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. 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.
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.
Carlo D'Eramo is a Professor of Reinforcement Learning and Computational Decision-Making at the University of Würzburg. He leads the LiteRL group at hessian.AI until 2025 and is affiliated with the Intelligent Autonomous Systems group at TU Darmstadt's Computer Science Department, as well as the Hessian Centre for Artificial Intelligence. Ph.D. : Information Technology, Politecnico di Milano (2019) Double MSc : Computer Engineering, Politecnico di Milano (2015) and University of Illinois at Chicago (2015) BSc : Computer Engineering, Politecnico di Milano (2011) His research focuses on lightweight reinforcement learning methods for adaptive autonomous agents, spanning multi-task/curriculum RL, multi-agent RL, deep RL, uncertainty quantification, residual learning, and planning. He developed MushroomRL, a widely adopted RL library, and investigates how agents can acquire real-world expert skills efficiently. The 15 most recent publications highlight trends in deep reinforcement learning architectures, adversarial and multi-agent systems, domain randomization, and curriculum design. Key subfields include optimal transport applications, entropy maximization, neural network distillation, and bounded rationality frameworks for robust learning. He has contributed to top venues like ICML, NeurIPS, AAAI, ICLR, JMLR, and IEEE Transactions on Pattern Analysis and Machine Intelligence, with a focus on advancing scalable and adaptive RL methodologies.
Prof. Felix Krahmer is a TUM Tenure Track Assistant Professor of Optimization and Data Analysis at the Department of Mathematics, Technische Universität München (TUM), part of the School of Computation, Information and Technology. Previously, he held a Junior Professorship (W1) for Mathematical Data Analysis at the University of Göttingen (2012–2015). His research focuses on mathematical foundations of data science, including compressed sensing, signal quantization, uncertainty quantification, and optimization algorithms. He has led an Emmy Noether research group and contributed to the ZeMat interdisciplinary project. Education : PhD in Mathematics (2009), New York University, advisors: Percy Deift & Sinan Güntürk MSc in Mathematics (2007), New York University BSc in Mathematics (2004), Jacobs University Bremen Research Interests : Krahmer’s work bridges theoretical mathematics and applied data science. Key areas include compressed sensing algorithms, high-dimensional signal reconstruction, quantization theory for analog-to-digital conversion, and optimization methods for inverse problems. He also explores applications in imaging (e.g., MRI reconstruction) and stochastic processes. Key Contributions : His research on phase retrieval, sigma-delta quantization, and compressed sensing recovery has advanced signal processing techniques. Recent efforts focus on uncertainty quantification for high-dimensional inverse problems and the mathematical analysis of consensus-based optimization. Grants & Awards : Emmy Noether Research Group Grant (DFG) Funding from BMBF (ZeMat project) Teaching & Outreach : Krahmer has taught advanced courses on compressed sensing, functional analysis, and random matrix theory. He co-organized workshops on probabilistic techniques and clinical risk prediction, emphasizing interdisciplinary collaboration. Labs/Teams : Member of the TUM Data Science Research Group, focusing on optimization, imaging, and uncertainty quantification. Active in the Munich Center for Quantum Science and Technology (MCQST).
Joel David Hamkins is the O’Hara Professor of Logic at the University of Notre Dame, with significant affiliations to logic and philosophy research communities in China and Japan. His work bridges set theory, computability, and philosophy of mathematics, focusing on foundational questions about infinity, truth, and mathematical existence. Key Research Areas : Set theory, potentialism, continuum hypothesis, surreal numbers, forcing, large cardinals, definability, halting problem history Recent Talks : Kobe University (2025), Notre Dame HPS Colloquium (2025), Fudan University seminars (2025), Peking University conference (2025) Scientific Contributions : 2024 arXiv paper on halting problem attribution, ongoing work on bi-interpretation of surreal arithmetic with ZFC, analysis of transitive submodel principles Awards & Recognitions : William Reinhardt Memorial Lecture (2025), former JSPS Fellowship at Kobe University Hamkins’ work reveals deep connections between technical set theory and philosophical inquiry, particularly through his modal logic approach to potentialism and analysis of truth nonabsoluteness. His 2024 paper with Theodor Nenu re-examines Turing’s legacy, while his technical collaborations with researchers from Fudan University and Oxford advance foundational mathematics.
Professor Steffen Dereich is a leading researcher in mathematical stochastics at the University of Münster's Faculty of Mathematics and Computer Science, where he serves as Professor at the Institute of Mathematical Stochastics. He is an active investigator in the Mathematics Münster cluster of excellence, contributing significantly to the fields of stochastic processes and machine learning theory. His primary research interests span Stochastic Processes , Machine Learning , Deep Learning , Complex Networks , and Stochastic Analysis . Dereich has developed a unique research program that bridges classical probability theory with modern machine learning challenges, particularly focusing on the mathematical foundations of optimization algorithms used in deep learning. His work on stochastic gradient descent methods, especially the Adam optimizer, has provided crucial theoretical insights into convergence properties and optimization landscapes. The 15 most recent publications reveal a strong trend toward mathematical analysis of deep learning, with approximately 70% of his work focusing on neural network optimization, convergence analysis, and theoretical foundations of machine learning algorithms. The remaining publications continue his earlier work on complex networks, stochastic processes, and branching structures, demonstrating how he has successfully connected his foundational work in probability with cutting-edge machine learning research. Professor Dereich actively supervises PhD students and maintains productive collaborations, particularly with Arnulf Jentzen and Sebastian Kassing. His research group at Münster has secured significant funding through the Mathematics Münster cluster, supporting multiple projects including T8: Random discrete structures and their limits, and T10: Deep learning and surrogate methods. His teaching portfolio includes advanced courses on Probability Theory, Stochastic Analysis, Markov Chains, and specialized seminars on Machine Learning and Financial Mathematics, reflecting his dual expertise in theoretical mathematics and applied data science.
Prof. Felix Brandt is a Professor of Algorithmic Game Theory at the Technical University of Munich (TUM), within the School of Computation, Information and Technology. His research focuses on algorithmic game theory, computational social choice, and their intersections with theoretical computer science, AI, and economics. Education: Diploma and PhD from TUM, postdoctoral research at Carnegie Mellon University and Stanford University. Habilitation from LMU Munich (2010). Research interests include social choice theory, mechanism design, and strategic behavior in multi-agent systems. Notable contributions include work on tournament solutions, probabilistic social choice, and Nash equilibrium characterizations. Recent articles explore Condorcet-consistent voting systems, stability in hedonic games, and axiomatic foundations of Nash equilibrium. Awards include the DFG Heisenberg Professorship (2010) and TUM Supervisory Award (2021). Advises over 10 PhD students and has supervised numerous postdocs. Active in editorial roles for journals like Games and Economic Behavior and Social Choice and Welfare .
Prof. Dr. Wolfgang Nejdl is a Professor at the Institute for Data Science within the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover. He serves as Executive Director of the L3S Research Centre and Leibniz Forschungszentrum Inclusive Citizenship. Web Science Information Retrieval Artificial Intelligence Deep Learning His recent research focuses on AI applications in medicine , multimodal data fusion , and ethical AI systems . Projects include CAIMed (AI in Causal Medicine) and DAISEC (AI & Cybersecurity). His publications span conferences like AAMAS, WWW, and SIGIR. Notable awards include membership in the National Academy of Science and Engineering (acatech) . Former students hold positions at institutions like Stanford, TU Dresden, and ETH Zürich. Current projects involve climate resilience AI , federated learning for healthcare , and quantum-inspired data science .
Professor Jörn Steuding holds the Professorship for Number Theory at the University of Würzburg since 2006, where he is affiliated with the Institute of Mathematics within the Faculty of Mathematics and Computer Science. His academic career includes a Ramon y Cajal research position at Universidad Autónoma de Madrid (2004-2006), postdoctoral work at the University of Frankfurt under Professors W. Schwarz and J. Wolfart (1999-2004), and completion of his habilitation at Frankfurt in 2004. His educational background includes a PhD from the University of Hannover in 1999 under Prof. G.J. Rieger, where he also served as an assistant from 1996-1999, and undergraduate studies in mathematics at Hannover from 1991-1995. Professor Steuding's research spans multiple areas of number theory, with particular focus on Zeta and L-functions (including zero distribution, universality properties, and connections to Random Matrix Theory), Diophantine analysis (covering approximation theory, equations, and the abc conjecture), elliptic curves and modular forms , algebraic number theory (including arithmetically equivalent fields), and elementary number theory with applications to primality testing and factorization. His work often bridges theoretical foundations with historical perspectives, as evidenced by his research on the Hurwitz brothers' contributions to complex continued fractions. His publication record demonstrates consistent contributions to leading journals in number theory, with research trends showing evolution from foundational work on Riemann zeta function zeros to broader investigations of L-functions in the Selberg class, Diophantine problems over quadratic fields, and historical aspects of number theory. His publications appear in prestigious journals including Mathematische Annalen, Acta Arithmetica, and the Bulletin of the American Mathematical Society. Professor Steuding has authored significant monographs including Diophantine Analysis (CRC Press/Chapman-Hall, 2005), Value distribution of L-functions (Springer Lecture Notes in Mathematics 1877, 2007), and Elementary Number Theory: A Gentle Introduction to Higher Mathematics (Springer Spektrum, 2015, co-authored with N. Oswald). He serves as the Erasmus Coordinator for his department alongside Dr. Jens Jordan, facilitating international academic exchanges. His research collaborations span multiple institutions, with notable co-authors including N. Oswald, M. Technau, H. Nagoshi, and L. Pankowski. Professor Steuding leads the Number Theory team at the University of Würzburg, maintaining an active research group focused on contemporary problems in analytic and algebraic number theory. His work continues to explore connections between classical number theory and modern mathematical physics through Random Matrix Theory applications.
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.
