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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.

• Probability Theory
• Stochastic Processes
• Large Deviations
• +۴ مورد دیگر

Prof. Dr. Ingo Scholtes is Chair of Machine Learning for Complex Networks at Julius-Maximilians-Universität Würzburg's Center for Artificial Intelligence and Data Science (CAIDAS). His research spans network science, graph machine learning, and computational social science, with applications in software engineering, ecology, biology, and physics. He received a Juniorfellowship from the German Informatics Society (2014) and an SNSF Professorship (CHF 1.5Mio, 2018). Current affiliations: JMU Würzburg (since 2021), University of Zurich (2018-2024), Bergische Universität Wuppertal (2019-2021) Research focus: Higher-order network modeling, temporal graph analysis, AI for collaborative systems, causality-aware machine learning His recent publications demonstrate strong trends in temporal network analysis , graph neural networks for time-series, and higher-order models across software engineering and social science domains. He co-chairs multiple international workshops on complex networks and serves as associate editor for EPJ Data Science and Advances in Complex Systems. Key scientific contributions: Foundational work on higher-order network models published in Nature Physics Methodological innovations in temporal network visualization (HOTVis) and path-based analysis (pathpy) As both educator and organizer, he leads the Computational Social Science Section at GI e.V., mentors across disciplines, and develops tools like git2net for collaboration analysis. His work bridges theoretical foundations with practical applications in network science.

• Network Science
• Graph Neural Networks
• Computational Social Science
• +۴ مورد دیگر

Ron Peled is a Full Professor in the School of Mathematical Sciences at Tel Aviv University , currently on leave to serve as the Brin Professor in the Department of Mathematics at the University of Maryland starting summer 2024. During 2022–2024 he was a Member at Princeton University and the Institute for Advanced Study . Education & Career: While explicit degrees are not listed, his trajectory shows appointments at NYU (2009–2010), UC Berkeley and Tel Aviv University as a teaching assistant, followed by faculty positions culminating in full professorship. Research Interests: His work lies at the intersection of probability theory, statistical physics, and combinatorics . Key themes include: Disordered systems and random environments (random-field Ising, spin glasses) First-passage percolation and random metrics Random surfaces and height functions Loop models and critical phenomena Random matrices and band matrices Geometric probability and allocation problems Publications & Impact: With over 70 papers in top journals such as Annals of Mathematics , Annals of Probability , Inventiones Mathematicae , and Communications in Mathematical Physics , his recent work explores minimal surfaces in random environments, localization in random band matrices, and quantitative disorder effects in low-dimensional spin systems. Grants & Awards: Research has been continuously funded by: Israel Science Foundation (grants 1048/11, 861/15, 1971/19, 2340/23) ERC Starting Grant LocalOrder ERC Consolidator Grant Transitions Marie Skłodowska-Curie International Reintegration Grant SPTRF Teaching & Mentoring: Prof. Peled has taught a broad spectrum of courses at Tel Aviv University (Brownian motion, probability, percolation, random matrices, stochastic calculus) and NYU (combinatorics, discrete mathematics). He has supervised 13 post-doctoral fellows and 8 graduate students (PhD & MSc) to date. Service & Outreach: He co-organizes the Joint Israeli Probability Seminar and has organized numerous international workshops and conferences including at Oberwolfach, Technion, and Tel Aviv University.

• Probability Theory
• Statistical Physics
• Mathematical Physics
• +۷ مورد دیگر

Noam Berger Steiger is a Professor of Stochastic Processes at the Technical University of Munich (TUM), within the School of Computation, Information and Technology and the Department of Mathematics. His office is located at Parkring 11, Garching bei München, and he can be contacted at noam.berger@tum.de. His research focuses on stochastic processes in random environments, percolation theory, and random walks. Key contributions include asymptotic analysis of preferential attachment graphs, quenched invariance principles for non-elliptic random walks, and slowdown phenomena in ballistic random motion. His work bridges theoretical probability with applications in complex systems. Analysis of his 2012-2014 publications reveals consistent focus on random walk dynamics in disordered media, with significant results on ballisticity conditions, trail detection in random scenery, and distributional limits. His research employs advanced probabilistic techniques published in top-tier journals including Annals of Probability and Probability Theory and Related Fields . Professor Berger has supervised 11 theses: 5 bachelor's theses at TUM covering Brownian motion properties and investment strategies for risk-averse investors, and 6 master's theses (3 at TUM, 3 at Hebrew University) on topics including return times for random walks, mass transport principles, and spin-glass percolation. His current teaching includes Markov Chains, Probability on Graphs, and Brownian Motion seminars. He is an active member of TUM's Probability Theory research group, which participates in the TUM-ICL Mathematical Sciences Hub and Exzellenzcluster MCQST. The group collaborates on quantum science initiatives while maintaining strong foundations in classical probability theory and stochastic analysis.

• Stochastic Processes
• Random Environments
• Percolation Theory
• +۲ مورد دیگر

Maximilian Egger is a Doctoral Researcher at the Institute for Communications Engineering under Prof. Antonia Wachter-Zeh at the Technical University of Munich (TUM). His research focuses on distributed machine learning, privacy-preserving computing, and information theory. He holds an M.Sc. in Electrical Engineering and Information Technology (2022, TUM) and a B.Eng. in Electrical Engineering (2020). He has conducted research stays at École Polytechnique Fédérale de Lausanne (2024) and Imperial College London (2023). Egger has received several awards, including the DAAD Scholarship (2023) and the VDE Award Bavaria (2020). His work emphasizes secure federated learning, Byzantine-resilient systems, and efficient distributed algorithms. He is affiliated with the Chair of Coding and Cryptography and actively contributes to advancements in decentralized learning systems. Recent publications highlight breakthroughs in privacy preservation, channel capacity estimation, and scalable federated edge learning.

• Distributed Machine Learning
• Privacy Preserving Computing
• Federated Learning
• +۳ مورد دیگر

Martin Grohe is a Professor at the School of Logic and Theory of Discrete Systems , part of the Department of Computer Science at RWTH Aachen University . His research spans Algorithms and Complexity , Logic , Database Theory , Graph Theory , and Machine Learning , with a focus on integrating logical frameworks into computational models. His recent work explores graph neural networks , Weisfeiler-Leman algorithms , and parameterized complexity , as seen in publications on isomorphism testing , database repairing , and probabilistic query evaluation . While no specific scientific awards are mentioned, his contributions to graph theory and machine learning are widely recognized through numerous peer-reviewed publications.

• Algorithms and Complexity
• Logic
• Database Theory
• +۲ مورد دیگر

Justin Salez is a Professor of Mathematics at Université Paris-Dauphine & PSL University and an Institut Universitaire de France junior fellow. He serves as Principal Investigator for the ERC Consolidator Grant CUTOFF project, focusing on the cutoff phenomenon in Markov processes. His academic affiliations include editorial roles at Electronic Journal of Probability and co-organizing the CEREMADE Colloquium. Education: Mathematics Aggregation (2007-2008), Master in Probability (2006-2007), Master in Theoretical Computer Science (2005-2006), PhD (2008-2011). Professional Experience: Full Professor (2019-present) and Assistant Professor (2012-2019) at Paris Dauphine/Diderot, Postdoc at UC Berkeley (2011-2012). His research centers on Markov chain mixing times , non-negative curvature , and functional inequalities , with applications to random graphs, interacting particle systems, and MCMC algorithms. Key themes include entropy dissipation, cutoff criteria, and spectral analysis. The 15 most recent publications highlight advances in cutoff theory, curvature-entropy relationships, and functional inequalities. These span journals like Transactions of the American Mathematical Society , Annals of Probability , and Annals of Applied Probability , reflecting interdisciplinary work in probability, spectral theory, and statistical physics. Scientific Awards: ERC Consolidator Grant (2023), Marc Yor Prize (2024), Bourbaki Seminar (2024), Saint-Flour Lecture (2025), and Institut Universitaire de France fellowship (2019). As an advisor, he has supervised or is supervising PhD students including Alexandre Bristiel , Hong Quan Tran , and Guillaume Conchon-Kerjan . His lab, CEREMADE, hosts a monthly colloquium he co-organizes, fostering interdisciplinary dialogue in probability, analysis, and statistics.

• Mixing Times of Markov Chains
• Stochastic Calculus
• Random Graphs
• +۵ مورد دیگر

Max Wardetzky is a Professor at the Institute for Numerical and Applied Mathematics within the Faculty of Mathematics and Computer Science at the University of Göttingen, Germany. His office is located at Lotzestraße 16-18, 37083 Göttingen, and he can be reached via email at wardetzky@math.uni-goettingen.de or by phone at +49 551 39 26778. Professor Wardetzky leads the Discrete Differential Geometry Lab at the University of Göttingen, where he conducts research at the intersection of mathematics, computer science, and geometry processing. His work bridges theoretical foundations with practical applications in computer graphics and scientific computing. His primary research interests include: Applied Geometry Discrete Differential Geometry Numerical Analysis Geometry Processing Physical Simulation Computer Graphics Professor Wardetzky's extensive publication record demonstrates significant contributions to the field of discrete differential geometry and its applications. His work shows a consistent focus on developing mathematically rigorous yet computationally efficient methods for geometric problems. Key trends in his research include the development of discrete analogues of smooth geometric objects, the study of convergence properties between discrete and continuous models, and the application of these methods to problems in computer graphics and physical simulation. Professor Wardetzky has made substantial contributions to the theoretical foundations of discrete differential geometry while maintaining strong connections to practical applications. His work on discrete Laplacians, curvature approximations, and geometric flows has influenced both theoretical mathematics and practical geometry processing algorithms.

• Applied Geometry
• Discrete Differential Geometry
• Numerical Analysis
• +۳ مورد دیگر

Sebastian Hensel is a Professor of Pure Mathematics at the Mathematical Institute of Ludwig Maximilian University of Munich (LMU), where he also serves as the Dean of Studies. His research focuses on the intersection of low-dimensional topology and geometric group theory, with emphasis on mapping class groups, handlebody groups, and diffeomorphism groups of surfaces. He leads the Geometry and Topology Working Group and is actively involved in teaching advanced seminars. Hensel received his PhD from the University of Bonn in 2011 under Ursula Hamenstädt. Before joining LMU, he held positions as a Dickson Instructor at the University of Chicago and as a temporary academic councilor in Bonn. His research employs geometric methods to study algebraic structures in topological spaces, particularly surfaces and 3-manifolds. Scientific Awards: Dickson Instructor Fellowship, University of Chicago Teaching & Advising: Hensel regularly teaches courses on Riemannian geometry, geometric group theory, and manifold topology. He currently advises bachelor and master's theses in geometry/topology and organizes block seminars. As Dean of Studies, he oversees academic programs at the Mathematical Institute. Affiliations: Member of the Geometry and Topology Group at LMU, with collaborations spanning multiple institutions including TUM and international partners.

• Low-dimensional Topology
• Geometric Group Theory
• Mapping Class Groups
• +۲ مورد دیگر

Prof. Dr. Silke Rolles is a Professor of Probability Theory at the Technical University of Munich (TUM) , specifically within the TUM School of Computation, Information and Technology. She has held this position since 2006 and previously held academic roles at the University of California, Los Angeles, the University of Zurich, and the Technical University of Eindhoven. Her research focuses on probability theory , particularly reinforced random processes , random processes in random environments , and statistical mechanics . Her recent work explores lattice Coulomb gas, supersymmetric hyperbolic sigma models, and localization phenomena. She was awarded the 2017 Golden Circle Teaching Award and the 2004 Stochastics Section Dissertation Prize . She has co-organized workshops such as Women in Probability and Stochastic Reinforcement Processes, and serves as an associate editor for probability journals.

• Probability Theory
• Random Processes in Random Environments
• Statistical Mechanics
• +۲ مورد دیگر

Sebastian Andres is Professor of Stochastics at TU Braunschweig's Institute of Mathematical Stochastics. His research focuses on random processes in disordered media, including homogenization of random walks, percolation theory, and heat kernel analysis. He holds a habilitation from University of Bonn and previously held positions at University of Cambridge and University of Manchester. Research Themes: Quantitative homogenization, invariance principles for degenerate random walks, and applications to mathematical physics. Teaching: Current courses include Probability Theory and Discrete Financial Mathematics; authored lecture notes on Martingale Theory for Finance.

• Stochastic Analysis
• Random Walks
• Percolation Theory
• +۱ مورد دیگر

Elmar Teufl is a Senior Lecturer in the Department of Mathematics at Eberhard Karls Universität Tübingen. He holds a Doctorate (2002) and a Habilitation (2015) from Graz University of Technology and Tübingen University, respectively. His research focuses on random walks on graphs and groups, combinatorial problems on self-similar structures, asymptotic methods, and fractal geometry. His work bridges probability theory, combinatorics, and graph theory with applications to fractal networks and recursive systems. Teufl has held academic positions across Europe, including visiting professorships at the University of Stuttgart (2012–2013) and a Marie Curie Fellowship at Bielefeld University (2005–2006). His research has been supported by the Austrian Science Fund (FWF) and the EU. Key achievements include contributions to spanning tree enumeration on Sierpinski graphs and resistance scaling in self-similar lattices. His publications span topics like asymptotic enumeration, determinant identities for Laplacian matrices, and structural properties of nilpotent groups. He received the Austrian Mathematical Society's Studienpreis (2003) for his thesis on self-similar graphs and fractals. Teaching responsibilities include advanced courses in combinatorics and probability, with a full teaching record documented in alma.

• Random Walks on Graphs
• Combinatorics
• Probability Theory
• +۳ مورد دیگر

Markus Heydenreich is a Professor at the Institute of Mathematics, Faculty of Mathematics, Natural Sciences and Technology, University of Augsburg. His research focuses on probability theory, particularly discrete random objects in statistical mechanics, mean-field behavior, lace expansion, and spatial random graph models. 2025: Workshop Bayrischzell 2024: Workshop Frauenchiemsee His recent publications highlight critical phenomena in random connection models, percolation, and spatial networks. He contributes to editorial boards and DFG-funded projects, including Random Geometric Systems and Mathematics of Many-Body Quantum Systems . Teaching includes courses on stochastic processes and seminars on probability and forensics. Key research themes: phase transitions, high-dimensional random walks, and mathematical physics applications. Collaborators include Remco van der Hofstad, Christian Hirsch, and Kilian Matzke. The team at Augsburg comprises doctoral researchers and postdocs.

• Probability Theory
• Statistical Mechanics
• Random Graphs
• +۷ مورد دیگر

Prof. Dr. Peter Müller is a Professor at the Mathematical Institute of Ludwig Maximilian University of Munich (LMU), where he also serves as Dean of the Faculty of Mathematics, Informatics and Statistics. His research group focuses on Analysis, Mathematical Physics, and Numerics. Office: Room 439, Block B, Theresienstr. 39, 80333 Munich. Education: Habilitation in Mathematics, University of Göttingen (2006) Habilitation in Physics, University of Göttingen (2002) Ph.D. in Physics, University of Erlangen-Nürnberg (1996) Diploma in Physics, University of Erlangen-Nürnberg (1991) Research Interests: Müller's work spans mathematical physics, analysis, and probability theory, with emphasis on: Random Schrödinger operators and spectral theory Delocalization phenomena in disordered systems Quasiperiodic structures and ergodic properties Entanglement entropy in quantum systems His research bridges rigorous mathematical analysis with applications in quantum mechanics and statistical physics. Publication Trends: Recent articles (2013–2025) predominantly explore spectral theory in disordered quantum systems. Key themes include localization/delocalization transitions, asymptotic analysis of entanglement entropy, and spectral properties of random operators. Methodologically, his work combines functional analysis, stochastic processes, and operator theory to address fundamental questions in mathematical physics. Student Advising: Extensive mentorship of graduate students: 8 PhD students (e.g., Jakob Stern, Ruth Schulte) 17 Master's students (e.g., Leonard Wetzel, Julian Widl) Research Team: Leads the working group "Analysis, Mathematical Physics and Numerical Analysis" with postdoctoral researchers (e.g., Constanza Rojas-Molina) and PhD candidates. Regularly organizes international conferences on mathematical physics and disordered systems.

• Mathematical Physics
• Analysis
• Probability Theory
• +۴ مورد دیگر

Zoi Kaoudi is a researcher at the IT University of Copenhagen , specializing in Data Management , Knowledge Graphs , and Machine Learning . Her work focuses on cross-platform data processing, query optimization, and scalable systems for graph analytics. She has published extensively in venues like SIGMOD , VLDB , and ISWC , with recent contributions to Apache Wayang , DORIAN , and Space-Efficient Graph Algorithms . Her research bridges theoretical advancements with practical frameworks for data science pipelines. Collaborations include Volker Markl, Jorge-Arnulfo Quiané-Ruiz, and Ioana Manolescu. She has explored topics such as Parameter Servers , Knowledge Graph Embeddings , and RDF Data Management in the cloud. Her work emphasizes open science and system integration.

• Data Management
• Knowledge Graphs
• Machine Learning
• +۳ مورد دیگر