Moritz Wiese is a Researcher at the Chair of Theoretical Information Technology within the Department of Electrical and Computer Engineering at Technische Universität München (TUM) . His work focuses on information-theoretic security, quantum communication, and wireless networks, with significant contributions to interference modeling and secure coding schemes. Research Interests : Information-Theoretic Security, Quantum Wiretap Channels, MIMO Systems, 5G/6G Technologies, Physical Layer Security, and Network Coding. Key Grants : Involved in DFG-funded projects (Gottfried Wilhelm Leibniz Prize, UKoloS) and BMBF initiatives (6G-life, QuaDiQua). Collaborations : Worked with Prof. Holger Boche, Christian Deppe, and Rami Ezzine on secure communication and randomness generation. Labs : Active in the ACES Lab and 6G-life research hub, focusing on joint communication/sensing and neuromorphic computing. Publications : Over 20 papers on secure estimation, wiretap channels, and quantum secrecy, including top-tier journals like IEEE Transactions on Information Theory and Journal of Mathematical Physics .
Prof. Dr. Dominik Schwarz is a faculty member at the Faculty of Physics , Bielefeld University. His research focuses on Cosmology and Particle Physics , particularly in the areas of Dark Energy , Dark Matter , Cosmological Inflation , and Large-Scale Structure Formation . He contributes to projects like the International LOFAR Telescope Consortium and the SFB-TRR 211 on strongly interacting matter. APART Fellow of Austrian Academy of Sciences Humboldt Fellow CERN Fellow His recent work explores the cosmic dipole anisotropy , axion density perturbations , and multi-wavelength cosmic web mapping . He also advances data science infrastructure through the PUNCH4NFDI consortium.
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.
Anna Levina is an Assistant Professor for Computational Neuroscience at the University of Tübingen , affiliated with the Department of Computer Science under the Faculty of Science. Her research focuses on the self-organization of neuronal activity, critical dynamics in neural networks, and the excitation/inhibition balance in cortical circuits. Current positions: Assistant Professor (since 2018), Group Leader (2017-2018), Equality Officer (Computer Science) Previous roles: IST Fellow (2015-2017), Associated Researcher (2011-2015), Postdoc/PI (2011-2015), Postdoc (2008-2011) Her research integrates mathematical modeling , statistical physics , and computational neuroscience to study criticality phenomena, neural avalanches, and adaptive network dynamics. Key interests include: Self-organized criticality in neural systems Excitation/Inhibition balance mechanisms Network topology and dynamics Timescale analysis in neural processing Stochastic modeling of neural activity Recent publications reveal trends in understanding critical dynamics across biological and artificial networks, with applications to memory systems, sensorimotor integration, and disease modeling. She has received recognition as an IST Fellow .
Agnes Desolneux is a CNRS Research Director at the Borelli Centre (formerly CMLA) and a Professor attached to the Mathematics Department at ENS Paris-Saclay. Education: PhD in Applied Mathematics (2000) from ENS Cachan Habilitation in Applied Mathematics (2010) from Université Paris Descartes Her research focuses on image analysis via statistical methods , particularly a contrario approaches, image restoration, texture synthesis, Determinantal Point Processes (DPP), optimal transport, Gaussian mixtures, geometry of random field excursions, shot-noise models, and mathematical modeling of visual perception through Gestalt theory. The articles extracted reflect her expertise in applied mathematics and computer vision , with recent works (2025-2020) on optimal transport algorithms, DPP applications, multiscale texture analysis, and stochastic modeling in medical imaging. Keywords span machine learning, probability theory, medical imaging, and computer vision . She has no listed scientific awards but has authored influential works including the book From Gestalt Theory to Image Analysis: A Probabilistic Approach (Springer, 2008) and Pattern Theory: the stochastic analysis of real-world signals (AK Peters, 2010).
Aleksandar Bojchevski is a full professor of Computer Science at the University of Cologne, leading the Trustworthy Artificial Intelligence Lab (TAIL). His research focuses on developing robust, interpretable, and privacy-preserving machine learning models, particularly graph neural networks (GNNs). The lab emphasizes trustworthiness in high-stakes applications through methods that handle noisy, adversarial, or anomalous data. Bojchevski holds a PhD and PostDoc from the Technical University of Munich, advised by Stephan Günnemann. Previously, he was faculty at CISPA Helmholtz Center for Information Security. His work bridges theory and practice, addressing adversarial robustness, uncertainty quantification, and scalable GNN techniques. Research interests include: robustness certification for GNNs, conformal prediction, adversarial attack analysis, and privacy-aware machine learning. His lab actively collaborates with RWTH Aachen and organizes events like the Learning on Graphs Meet Up. Recent achievements include a teaching award for the Machine Learning lecture (SS 24) and NeurIPS 2024 acceptance of SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors . Open positions are available in his group focusing on trustworthy ML topics. Key labs/teams: Trustworthy Artificial Intelligence Lab (TAIL), Center for Data and Simulation Science (CDS) as a core scientist.
Kord Eickmeyer is a Lecturer at Technische Universität Darmstadt in the Department of Mathematics, specializing in the mathematical logic group. He holds a PhD in mathematics from Humboldt University Berlin and has held postdoctoral positions at TU Darmstadt (2011–2017) and the National Institute of Informatics in Tokyo (2011–2013). His research focuses on finite model theory, graph structure theory, and computational complexity, particularly in descriptive and parameterized complexity, as well as randomization and derandomization techniques. Research interests include exploring the boundaries of computational complexity through logical frameworks, analyzing graph structures for efficient algorithm design, and investigating the role of randomness in computation. His work bridges theoretical computer science and mathematical logic, with applications in algorithm design and formal methods. Publications span topics from model-checking on ordered structures to gap-planar graphs and randomized logics. Collaborations include prominent institutions like the National Institute of Informatics and Humboldt University Berlin. No scientific awards are explicitly listed, but his extensive academic contributions reflect a strong research trajectory. Advising and grants are not detailed in the provided text, though his academic career includes supervision roles during his PhD and postdoctoral phases. His involvement with the mathematical logic group at TU Darmstadt highlights collaborative research efforts in foundational areas of computer science and mathematics.
Krishna Gummadi is a Scientific Director and Professor at the Max Planck Institute for Software Systems (MPI-SWS) in Germany, where he leads the Networked Systems Research Group. He also holds a professorship at the University of Saarland, demonstrating his dual commitment to research and academic instruction in computer science. His educational background includes: Ph.D. in Computer Science and Engineering from the University of Washington (2005) B.Tech. in Computer Science and Engineering from the Indian Institute of Technology, Madras (2000) Gummadi's research spans networked and distributed computer systems with a current focus on social computing systems. His work addresses critical challenges in algorithmic fairness, privacy in social media, trustworthiness of online identities, and information dissemination in social networks. He approaches these problems through interdisciplinary methods combining user-centric studies, data-centric analysis, and systems-centric design to create practical solutions that enhance fairness, transparency, and user control in online platforms. His methodology integrates large-scale observational studies, computational modeling, and system implementation to tackle complex human-computer interaction challenges at societal scale. His recent publications reveal a strong emphasis on fairness in algorithmic decision making, with significant contributions to quantifying and addressing discrimination in machine learning systems. His work bridges computer science, social science, and ethics, creating frameworks for fair classification, understanding media bias, and developing privacy-preserving techniques that maintain functionality while protecting user data. The research demonstrates a progression from technical system design to addressing societal implications of computing systems. Among his notable scientific achievements: ERC Advanced Grant in 2017 for 'Foundations for Fair Social Computing' Test of Time Awards at ACM SIGCOMM and AAAI ICWSM Casper Bowden Privacy Enhancing Technologies (PET) and CNIL-INRIA Privacy Runners-Up Awards IW3C2 WWW Best Paper Honorable Mention Multiple Best Paper awards across prestigious conferences Gummadi has advised numerous PhD students and postdoctoral researchers who have gone on to prominent positions in academia and industry. His ERC Advanced Grant has supported extensive research into fair social computing, while his leadership in major conferences (including serving as General Chair for ICWSM 2016 and Program Chair for WWW 2015) has shaped research directions in the field. His teaching portfolio includes courses on Distributed Systems, Human-Centered Machine Learning, and Social Media Analysis. He leads the Networked Systems Research Group at MPI-SWS, which has developed several publicly available systems including tools for fair classification, privacy risk assessment, trust evaluation in social media, and information diet management. The group's work bridges theoretical advances with practical implementations that address real-world challenges in social computing, with numerous software releases and datasets made available to the research community.
Prof. Dr. Hendrik Weber is a Professor of Mathematics at the University of Münster, leading the Workgroup for Stochastic Analysis. He holds the Bridging the Gaps Professorship and is affiliated with the Faculty of Mathematics and Computer Science. His expertise lies in stochastic analysis, particularly stochastic partial differential equations (SPDEs) and their applications in mathematical physics and statistical mechanics. Weber's research focuses on regularity structures, singular SPDEs, and the interplay between stochastic processes and nonlinear dynamics. Education and Career: Weber earned his PhD from the University of Bonn (2010) and held positions at the University of Warwick (2010–2018) and the University of Bath (2018–2022) before joining Münster in 2022. He has been recognized with awards including the ERC Consolidator Grant (2022), Philip Leverhulme Prize (2017), and Rollo Davidson Prize (2016). Research Interests: Weber's work addresses theoretical challenges in SPDEs, including invariant measures, phase transitions, and scaling limits. His projects span topics like singularities in PDEs, field theory randomness, and deep learning surrogate methods. Recent studies include the dynamic Φ⁴ model, stochastic quantization in non-commutative spaces, and a priori bounds for quasilinear SPDEs. Publications: Over 60 peer-reviewed articles, including high-impact contributions to Annals of Probability , Communications in Mathematical Physics , and Archive for Rational Mechanics and Analysis . His work emphasizes rigorous mathematical analysis of stochastic systems and their physical implications. Awards and Grants: ERC Consolidator Grant (2022), Royal Society Fellowship (2016), and multiple collaborative projects funded by the EPSRC and DFG. His research also bridges theoretical developments with applications in machine learning and feature engineering using regularity structures. Labs/Teams: Leads a dynamic research group comprising PhD students (e.g., Sophie Mildenberger) and postdoctoral researchers. Collaborations with global institutions like the University of Warwick and the University of Bath drive interdisciplinary advancements in stochastic analysis.
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.
Prof. Dr.-Ing. Lars Linsen is a full Professor of Computer Science at the Westfälische Wilhelms-Universität (WWU) Münster, leading the VISualization & graphIX (VISIX) group. His primary affiliation is the Institute of Computer Science within the Faculty of Mathematics and Computer Science. He holds adjunct professorships at Jacobs University, Bremen, and has held previous academic roles including Full Professor at Jacobs University (2012–2017) and Associate/Assistant Professor roles in Germany and the U.S. His research focuses on interactive visual analysis, medical visualization, and scientific visualization, with applications in life sciences and engineering. Education: PhD (Dr.-Ing.) in Computer Science from Universität Karlsruhe (2001), M.Sc. (Diplom) in Computer Science (1997), B.Sc. (Vordiplom) in Computer Science (1994). Awards: IEEE Visualization Design Contest Winner (2008, 2022, 2018), Preis des Fördervereins des Forschungszentrum Informatik (2002). Research Highlights: Develops visualization tools for medical imaging (e.g., mass spectrometry imaging, MRI data analysis) and physical simulations (e.g., wildfire spread analysis, asteroid impact modeling). Active in EU-funded projects like Pig-Pro-QuO (surface coatings) and cells-in-motion initiatives. Supervised over 20 PhD/MS advisees, including notable graduates in medical visualization and simulation ensemble analysis. Publications: Over 100 peer-reviewed articles in top venues like IEEE Transactions on Visualization and Computer Graphics, Computers & Graphics, and EuroVis. Key works include SciVis contest-winning wildfire analysis frameworks and medical visualization tools for stenosis detection. Teaching: Offers courses on visualization, computer graphics, and computational science. Actively involved in thesis supervision and curriculum development at both WWU Münster and Jacobs University. Grants & Collaborations: Principal investigator on DFG-funded projects (e.g., hemodynamics simulations, ensemble visualization) and industry collaborations (e.g., Tascon GmbH for coating quality analysis). Member of the Cells-in-Motion Interfaculty Centre and CDH board at WWU.
Prof. Dr. Markus Bachmayr is a full professor at the Institute for Geometry and Practical Mathematics, RWTH Aachen University, holding the chair for Applied Mathematics. His research focuses on nonlinear approximation, high-dimensional partial differential equations (PDEs), uncertainty quantification, and numerical methods in quantum chemistry. He leads the ERC Consolidator Grant project Computational Complexity of Highly Nonlinear Approximations (COCOA) and contributes to CRC 1481 Sparsity and Singular Structures, and RTG 2326 Energy, Entropy, and Dissipative Dynamics. His recent work emphasizes adaptive low-rank and sparse approximation techniques for parametric and stochastic PDEs, including applications in radiative transfer and poroviscoelastic flow modeling. He serves as Editor-in-Chief of Foundations of Computational Mathematics and Associate Editor for multiple journals. Scientific Awards: John Todd Award 2013 Borchers Plakette 2014 Erwin Wenzl Preis 2007 He has taught courses such as Numerische Analysis I/II, Numerische Mathematik für Elektrotechniker, and seminars on numerical methods and approximation theory.
Alexey Bufetov is a Professor at Leipzig University, holding an ERC Starting Grant for his research in Integrable Probability (2022-2027). Previously, he served as a W2-Professor ("Bonn Junior Fellow") at the Hausdorff Center for Mathematics (2018-2021) and as a CLE Moore Instructor at Massachusetts Institute of Technology (2015-2018). His research centers on Probability Theory , with deep connections to Mathematical Physics and Combinatorics . Key areas include integrable probability, stochastic particle systems (ASEP/TASEP), random tilings, Schur generating functions, and representation-theoretic aspects of probability. His work often bridges abstract mathematical structures with physical models from statistical mechanics. Bufetov's recent publications reveal a strong focus on integrable systems and asymptotic analysis , particularly exploring connections between Mallows measures, vertex models, and random matrix theory. His 2025 work on Aztec diamond domino tilings exemplifies his signature approach combining combinatorial structures with probabilistic methods. His primary recognition is the ERC Starting Grant "Integrable Probability" (2022-2027), supporting his cutting-edge research program. Bufetov has maintained a prolific collaborative network, frequently publishing with leading researchers including Alexei Borodin, Vadim Gorin, Leonid Petrov, and Kailun Chen. His work appears in top journals such as Advances in Mathematics , Duke Mathematical Journal , and Communications in Mathematical Physics .
Prof. Dr. Fabian Gieseke is a Professor and Chair of Machine Learning and Data Engineering at the University of Münster. He holds a PhD in Computer Science from Carl von Ossietzky University of Oldenburg and a dual degree in Mathematics and Computer Science from the University of Münster. His research focuses on Machine Learning, High-Performance Computing, and their applications in Geosciences, Smart Cities, and Astrophysics. Education: PhD in Computer Science (2012), Carl von Ossietzky University of Oldenburg University studies in Mathematics and Computer Science (2006–2011), University of Münster Research Interests: Data Mining and Machine Learning High-Performance Computing & Distributed Systems Deep Learning Applications in Environmental Science and Astrophysics Geospatial Data Analysis using Satellite Imagery Publications Trends: His recent work emphasizes large-scale environmental monitoring via deep learning, including canopy height estimation, forest biomass prediction, and national-scale tree counting. He also explores interactive systems for geospatial data retrieval and optimization of machine learning models for resource-constrained environments. Advising & Grants: Supervised over 30 theses on topics like satellite image analysis, deep learning on microcontrollers, and data marketplaces for smart grids. Active in securing grants for interdisciplinary projects combining AI with Earth observation. Labs/Teams: Leads the Machine Learning and Data Engineering group at the University of Münster, focusing on scalable AI solutions for real-world challenges in science and industry.
Professor Katrin Tent is a distinguished mathematician specializing in mathematical logic at the University of Münster, where she holds a professorship in the Faculty of Mathematics and Computer Science within the Institute for Mathematical Logic and Foundations Research. She is an active researcher in Mathematics Münster, an investigator in CRC 1442 Geometry: Deformations and Rigidity, and contributes to multiple research projects including Topics in Mathematics Münster T3: Models and universes, T4: Groups and actions, and T8: Random discrete structures and their limits. PhD in Linguistics, Christian-Albrechts-Universität zu Kiel (1988) Diplom in Mathematics, Christian-Albrechts-Universität zu Kiel (1989) PhD in Mathematics, University of Notre Dame (1994) Habilitation, "Model theory of groups and BN-pairs" (2000) Professor Tent's research bridges model theory, group theory, and geometry, with particular focus on finite Morley rank structures, BN-pairs, and sharply multiply transitive groups. Her work often combines methods from these areas to prove unexpected results, either constructing groups or incidence geometries with surprising model theoretic properties or using model theory to construct new and interesting geometries or groups. Her recent publications reveal a consistent trajectory connecting model theory with group-theoretic structures. She has made significant contributions to understanding sharply 2- and 3-transitive groups, finite Morley rank geometries, and the model theory of generalized polygons. Her work demonstrates how model-theoretic techniques can solve deep problems in group theory and geometry, particularly through the study of BN-pairs and incidence structures. DFG Research Fellowship (1996-1998) Bayerischer Habilitationsförderpreis (1998-2001) Heisenberg-Stipendium (2001-2004) ERC Consolidator Grants expert panel (2016, 2018) Elected to DFG Senate (2019) Professor Tent leads an active research group with current members including Marco Amelio, Dr. Benjamin Brück, Anna Cascioli, Lukas Jonuska, Silke Meissner, Zahra Mohammadi Khangheshlaghi, and Dr. Sam Shepherd. Her former research group members include Dr. Simon Andre, Dr. Isabel Müller, and Dr. Tim Clausen, among others. Her supervisory work spans both theoretical foundations and specific applications in geometric group theory and model theory. Her research group operates within the Institute for Mathematical Logic and Foundations Research at the University of Münster, collaborating closely with other researchers in the Mathematics Münster cluster. The group participates in various projects including CRC 1442 - C04: Group theoretic aspects of negative curvature, contributing to the vibrant mathematical research environment at one of Germany's leading mathematics institutions.
