Prof. Dr. Nina Gantert is a distinguished Professor of Probability Theory at the Technical University of Munich (TUM) , affiliated with the TUM School of Computation, Information and Technology . She has held faculty positions at Karlsruhe Institute of Technology and the University of Münster prior to joining TUM in 2011. Her research focuses on probability theory , particularly stochastic processes , large deviations , and random media . She investigates random walks in random environments as models for transport in disordered systems and explores applications in physics and biology . Recent publications highlight her work on branching random walks , mixing times , biased random walks , and large deviation principles for complex stochastic systems. She has co-authored studies on random walks in dynamical percolation , interacting edge-reinforced processes , and extremal point processes in branching models. Scientific Awards: Elected fellow of the IMS (2016) Her academic career spans institutions including ETH Zürich, University of Bonn, Technical University of Berlin, and TUM. She has supervised numerous Bachelor’s and Master’s theses on topics ranging from mixing time analysis to percolation theory , often collaborating with international co-authors.
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Prof. Dr. Gerold Alsmeyer is a faculty member at the Institute of Mathematical Stochastics, Department of Mathematics and Computer Science, University of Münster. He is an active researcher with a focus on stochastic processes, particularly stochastic fixed-point equations and iterated function systems. His work is supported by his role as an Investigator in Mathematics Münster in the project EXC 2044 - C1: Evolution and asymptotics. His primary research interests include the theory of stochastic processes, branching processes, Markov random walks, renewal theory, and the asymptotic analysis of random structures such as random trees and polytopes. He has made significant contributions to the understanding of fluctuation theory, perpetuities, and the smoothing transform. His recent publications (2017–2023) reveal a sustained focus on theoretical probability, with recurring themes in random difference equations, iterated function systems, and limit theorems for stochastic processes. The work spans pure mathematical theory and applications in mathematical biology and combinatorics, indicating a broad yet deep research profile. Prof. Alsmeyer has supervised numerous doctoral and master’s students, including Viet Hung Hoang, Christopher Eick, Philipp Godland, and Fabian Buckmann, whose dissertations cover topics in branching processes, random walks, and stochastic fixed-point equations. He has no listed scientific awards in the provided texts. He teaches courses in probability theory, mathematical statistics, branching processes, and stochastic recursion equations, demonstrating a strong commitment to academic mentoring and education.
Michel Painمشاهده پروفایل
پژوهشگر
Michel Pain is a CNRS Researcher at the Toulouse Mathematics Institute (Université de Toulouse). Previously, he held a Courant Instructor position at NYU's Courant Institute of Mathematical Sciences (2019-2021) and completed his PhD in probability theory at Sorbonne Université under Zhan Shi, focusing on branching Brownian motion. His research spans log-correlated fields , including branching Brownian motion/random walks, Derrida-Retaux models, and β-ensembles. He studies extremal statistics , stochastic structures , and phase transitions in hierarchical systems. Michel has published extensively on branching processes , weighted trees , and log-correlated random matrices , with recent work on supercritical phase overlaps (2025) and height asymptotics for weighted trees (2024). His supervised students include PhD candidate Louis Chataignier and several Bachelor/Master thesis authors. He currently teaches the Master 2 course Branching Processes with Pascal Maillard, having previously taught advanced probability at Université Toulouse III, complex analysis at NYU, and integration theory at ENS Paris.
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.
Prof. Dr. Matthias Meiners is a faculty member at Justus-Liebig-University Giessen, holding the Chair of Stochastics within the Mathematical Institute. His research focuses on Asymptotic statistics Branching processes Limit theorems Random walks in random environments Regenerative processes Renewal theory Statistical mechanics . Recent publications analyze complex stochastic phenomena across branching processes, random walks, and renewal equations, with 2025 preprints on CMJ explosions and Markov renewal expansions. 2024 works examine active Brownian particle dynamics and supercritical branching fluctuations. Earlier studies address kinetic equations, martingale convergence, and percolation model speeds. Contact: Email: Matthias.Meiners@math.uni-giessen.de Phone: 0641 99-32100 Office: Room 216, Arndtstr. 2, 35392 Gießen
Prof. Dr. Anita Winter is a Professor at the University of Duisburg-Essen within the Faculty of Mathematics. Her research focuses on Probability Theory, Stochastic Processes, and their applications in areas like Mathematical Biology and Statistical Physics. She holds a prominent position in the field, with extensive contributions to branching processes, measure-valued processes, and tree-valued stochastic dynamics. Her work integrates algebraic and geometric structures with probabilistic methods, such as algebraic measure trees and pruning processes. She has published extensively in leading journals like *Stochastic Processes and their Applications* and *The Annals of Probability*. Her research often explores the interplay between population dynamics and spatial structures, including studies on coalescent processes, random graphs, and evolutionary models. Anita Winter has taught advanced courses in Probability Theory, Levy processes, and stochastic analysis across multiple semesters, reflecting her deep engagement with both research and education. Her team assistance, led by Dagmar Goetz, supports her academic activities and administrative coordination.
Pascal Maillard is a Professor at the Department of Mathematics at Université Toulouse III - Paul Sabatier, affiliated with the Institut de Mathématiques de Toulouse (CNRS UMR5219). He has been a Junior member of the Institut Universitaire de France since October 2021. His research focuses on probability theory, particularly branching random walks, multiplicative cascades, and random energy models, with applications in statistical mechanics and mathematical physics. Maillard has coordinated the ANR-DFG funded project REMECO (2021-2024), investigating extreme value distributions, partition functions at complex temperatures, and optimization algorithms in random energy models. He has also co-organized the annual 'Les probabilités de demain' conference (2016–2019) to support early-career researchers in probability. His teaching spans advanced modules in stochastic modeling, probability theory, and mathematical statistics at both undergraduate and graduate levels. He is currently developing lecture notes on branching random walks and multiplicative cascades based on his Master's course at Université Paris-Sud. Research Interests: Extremal processes in branching systems, random energy landscapes, stochastic optimization, and applications to statistical physics. Awards: Junior member of Institut Universitaire de France (2021–present). Advising: Supervised three PhD students and multiple research projects in probability theory, including studies on SLE, Markov chain mixing times, and Erdős-Rényi graphs. Labs/Teams: Co-lead of REMECO project with Lisa Hartung, involving institutions in Toulouse and Mainz.
Prof. Dr. Alexander Drewitz is a Professor in the Department of Mathematics and Computer Science at the University of Cologne, specifically within the Division of Mathematics. His research focuses on probability theory, with primary interests in percolation theory, random geometric structures, transport processes in random media, and applications of high-dimensional probability to data science. He has held prior positions as an ETH Fellow at ETH Zurich and as a J.F. Ritt Assistant Professor at Columbia University. His work bridges theoretical advancements with practical applications, particularly in understanding critical phenomena and stochastic processes in complex systems. Education: Doctorate in Mathematics (details not explicitly provided in text). Research Interests: Prof. Drewitz explores percolation models, Gaussian free fields, random interlacements, and concentration of measure phenomena. His studies often address geometric and dynamic properties of random structures, with implications for data science and statistical physics. Recent work includes investigations into cluster volumes, arm exponents, and universality classes in percolation models. Publications Trends: His 15 most recent articles (2008–2025) reflect a focus on Gaussian free fields, random interlacements, branching processes, and long-range correlations. Key themes include percolation thresholds, invariance principles, and high-dimensional asymptotics. Grants & Activities: Organized conferences like 'Long-range phenomena in percolation' (2024) and 'Geometric and Topological Properties of Random Algebraic Varieties' (2023). Co-founded the Scientific Network on Stochastic Processes on Evolving Networks. Labs/Teams: Active in the Center for Data and Simulation Science at Cologne, contributing to interdisciplinary research on random geometry and data-driven methods. Leads the stochastics seminar and Bonn Cologne Mathematics-Physics seminar.
Dominik Schmid is a junior professor at the University of Augsburg , affiliated with the Faculty of Mathematics, Natural Sciences and Technology and the Institute of Mathematics under the Chair of Stochastics and Its Applications . Prior to this role, he held postdoctoral positions at Columbia University , University of Bonn , and Princeton University . His research focuses on probability theory , particularly mixing times for Markov chains , limit theorems for interacting particle systems , and stochastic processes in random media . Education: PhD in Mathematics, Technical University of Munich (2021) Research Trends: His work on exclusion processes , random walks in dynamic environments , and fragmentation models has been published in leading journals such as Annals of Probability , Communications in Mathematical Physics , and Annals of Applied Probability . Key themes include non-equilibrium statistical mechanics , random graph dynamics , and hydrodynamic limits . Scientific Contributions: Developed sharp mixing time bounds for exclusion processes in various boundary conditions Extended cutoff theory to tree structures and Galton-Watson random media Derived limit profiles for the Bernoulli-Laplace urn and ASEP models Academic Engagement: Organizes workshops at institutions like the Simons Center and Oberwolfach , and has delivered invited talks in Poland , Hungary , Italy , and China . Teaches advanced courses on discrete probability and KPZ universality at the University of Augsburg.
Yuichi Yoshida is a Professor at the National Institute of Informatics (NII), affiliated with the Principles of Informatics Research Division. He also holds a concurrent position as Senior Researcher at Preferred Networks. His academic career at NII spans from Assistant Professor (2012–2015), Associate Professor (2015–2022), to full Professor since 2022. He serves as Vice Director of the Global Research Center for Big Data Mathematics at NII and has held advisory and research roles at Preferred Infrastructure and the Ministry of Education, Culture, Sports, Science and Technology (MEXT). Ph.D. in Informatics, Kyoto University, 2012 Master of Informatics, Kyoto University, 2009 Bachelor of Engineering, Kyoto University, 2007 His research focuses on theoretical computer science, particularly property testing , approximation algorithms , sublinear-time algorithms , and constraint satisfaction problems . He investigates how theoretical insights can be applied to real-world graphs, with recent emphasis on average sensitivity of algorithms and Lipschitz continuity in combinatorial optimization . His work bridges theory and practical scalability in large network analysis and machine learning. The most recent 15 publications (2022–2025) demonstrate a strong trend in algorithmic stability, spectral methods for hypergraphs, influence propagation in networks, and learning-augmented algorithms. Key venues include FOCS, SODA, ICALP, and NeurIPS, reflecting high impact in theoretical computer science and machine learning. Topics such as Lipschitz continuity, average sensitivity, spectral sparsification, and sublinear algorithms recur, indicating a cohesive research program on robust and efficient computation. Best Paper Award, AISTATS (2018) MEXT Commendation for Science and Technology – Young Scientists’ Prize (2017) Inoue Research Award for Young Scientists (2014) KDDI Foundation Award (2024) Funai Information Technology Award (2024) Yuichi Yoshida advises PhD students and hosts numerous postdocs, RAs, and international interns. He leads major research projects funded by JSPS and JST, including Grants-in-Aid for Scientific Research (S) and PRESTO programs. His service includes program committee roles for ICML, NeurIPS, SODA, and ICALP. He has co-authored a book on Property Testing and contributed to encyclopedias on big data technologies. He leads research teams on algorithm desensitization and large-scale graph algorithms, and has been program co-chair for GRADES-NDA'23. His lab fosters international collaboration, hosting interns from top universities worldwide.
Matthias Birkner is a Professor of Probability Theory at the Institute of Mathematics, Faculty of Physics, Mathematics and Informatics, Johannes Gutenberg University Mainz. His research focuses on probability theory, stochastic processes, and their applications in mathematical biology and population genetics. His primary research interests include: Probability Theory, particularly branching processes and coalescent theory Stochastic processes in population genetics and evolutionary biology Mathematical modeling of population dynamics Spatial stochastic processes and random walks Applications of probability theory in economics and finance Analysis of Birkner's recent publications reveals a strong focus on probabilistic models for population genetics, particularly examining branching processes, coalescent theory, and spatial population models. His work often bridges rigorous mathematical theory with applications in evolutionary biology, investigating phenomena like fixation probabilities, genealogical structures, and the effects of skewed offspring distributions. More recently, he has expanded into mathematical economics, studying wealth distribution models. Professor Birkner actively collaborates with researchers across Europe and North America, evidenced by his extensive list of co-authors from institutions in Germany, France, Switzerland, the UK, and the United States. His teaching portfolio includes advanced courses in stochastic processes, population models, and biostatistics. His academic leadership is evident through his involvement in organizing workshops and conferences on probability theory and its applications, including events focused on branching processes and multiple merger coalescents in population genetics.
Ellen Baake is a Professor of Biomathematics and Theoretical Bioinformatics at the Faculty of Technology, Bielefeld University (since 2012 as full professor; joint membership in the Faculty of Mathematics since 2011). Her work bridges mathematical population genetics, probability theory, and mathematical immunobiology, with a focus on mutation-selection balance, recombination dynamics, and stochastic processes in evolutionary systems. Education : Habilitation in Zoology and Theoretical Biology, Munich University (1999) PhD in Theoretical Biology, Bonn University (1989) Diploma in Biology, Bonn University (1985) Her research explores the interplay of mutation, selection, and drift through exact solutions and probabilistic frameworks, leveraging models like the Moran process and Wright-Fisher equations. Recent projects include DFG Collaborative Research Centre 1283 on uncertainty and randomness. Notable awards include the 2022 Feldman Prize and the 2007 Kloosterman Chair. She has led initiatives such as the Research Centre for Mathematical Modelling (2006-2023) and the DFG Priority Programme on probabilistic structures (2011-2022).
- Probability Theory
- Random Walks
- Random Matrices
- +۱ مورد دیگر
Jan Nagel is Professor of Stochastics at TU Dortmund University's Department of Mathematics. His research explores probability theory with focus areas including random walks in random environments, random matrix theory, and stochastic processes. Recent publications investigate the dynamic behavior of viscoelastic structures with random material properties, sum rules in mathematical physics, and functional central limit theorems for random matrices. Dr. Nagel completed his doctorate in Mathematics at Ruhr University Bochum (2010) following a Diplom degree (2008). His academic trajectory includes positions as Assistant Professor at TU Dortmund (2019-2023), research fellowship at TU Eindhoven, and visiting professorships at LMU Munich. His work bridges theoretical mathematics with applications in statistical mechanics and disordered systems.
Prof. Rudolf Grübel is a full Professor at the Institute for Insurance and Financial Mathematics, Leibniz University Hannover (since 1994). He previously held positions at the University of Paderborn (1993-1994), Technical University of Delft (1989-1993), and Imperial College London (1985-1989). His research focuses on Probability Theory, stochastic processes, and combinatorial structures, with applications to algorithm analysis and actuarial science. Expertise includes renewal theory, random trees, and statistical methods. Contributions to branching processes, Markov chains, and permutons. His work emphasizes probabilistic models in discrete structures (e.g., search trees, random graphs), with recent trends toward copula-based statistical tests and mixture representations of distributions. Key topics include tail behaviors, limit theorems, and boundary theory approaches. Publications span over 40 years, with notable contributions to the Annals of Probability, Journal of Applied Probability, and Electronic Journal of Probability. No awards are listed, but his extensive academic output reflects his influence in stochastic analysis.
Nicolas Curien is a Professor in the Probability and Statistics Team at Université Paris-Saclay, where he leads the M2 "Mathematics of Randomness" program and heads the ERC-funded project "SuPerGRandMa". His office is located in Building 307, Orsay. Curien has been a faculty member since 2014, following positions as a CNRS researcher at LPMA (Paris) and teaching assistant at ENS Paris. He completed his PhD under Jean-François Le Gall in 2011. Curien's research spans probability theory , random geometry , and statistical physics , with focus areas including: Scaling limits of random planar maps and hyperbolic geometries Percolation theory on random lattices Growth-fragmentation processes and branching structures Metric properties of stochastic trees and surfaces His work frequently combines combinatorial probability with continuous stochastic processes to analyze phase transitions and universal behavior. Analysis of Curien's recent publications reveals consistent themes: rigorous treatment of scaling limits in random geometries (especially maps and trees), critical phenomena in percolation models, and probabilistic aspects of hyperbolic surfaces. Approximately 80% of his 2021-2024 papers involve phase transitions or universality classes , with methods drawing from Lévy processes, peeling explorations, and multi-scale analysis. Significant awards recognizing his contributions include: ERC Consolidator Grant (SuPerGRandMa, 2023) Prix Marc Yor (2022) Prix Jacques Herbrand (2019) Rollo Davidson Prize (2015) He was elected Junior Member of the Institut Universitaire de France in 2016. Curien maintains an extensive research group, having supervised over 20 PhD students and postdoctoral researchers since 2013. Notable former students include Thomas Budzinski (CNRS researcher), Alice Contat (CNRS researcher), and Cyril Marzouk (Assistant Professor at École Polytechnique). His ERC project currently supports 4 early-career researchers. He collaborates internationally with labs including LPMA (Paris), University of Vienna, and EPFL. Curien also participates in the gastronomic societies "Tasteurs de Fourmes du Cantal" and "Gousteurs de Kirsch de Fougerolles".