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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)

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.

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 .

Chiranjib MukherjeeView profile

Professor

• Stochastic Analysis
• Large Deviations
• Stochastic Partial Differential Equations (SPDEs)
• +5 more

Prof. Dr. Chiranjib Mukherjee is a Professor of Mathematics at the University of Münster, affiliated with the Institute of Mathematical Stochastics. He holds a leadership role as an Investigator in Mathematics Münster and contributes to the Collaborative Research Center (CRC) projects on Groups, Geometry & Actions. His research focuses on stochastic analysis, large deviations, stochastic PDEs, and applications in statistical mechanics. Education and Academic Background: While specific degree details are not explicitly listed, his academic trajectory includes postdoctoral work at institutions like the Courant Institute (NYU) and his current role as a full professor indicates advanced academic qualifications. Research Interests: His work bridges probability theory and mathematical physics, addressing topics like directed polymers, stochastic homogenization, percolation, and geometric group theory. Recent projects include studies on polaron measures, Gaussian multiplicative chaos, and the interplay between stochastic processes and geometric structures. Publications: Over 30 peer-reviewed articles since 2016, appearing in journals such as Annals of Probability , Communications on Pure and Applied Mathematics , and Probability Theory and Related Fields . Key contributions address the effective mass of polarons, large deviation principles for random walks, and SPDEs in disordered media. Teaching & Supervision: Supervised over 10 master's and bachelor's theses on topics like Liouville first passage percolation, electrical networks, and machine learning applications. Teaches advanced courses on stochastic analysis, Markov processes, and probability theory on groups/networks. Labs/Teams: Leads the research group in stochastic analysis at the Institute of Mathematical Stochastics, collaborating with postdocs like Konstantin Recke and PhD students such as Luzie Kupffer. Active in organizing conferences on probability, dynamics, and group theory.

• 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.

• Computational Geometry
• Discrete Geometry
• Convex Optimization
• +6 more

Francisco Criado is a researcher with significant contributions to computational geometry, discrete mathematics, and applied algorithm design. His work spans theoretical and applied domains, including convex optimization, Voronoi diagrams, and geometric modeling in gas dynamics and earthquake decision-making frameworks. Collaborators : Francisco Santos, Sebastian Pokutta, Michael Joswig Key Venues : Discrete & Computational Geometry , Foundations of Computational Mathematics , NeurIPS Research Interests focus on geometric algorithms, polyhedral complexes, and applications of fuzzy logic. His 2025 papers explore convex hulls, zonotopes, and tropical geometry, while earlier works address gas dynamics and decision-making models. Article Trends reveal expertise in high-dimensional geometry, combinatorial optimization, and numerical methods. He frequently employs randomized and linear convergence algorithms in 2022–2025. Advising and Grants : No explicit data provided, but his extensive co-authorship network suggests collaborative research leadership.

• Computational Algebra
• Commutative Algebra
• Applied Algebraic Geometry
• +3 more

Daniele Taufer is a postdoctoral researcher affiliated with the NUMA research unit at KU Leuven (Belgium) since 2022, supported by the FWO (Flemish Fund for Scientific Research) under project 12ZZC23N. Previously, he worked at CISPA (Germany) from 2020 to 2022 as a postdoc on elliptic curve cryptography within the ERC-669891 project, supervised by Antoine Joux. Education: Ph.D. in Mathematics (2016–2020), University of Trento (IT), cum laude, thesis: "Elliptic Loops" (supervised by Massimiliano Sala) Master in Mathematics (2014–2016), University of Duisburg-Essen (DE) and University of Leiden (NL) via the ALGANT double-degree program, thesis: "Algebraic aspects of the Number Field Sieve" (supervised by Hendrik W. Lenstra) Bachelor in Mathematics (2011–2014), University of Padova (IT), thesis: "Gröbner bases and applications" (supervised by Alberto Tonolo) Research interests span computational and commutative algebra, applied algebraic geometry, and cryptographic applications. Key areas include symmetric tensor decomposition (Waring, tangential, Chow, cactus ranks), effective decomposition algorithms (apolarity, Hankel operators), and algebro-geometrical properties of apolar schemes. His work bridges theoretical algebra and practical cryptography, particularly focusing on elliptic curves and their applications in blockchain and isogeny-based systems. Recent scientific contributions examine decompositions of symmetric tensors, elliptic curve discrete logarithm problems (ECDLP), and group structures over discrete rings. His algorithmic developments leverage apolarity and Hankel operators for computational efficiency. Scientific accolades: Maître de conférences qualification (2025) in Mathematics and Applied Mathematics sections Member of the SIAM Activity Group on Algebraic Geometry Labs and teams include the NUMA group at KU Leuven and the ERC-669891 project at CISPA.

Ehrhard BehrendsView profile

Professor

• Functional Analysis
• Probability Theory
• Stochastic Processes
• +4 more

Prof. Ehrhard Behrends is a Professor of Mathematics at the Department of Mathematics and Computer Science, Freie Universität Berlin. His research focuses on functional analysis, probability theory, and stochastic processes. He is also deeply involved in mathematical education and public outreach, having authored numerous popular mathematics books and articles. Key roles include leading the European Mathematical Society's Raising Public Awareness committee (2009–2015) and developing the mathematics portal mathematics-in-europe.eu . Behrends has organized major exhibitions like 'Mathematics for all the Senses' and contributed to initiatives such as the Year of Mathematics 2008. His work bridges advanced research with accessible communication, reflected in his books on Markov chains, analysis textbooks, and the Five Minutes of Mathematics column.

Matthias KellerView profile

Professor

• Graph Theory
• Spectral Theory
• Discrete Geometry
• +6 more

Prof. Dr. Matthias Keller is a leading researcher in discrete spectral theory and graph analysis, affiliated with the Institute of Mathematics at the University of Potsdam since 2015. His work bridges geometric properties of graphs with spectral theory, focusing on Dirichlet forms, Schrödinger operators, and functional inequalities. Key Collaborations : Daniel Lenz, Radoslaw Wojciechowski, Yehuda Pinchover Books Authored : Graphs and Discrete Dirichlet Spaces (Springer, 2021) His research explores non-positively curved graphs, stochastic completeness, and magnetic sparseness. Recent projects include optimal Hardy inequalities and spectral analysis of fractional Laplacians. Scientific Awards : Swiss Fellowship (2023) Golda Meir Fellowship (2012-2013) Klaus Murmann PhD Fellowship (2007-2010) He advises PhD and Master’s students such as Yannik Thomas , Matti Richter , and Philipp Bartmann , while maintaining active roles in DFG-funded projects and international workshops.

• Mathematical Analysis
• Stochastic Analysis
• Metric Geometry
• +4 more

Dr. Michael Hinz is a researcher at Bielefeld University's Faculty of Mathematics with significant involvement in multiple research initiatives. He is affiliated with the International Research Training Group 2235 as a participating scientist and contributes to the Collaborative Research Center 1283 (SFB 1283) project titled 'Taming uncertainty and profiting from randomness and low regularity in analysis, stochastics and their applications,' specifically working on subproject A3: 'Analysis of manifolds, metric spaces and graphs.' Additionally, he is a member of the Bielefeld Graduate School in Theoretical Sciences academic staff. His research focuses on the intersection of mathematical analysis and stochastic processes, with particular emphasis on geometric structures. Dr. Hinz maintains offices at locations UHG V4-239 and UHG V3-237 on campus and can be reached through the Faculty of Mathematics secretariat at +49 521 106-4773. Research interests include: Analysis on manifolds and metric measure spaces Stochastic processes in geometric settings Spectral theory on graphs and fractals Dirichlet forms and potential theory Geometric analysis with applications to mathematical physics Dr. Hinz contributes to the 'Mathematical World' strategic research area at Bielefeld University, which develops fundamental mathematical concepts and theories with applications to solve long-standing open problems in economics and the natural sciences. His work aligns with the university's interdisciplinary research culture, particularly connecting mathematical theory with applications in complex systems.

Niv BuchbinderView profile

Professor

• Combinatorial Optimization
• Online Algorithms
• Algorithmic Game Theory
• +2 more

Prof. Niv Buchbinder is a faculty member in the Department of Statistics and Operations Research at the School of Mathematical Sciences, Tel Aviv University. His research centers on algorithmic solutions for combinatorial optimization in offline and online contexts, with significant contributions to primal-dual methodologies and algorithmic game theory. His academic background includes a Ph.D. in Computer Science from the Technion (2008) under Prof. Seffi Naor and an M.Sc. in Computer Science from the Technion (2003) under Prof. Erez Petrank. Key research areas encompass Combinatorial Optimization, Online Algorithms, Algorithmic Game Theory, Primal-Dual Methods, and Submodular Optimization, focusing on competitive analysis for problems like set cover, ad-auctions, and caching. Recent publications (2012-2015) reveal a concentrated effort in submodular optimization and online decision-making, with applications in advertising, resource allocation, and machine learning. These works consistently employ primal-dual frameworks to achieve strong competitive ratios in adversarial settings. Scientific recognition includes: Best Paper Award at ESA 2007 for “Online Primal-Dual Algorithms for Maximizing Ad-Auctions Revenue” Best Paper Award at FOCS 2011 for “A Polylogarithmic Competitive Algorithm for the k-Server Problem” No information is available regarding student advising or research grants. Similarly, details about laboratory facilities, research teams, or future projects are not provided in the source materials.

Oleg IvriiView profile

Senior Lecturer

• Complex Analysis
• Conformal Geometry
• Thermodynamic Formalism
• +3 more

Prof. Oleg Ivrii is a Senior Lecturer in the Department of Theoretical Mathematics at Tel Aviv University's Faculty of Exact Sciences. He earned a B.Sc. in Mathematics from the University of Toronto (2009) and a Ph.D. in Mathematics from Harvard University (2014). Following postdoctoral research positions at the University of Helsinki (2014-2016) and California Institute of Technology (2016-2019), he joined Tel Aviv University in 2019. Research Focus: Complex analysis, conformal geometry, thermodynamic formalism, and geometric function theory. Key Contributions: Studies on analytic mappings of the unit disk, inner functions, quasiconformal homogenization, and Makarov's principle. His work explores critical structures of inner functions, stable convergence, and applications to dynamical systems. He has advised Emanuel Sygal (Master's thesis) and collaborated with Artur Nicolau, Mariusz Urbański, and Vladimir Marković. Ivrii's publications appear in journals like Inventiones Mathematicae , Journal of Differential Geometry , and Analysis & PDE .

Bin GaoView profile

Associate Professor

• Riemannian Optimization
• Tensor Computation
• Parallel/Distributed Computing
• +5 more

Bin Gao is an Associate Professor at the Academy of Mathematics and Systems Science (AMSS), Chinese Academy of Sciences. He holds a Ph.D. in Applied Mathematics (2019, University of Chinese Academy of Sciences) and a B.Sc. in Mathematics (2014, Sichuan University). His postdoctoral experience includes positions at UCLouvain (2019-2021) and the University of Münster (2021-2022). Research Interests: Riemannian optimization, tensor computation, parallel/distributed algorithms for orthogonality constraints, machine learning applications. Key Contributions: Development of retraction-free methods on Stiefel manifolds, preconditioned Riemannian algorithms, and geometric frameworks for symplectic eigenvalue problems. Article Trends: Recent work focuses on overcoming the curse of dimensionality via manifold-based optimization, including distributed algorithms for Stiefel manifolds, graph-regularized tensor completion, and second-order methods for symplectic structures. Keywords span numerical analysis, quantum information, and machine learning. Scientific Awards: 2021 Zhong Jiaqing Mathematics Award 2018 Best Student Paper Award (CSIAM) 2018 CAS Special President Scholarship 2017 National Scholarship for Doctoral Students (China) 2016 Honor Student Award (International Workshop on Modern Optimization and Application) Advising & Collaborations: Collaborates with researchers from UCLouvain, University of Münster, and AMSS. Mentors students in Riemannian optimization and tensor computation. Leads the popman research group.

Simon ApersView profile

Researcher

• Quantum Algorithms
• Theoretical Computer Science
• Random Walks
• +4 more

Simon Apers is a CNRS researcher at IRIF (Institut de Recherche en Informatique Fondamentale) at Université Paris Cité. He focuses on quantum algorithms with broader interests in theoretical computer science, including random walks, graph theory, and combinatorial optimization. His primary research interests span quantum computing, theoretical computer science, and algorithms. Apers has made significant contributions to quantum walks, quantum property testing, graph algorithms, and computational complexity. His work often bridges quantum information with classical theoretical computer science, exploring how quantum techniques can improve classical algorithms and solve problems more efficiently. He has developed quantum speedups for various computational tasks including sampling, optimization, and graph problems. Simon Apers serves as a program committee member for prestigious conferences including TQC'25, QIP'25, TQC'23, SODA'23, and ESA '21. He also serves as an editor for the journal Quantum. His teaching activities include courses at Sorbonne University (2022-present) on Advanced Quantum Algorithms, MPRI (2021-present) on Quantum Algorithms and Complexity, and Bad Honnef (2022). His publication record shows a strong trend toward quantum algorithms for graph problems, quantum walks, and connections between quantum computing and classical theoretical computer science. He frequently collaborates with researchers from various institutions across Europe and has published in top venues including FOCS, STACS, ESA, PRL, and JMLR. Simon Apers actively mentors students and has open positions for PhD students and postdocs in quantum algorithms at IRIF. His research group is involved in cutting-edge work at the intersection of quantum computing and theoretical computer science, with significant contributions to quantum walks, property testing, and computational complexity.