conformal mapping techniques, and stochastic partial differential equations. His work bridges theoretical mathematics with computational methods, addressing challenges in gravitational lensing and wave propagation. Dr. Sète collaborates with institutions globally, including co-authors like Jörg Liesen and Klaus Schiefermayr. His research has been cited extensively in numerical analysis, complex analysis, and astrophysics. He maintains an active private homepage and is affiliated with zbMATH, MathSciNet, and Google Scholar. Current projects include advancing conformal mapping for lemniscatic domains and exploring uncertainties in the Helmholtz equation's wavenumber. He advises on academic affairs and contributes to Ulm University's mathematical curriculum development.

• Artificial Intelligence
• Game Theory
• Multiagent Systems
• +4 more

Amy Greenwald is a Professor affiliated with Brown University. Her research focuses on artificial intelligence, game theory, and multiagent systems. She has contributed extensively to areas such as market design, auction mechanisms, reinforcement learning, and empirical game-theoretic analysis. Her work bridges theoretical foundations with practical applications, including automated negotiation platforms and supply chain management systems. Key contributions include the development of methodologies for analyzing equilibria in complex games, designing efficient mechanisms for resource allocation, and advancing algorithms for multiagent learning. She has collaborated on projects like the TAC Travel Agent competition and the NegMAS negotiation framework. Her publication record spans over 135 works from 1999 to 2025, appearing in top venues such as AAAI, AAMAS, EC, NeurIPS, and Games and Economic Behavior. She has also contributed to textbook writing and editorial roles in the field of autonomous bidding agents.