Prof. Zakhar Kabluchko is a faculty member at the University of Münster, affiliated with the Institut für Mathematische Stochastik and the Mathematics Münster cluster of excellence. His research focuses on stochastic processes, convex and integral geometry, and probabilistic number theory. He holds a professorship and has contributed to high-impact publications in areas like random polytopes, stochastic geometry, and extreme value theory. His research interests include the study of random analytic functions, stochastic processes in high dimensions, and geometric probability. Notably, he has explored beta-star polytopes, Poisson zero cells, and the interplay between convex hulls and random walks. Kabluchko has also investigated applications of stochastic geometry in statistical mechanics and number theory. Recent work includes studies on high-dimensional limit theorems, propagation of chaos in spin systems, and the geometry of random simplices. He collaborates actively with researchers like Christoph Thäle and Vladimir Vysotsky, contributing to advancements in geometric probability and stochastic analysis.
JProf. Dr. Aleksandra Kwiatkowska is a Professor at the University of Münster, affiliated with the Institute for Mathematical Logic and Foundational Research. She is an Investigator in Mathematics Münster and a member of CRC 1442 Geometry: Deformations and Rigidity. Her research focuses on model theory, set theory, and mathematical logic, with particular attention to automorphism groups, Fraïssé limits, and topological dynamics. She holds dual affiliations with the University of Wrocław's Institute of Mathematics. Her work explores the intersection of combinatorial set theory, topological structures, and group theory. Notable projects include Topics in Mathematics Münster T3: Models and Universes. Recent publications analyze embeddings into random posets, projective Fraïssé limits of graphs and trees, and the structure of automorphism groups in diverse mathematical contexts. No scientific awards or grants are explicitly listed in the provided materials. Her research contributes to foundational areas of mathematics, emphasizing rigorous logical frameworks and their applications to geometric and combinatorial problems.
Arne Grauer is a researcher in probability theory at the Department of Mathematics, University of Cologne. His work focuses on geometric random graphs, percolation, and stochastic processes in random environments. He completed his PhD in 2022 under Prof. Peter Mörters, exploring ultrasmallness and chemical distance in scale-free geometric random graphs. Education: PhD in Mathematics (2022, University of Cologne), Master’s in Mathematics (2017, University of Münster), Bachelor’s in Mathematics (2015, University of Münster). Research interests include understanding network structures and dynamics, particularly in scale-free and spatially embedded networks. His studies analyze ultrasmall graph distances, infection spread via contact processes, and preferential attachment models. Publications focus on mathematical physics and network science, with contributions to Communications in Mathematical Physics and Journal of Statistical Physics . He co-organized workshops on random geometric graphs and spatial networks.
Bernard Haasdonk is a Professor at the University of Stuttgart, affiliated with the Institute of Applied Analysis and Numerical Simulation (IANS), part of the Faculty of Mathematics and Computer Science. His research focuses on model reduction techniques for parametrized partial differential equations (PDEs), kernel-based methods, numerical analysis, and machine learning applications in scientific computing. He leads a research group in numerical mathematics and has contributed to software tools like RBMatlab and KerMor. Affiliations: Institute of Applied Analysis and Numerical Simulation, University of Stuttgart Roles: Academic Researcher, Software Developer, Grant Principal Investigator His work bridges numerical simulation, machine learning, and reduced basis methods, addressing challenges in optimal control, fluid dynamics, and biomechanics. Haasdonk has held multiple funded projects, including those on kernel methods for model reduction and certified RB-ML-ROM surrogate models. Research Interests: Model reduction for PDEs, kernel methods, greedy algorithms, numerical analysis, optimal control, and applications in fluid dynamics and porous media. He emphasizes structure-preserving methods for Hamiltonian systems and data-driven approaches for surrogate modeling. Publications: Over 200 articles in journals like SIAM, BIT Numerical Mathematics, and Physica D, focusing on convergence analysis, kernel-based approximation, and reduced-order modeling. Recent trends include adaptive greedy algorithms, symplectic model reduction, and energy-conserving surrogates. Awards: IEEE PerCom 2017 Best Paper Award, Teaching Excellence Awards (2012-2017), and early-career research grants. Grants: DFG-funded projects on model reduction, SimTech Cluster contributions, and collaborations on fuel cells and biomechanics. Teams: Leads the Numerical Mathematics Research Group at IANS, collaborating with interdisciplinary teams on projects like MORCOS (Model Order Reduction of Coupled Systems) and KerMor (Kernel Methods for Model Reduction).
Professor Yaron Ostrover is a faculty member in the Department of Theoretical Mathematics at the School of Mathematical Sciences, Tel Aviv University. His academic journey includes being a von Neumann fellow at the Institute for Advanced Study during the 2018-2020 academic years and previously serving as a C.L.E Moore instructor in the Department of Mathematics at MIT. He received his Ph.D. from Tel Aviv University under the supervision of Leonid Polterovich. His educational background includes: Ph.D. in Mathematics from Tel Aviv University (supervised by Leonid Polterovich) Previous postdoctoral positions including C.L.E Moore instructor at MIT Von Neumann fellow at the Institute for Advanced Study (2018-2020) Professor Ostrover's research focuses on the intersection of geometry, dynamics, and mathematical physics. His primary areas of interest are symplectic geometry and Hamiltonian dynamics, with specific emphasis on the geometry of the group of Hamiltonian diffeomorphisms, Floer and quantum homology, symplectic embeddings, billiards dynamics, interaction between symplectic and convex geometry, asymptotic geometric analysis, and the theory of symplectic measurements. His work bridges abstract mathematical concepts with applications in mathematical physics, creating innovative approaches to understanding geometric structures and dynamical systems. An analysis of his recent publications reveals a consistent focus on symplectic geometry and its connections to other mathematical domains. His research demonstrates a progression from foundational work in symplectic capacities and embeddings to more specialized investigations of billiards dynamics, Lagrangian products, and connections to integrable systems like the Toda lattice. A notable trend is the increasing interdisciplinary nature of his work, connecting symplectic geometry with convex geometry, asymptotic analysis, and mathematical physics. Professor Ostrover has received recognition through prestigious positions including: von Neumann Fellow at the Institute for Advanced Study (2018-2020) C.L.E Moore Instructor position at MIT As a principal investigator, he has led significant research projects including "Symplectic Measurements and Hamiltonian Dynamics" (SYMPLECTIC), an ERC Starting Grant from 2015-2020, and serves as a local coordinator for the "Topology in Dynamics and Physics" (TIDY) program. He has co-organized numerous international conferences including LP-60: Geometry and Dynamics (2023, ETH Zürich), Finite Dimensional Integrable Systems in Geometry & Mathematical Physics (2022, Tel Aviv University), and Topological Data Analysis meets Symplectic Topology (2018, Tel Aviv University). Professor Ostrover is actively involved in teaching at Tel Aviv University, offering courses such as Calculus 1A-3, Analysis on Manifolds, Topics in Geometry Mechanics and Dynamics, Introduction to Dynamical Systems, Introduction to Symplectic Geometry, and Non-Euclidean Geometry. He previously taught Measure and Integration and Calculus at MIT.
Prof. Dr. André Uschmajew is a full professor and holds the Chair of Mathematical Data Science at the Institute of Mathematics, Faculty of Mathematics, Natural Sciences, and Materials Engineering, University of Augsburg, Germany. He has held prominent research and academic positions at institutions including the Max Planck Institute for Mathematics in the Sciences (Leipzig), University of Bonn, and EPF Lausanne. 2022–present: Chair of Mathematical Data Science, University of Augsburg 2017–2022: Research Group Leader, Max Planck Institute MiS Leipzig 2014–2017: Bonn Junior Fellow Professorship, University of Bonn 2013: Ph.D. in Mathematics, TU Berlin His research centers on the theoretical and computational aspects of low-rank tensor and matrix approximations, with deep connections to Riemannian optimization, functional analysis, and high-dimensional scientific computing. He investigates the geometry of low-rank varieties, convergence of alternating algorithms, and applications in data science and dynamical systems. His work combines rigorous mathematical analysis with algorithmic innovation. The recent publications (2023–2025) reflect a strong focus on optimization methods for low-rank structures, dynamical low-rank approximation for PDEs like the Vlasov-Poisson equation, randomized SVD, Sinkhorn-type algorithms with overrelaxation, and Kronecker product operator approximation. Key themes include convergence analysis, algorithmic acceleration, and applications in scientific computing and signal processing. Although no specific awards are listed, his publication record in top-tier journals such as Numerische Mathematik , SIAM Journal on Optimization , and Foundations of Computational Mathematics indicates significant recognition in applied mathematics and numerical analysis. He advises students and researchers in mathematical data science and numerical analysis, though specific advisees are not named. He teaches courses such as Kernel Methods and Linear Algebra II. He has collaborated with leading researchers including Bart Vandereycken, Daniel Kressner, and Wolfgang Hackbusch. His work is supported through institutional affiliations and likely research grants, though specific grants are not listed. He is actively involved in the development of numerical methods for high-dimensional problems, particularly using tensor networks and manifold optimization. He is affiliated with research teams at the University of Augsburg and previously led a group at the Max Planck Institute MiS Leipzig, focusing on mathematical aspects of data science and tensor methods.
V. Arvind is a Professor in the Theoretical Computer Science faculty at the Institute of Mathematical Sciences (IMSc) , Chennai. His research is centered on computational complexity theory, with a focus on structural complexity, randomized and algebraic computation, and quantum information and computation. He explores the deep connections between theoretical computer science and mathematics. Institution: Institute of Mathematical Sciences (IMSc), Chennai School: Theoretical Computer Science Academic Rank: Professor Arvind's research interests include computational complexity, structural complexity theory, algebraic computation, derandomization, and quantum computing. He is particularly interested in the interplay between mathematical structures and computation. His work often bridges theoretical computer science with algebra, combinatorics, and logic. His recent publications, primarily expository articles in the EATCS Bulletin’s Computational Complexity Column, cover a wide range of topics such as robust oracle machines, the Alon-Roichman theorem, noncommutative arithmetic circuits, graph isomorphism, and quantum computation. These works reflect trends in foundational complexity theory, algebraic methods in computation, and the exploration of quantum models. The articles emphasize structural insights, lower bounds, and connections to mathematical disciplines. Professional Service and Editorial Roles: Associate Editor, ACM Transactions on Computation Theory Editor, EATCS Computational Complexity Column (since June 2011) Editorial Board Member, International Journal of Computer Mathematics (2009–2013) Co-organizer, ICM Satellite Conference on Algebraic and Probabilistic Aspects of Combinatorics and Computing Program Committee Member for WALCOM 2014, STACS 2012, COCOON 2009, FSTTCS (multiple years, including chair roles), CCC 2006, INDOCRYPT (2002, 2005), and others Teaching: Arvind has taught advanced courses including Computational Complexity, Algorithms, Algebra and Computation, and Discrete Mathematics, often based on foundational texts and notes from leading experts. Lecture notes from his courses have been compiled by students and collaborators. Collaborations: He has an extensive list of co-authors, including prominent researchers such as Manindra Agrawal, Eric Allender, Johannes Köbler, Meena Mahajan, Jacobo Torán, and Ramprasad Saptharishi, indicating strong collaborative research networks in complexity theory and algorithms.
Laxman Dhulipala is an Assistant Professor in the Department of Computer Science at the University of Maryland, College Park, and a research scientist at Google Research in the Graph Mining team. He holds a Ph.D. from Carnegie Mellon University, advised by Guy Blelloch, and was a postdoctoral researcher at MIT with Julian Shun. Ph.D., Carnegie Mellon University Postdoctoral Research, MIT His research focuses on efficient parallel algorithms, particularly for graph processing and clustering. He explores theoretical and practical models of parallel computation aligned with modern hardware. His work spans parallel graph algorithms, computational geometry, and scalable systems for massive datasets. The recent publications demonstrate a strong trend in scalable and dynamic graph algorithms, with a focus on hierarchical clustering, connectivity, and benchmarking. Key themes include batch-dynamic updates, memory-efficient data structures, and practical parallel implementations for massive-scale problems. Many works appear in top venues such as SPAA, VLDB, NeurIPS, and SIGMOD. Best Paper Award at VLDB'25 Best Paper Award at SPAA'22 Best Paper Runner Up at VLDB'22 Distinguished Paper Award at PLDI'19 Best Paper Award at SPAA'18 Memorable Paper Award Finalist at NVMW'20 Honorable Mention, CMU SCS Dissertation Award Nominated for ACM Dissertation Award Dhulipala has advised and collaborated with numerous students and researchers, including Quinten De Man, Shangdi Yu, Jessica Shi, and others, contributing to influential projects such as Aspen, ParGeo, GBBS, and ParlayLib. He has received recognition for both theoretical and practical contributions to parallel computing. He teaches courses such as CMSC858N (Scalable Parallel Algorithms and Data Structures) and CMSC451 (Design and Analysis of Computer Algorithms). He is actively involved in building tools and frameworks for parallel algorithm development and evaluation, including benchmark suites and graph processing systems. His dual affiliation with academia and Google Research enables impactful, scalable research bridging theory and practice.
Artem Sapozhnikov is a Professor at the Mathematisches Institut of Universität Leipzig, Germany. His research focuses on probability theory, particularly percolation, random walks, and random graphs. He maintains an active research program with numerous publications and supervises PhD students and postdocs. Professor Sapozhnikov teaches a range of mathematics courses for physicists, including Mathematics for Physicists 1-4, Probability Theory I & II, and specialized courses on percolation. His teaching spans both undergraduate and graduate levels, demonstrating his commitment to mathematical education. His research interests center around probability theory , with specific focus on: Percolation theory and phase transitions Random walks and their properties on various structures Random interlacements and Brownian motion Boolean models and geometric probability Models with long-range correlations His work bridges theoretical probability with applications in statistical physics. Analysis of his recent publications shows a strong focus on the properties of random interlacements, Brownian motion, and percolation models. His research examines connectivity properties, phase transitions, and geometric aspects of these stochastic processes. Recent work with Yingxin Mu has explored visibility windows and indistinguishability of components in various models. Earlier collaborations with Caio Alves, Deepan Basu, and Yinshan Chang investigated decoupling inequalities, crossing probabilities, and loop percolation. Professor Sapozhnikov has supervised several PhD students and postdocs, including: Deepan Basu (PhD 2013-2017) Caio Alves (postdoc 2016, 2017-2020) Yinshan Chang (postdoc 2013-2016) Lorenzo Taggi (PhD 2012-2015) Yingxin Mu (postdoc 2021-2023) He currently has open positions for PhD students through the IMPRS program.
Eva Kopfer is an Associate Professor at the University of Bonn's Institute for Applied Mathematics, Department of Stochastic Analysis. Her research focuses on stochastic analysis, optimal transport theory, Ricci flows, and geometric analysis. She has led courses on stochastic analysis, financial mathematics, and advanced calculus. Notable contributions include work on exponential ergodicity for kinetic SDEs, stochastic homogenization of transport problems, and quantum gravity measures on manifolds. Her research interests bridge probability theory, differential geometry, and functional analysis, with applications to geometric flows and metric measure spaces. Recent work explores conformally invariant random fields, polyharmonic structures in quantum gravity, and generalized Ricci flow dynamics. She has collaborated extensively on projects involving discrete-to-continuous limits in optimal transport and Liouville quantum gravity measures. Publications highlight advancements in stochastic differential equations, ergodic theory, and geometric PDEs. Her work often integrates probabilistic methods with geometric analysis, yielding insights into transport phenomena and manifold structures. Current research themes include non-equilibrium systems, functional inequalities, and random geometric structures. Eva Kopfer's academic contributions are evident in high-impact journals like Journal of Functional Analysis and Communications in Pure and Applied Mathematics. She actively engages in seminar series and lecture series at the University of Bonn, covering topics from stochastic calculus to frontiers in economics and mathematics.
Prof. Dr. Moritz Kaßmann is affiliated with the Faculty of Mathematics at Bielefeld University, Germany. His academic career spans institutions such as the University of Bonn, University of Connecticut, and Wroclaw University of Technology. Professor (W3), Bielefeld University (2016–present) Speaker of GRK 2235 (2016–present) Subproject Leader in SFB 1283 (2017–2025) Education: Doctorate (2001), University of Bonn Habilitation (2007), University of Bonn Studies at University of Bonn (1994–1997), St. Petersburg State University (1993–1994), and University of Konstanz (1991–1993) Research Interests: Moritz Kaßmann specializes in nonlocal operators, stochastic processes, and their applications in partial differential equations. His work addresses regularity theory, Harnack inequalities, and degenerate integrodifferential operators. He explores connections between singular systems, random structures, and analysis on fractals. Article Trends: His recent publications focus on nonlocal kinetic equations, parabolic Harnack inequalities, and robust Hölder estimates. Key themes include the failure of classical assumptions in nonlocal settings, trace theorems for degenerate operators, and geometric analysis of irregular systems. Scientific Awards: Alexander von Humboldt Research Fellowship (2014) Foundation for Polish Science Fellowship (2014) Advising and Grants: He supervises students in projects like GRK 2235 and SFB 1283, funded by the German Research Foundation (DFG). His research investigates regularity properties of solutions to nonlocal equations and numerical methods for degenerate operators. Labs/Teams: Kaßmann collaborates with international experts in the International Research Training Group (IRTG) between Bielefeld and Seoul National University, focusing on singular and random systems in mathematics.
Professor Melanie Schmidt is a faculty member in the Department of Computer Science at Heinrich-Heine-Universität Düsseldorf, where she leads the Algorithms and Data Structures research group. Previously, she was affiliated with the University of Bonn's Institute of Computer Science, where she completed her PhD under Prof. Dr. Heiko Röglin and headed a subgroup on "clustering for big data" within his research group. Current Position: Professor at Heinrich-Heine-Universität Düsseldorf Previous Position: Researcher and lecturer at University of Bonn PhD Advisor: Prof. Dr. Heiko Röglin Her research focuses on geometric data analysis, particularly k-means clustering in data streams, combinatorial optimization, and approximation algorithms. Her work bridges theoretical computer science with practical applications in big data processing. She has made significant contributions to understanding the theoretical foundations of clustering algorithms while developing efficient implementations for real-world applications. Professor Schmidt's publication record shows a consistent evolution from theoretical analysis of k-means to practical implementations for big data environments. Her recent work explores fairness in clustering, privacy-preserving techniques, and efficient algorithms for high-dimensional data. She has published in top-tier conferences including SODA, ICALP, ESA, and ITCS, demonstrating both theoretical rigor and practical relevance. Best Student Paper Award at ESA 2012 (joint work with Martin Groß, Jan-Philipp W. Kappmeier, and Daniel Schmidt) She actively supervises numerous Master's and Bachelor's students, with current advisees including Lena Carta, Lukas Drexler, and Anna Arutyunova. Her research group includes members such as Anja Rey, Julian Wargalla, and Annika Hennes. She teaches advanced courses in algorithms and data structures, with a focus on randomized algorithms and efficient algorithm design for big data problems. Professor Schmidt leads the Algorithms and Data Structures research group at Heinrich-Heine-Universität Düsseldorf, which focuses on developing and analyzing efficient algorithms for fundamental computational problems, with particular emphasis on clustering, geometric data analysis, and big data applications.
Daniel Peralta-Salas is a CSIC Research Professor and Chair of the Group 'Differential Geometry and Geometric Mechanics' at the Institute of Mathematical Sciences (ICMAT) in Madrid, Spain. ICMAT is a joint research institute of the Spanish National Research Council (CSIC) and several Madrid universities. He is an active researcher with leadership roles in the ICMAT board and multiple international advisory boards. PhD in Mathematics, Universidad Complutense de Madrid, 2006 His research lies at the intersection of dynamical systems , partial differential equations , and differential geometry , with applications in fluid mechanics, plasma physics, quantum mechanics, and mathematical physics. He has developed a unifying theory to study geometrically complex structures in physical models. His work includes proving long-standing conjectures by Arnold, Lord Kelvin, and Michael Berry, and constructing fluid flows that simulate universal Turing machines, thereby demonstrating undecidable dynamics in fluid particle paths. The most recent articles highlight his ongoing focus on steady Euler flows , topological obstructions in magnetic fields , optimal eigenvalue problems , and Turing universality in fluid dynamics . His publications span geometric analysis, spectral theory, and mathematical physics, often combining deep topological insight with PDE techniques. He has developed novel methods such as global approximation and inverse localization, which have broad implications across mathematical physics. His scientific awards include: ERC Starting Grant (2014–2019) Barcelona Dynamical Systems Prize (2015) Plenary Speaker, European Congress of Mathematics (2016) The Floer Lectures (2019) MINT Distinguished Lectures, Tel Aviv (2020) Plenary Speaker at major national and international mathematical societies (2021–2024) EMS Distinguished Speaker (2023) Peralta-Salas advises and collaborates extensively with researchers including Alberto Enciso, Robert Cardona, Eva Miranda, and others. His work has been supported by prestigious grants such as the ERC Starting Grant. He has published over 100 papers in top journals including Annals of Mathematics , Acta Mathematica , PNAS , and Physical Review Letters . He is a member of editorial boards for journals such as Revista Matemática Iberoamericana and Journal of Dynamics and Differential Equations , and serves on the scientific advisory board of IMTECH (UPC-Barcelona Tech). He leads the research group on Differential Geometry and Geometric Mechanics at ICMAT, which focuses on geometric structures in dynamical systems and PDEs. The group explores topics like Beltrami fields, vortex dynamics, and topological aspects of eigenfunctions. Their work is highly collaborative and interdisciplinary, bridging pure mathematics and theoretical physics.
Paul Breiding is a Professor for Mathematical Methods in Data Science at the University of Osnabrück, within the Faculty of Mathematics/Computer Science/Physics. He is part of the Applied Algebra and Data Analysis working group and the Research Unit Data Science. His research focuses on nonlinear algebra, metric algebraic geometry, and their applications in numerical methods and data science. He is a Fellow of the Junge Akademie Mainz and co-authored the book 'Metric Algebraic Geometry' with Kathlen Kohn and Bernd Sturmfels. His work includes developing the software HomotopyContinuation.jl (v.2.11), which is widely used for numerical algebraic geometry. His research interests span algebraic geometry, tensor decompositions, and computational methods. Recent publications investigate geometric properties of algebraic varieties, condition numbers in tensor approximations, and probabilistic aspects of algebraic structures. Key contributions include studies on the reach of algebraic manifolds, typical ranks of random tensors, and sensitivity analysis in numerical algorithms. His work bridges theoretical algebraic geometry with practical computational tools for data science applications. Software: HomotopyContinuation.jl (v.2.11) Labs/Teams: Applied Algebra and Data Analysis, Research Unit Data Science
Robert Stelzer is a Professor and Head of the Institute of Mathematical Finance at Ulm University. His research focuses on stochastic processes, financial mathematics, and statistical methodologies. He has supervised numerous PhD students and organized international scientific events. Key contributions include work on multivariate stochastic volatility models, Lévy processes, and time series analysis. Awards include the Förderpreis and Promotionspreis for his doctoral work. He holds editorial roles in leading journals and actively participates in academic service. Research interests span financial mathematics, stochastic volatility, and extreme value theory. His publications explore CARMA processes, supOU models, and geometric ergodicity. Teaching includes courses on financial mathematics, stochastic analysis, and econometrics. Supervised students have contributed to advancements in stochastic finance and statistical theory. Organized events include workshops on extreme value theory and financial mathematics. Editorships include Statistics and Risk Modeling, reflecting his leadership in statistical research. His work bridges theoretical probability and practical applications in finance and risk management.
