Kurt Johansson is a Full Professor of Mathematics at KTH Royal Institute of Technology, Sweden. His academic journey includes roles as Associate Professor at KTH (1993-2001) and Uppsala University (1988-1993), alongside research funded by the Swedish Natural Science Research Council (1998-2003). His primary affiliations are within the Department of Probability, Mathematical Physics & Statistics at KTH, where he also coordinates courses on Differential Equations and Fourier Analysis. Education: BSc in Physics (1982) and PhD in Mathematics (1988), both from Uppsala University. Research interests focus on Probability Theory , Mathematical Physics , and Random Matrix Theory , with contributions to stochastic models, determinantal processes, and universality in statistical mechanics. Key Awards: Wallenberg Prize (1995), Rollo Davidson Prize (2000), Göran Gustafsson Prize (2002), Fellow of the American Mathematical Society (2012), and multiple Wallenberg Scholar grants (2011-2023). Grants: Major funding from the Swedish Research Council (VR), K&A Wallenberg Foundation, and others. His research group explores Random Matrices, Stochastic Models, and Analysis , with notable work on the Arctic Circle Theorem and KPZ universality class. Recent publications analyze Brownian directed percolation and domino tilings of the Aztec diamond, reflecting his focus on interdisciplinary applications of probability and mathematical physics.
Peter Druschel is a Professor and founding Director of the Max Planck Institute for Software Systems (MPI-SWS) in Saarbrücken, Germany. He holds adjunct professorships at Saarland University and the University of Maryland. His research focuses on distributed systems, operating systems, and privacy-preserving technologies. He earned his Ph.D. from the University of Arizona in 1994 and has held roles at Rice University since 1994, including Professor of Computer Science (2002–2005). Education: Ph.D. in Computer Science, University of Arizona (1994) Research Interests: Distributed systems, operating systems, network security, accountable computing, and privacy technologies. Current projects include privacy compliance in data systems (Thoth), secure communication (EbN), and privacy-aware image capture (I-Pic). Awards: SIGOPS Mark Weiser Award (2008) NSF CAREER Award (1995) Member of Academia Europaea and German Academy of Sciences Leopoldina Grants & Leadership: Leads the ERC Synergy Project imPACT, chairs the Max Planck Society’s Chemistry, Physics, and Technology Section, and collaborates with institutions like Cornell and Google. Advises on policy issues related to technology and privacy. Labs/Teams: Distributed Systems Group at MPI-SWS, collaborations with Microsoft Research and MIT. Current team includes students and postdocs working on privacy, security, and distributed systems.
Karl-Theodor Sturm is a Professor of Mathematics at the University of Bonn, holding this position since 1997. He is affiliated with the Institute for Applied Mathematics and leads the Cluster of Excellence Hausdorff Center for Mathematics. His academic journey includes a PhD (1989) and habilitation (1993) from the University of Erlangen-Nürnberg, followed by postdoctoral positions at Zurich, Erlangen-Nürnberg, and the Max Planck Institute for Mathematics in the Sciences (MPI Leipzig). He has held visiting professorships at Stanford, Toulouse, Paris, and Bonn. Sturm's research focuses on stochastic analysis and geometric analysis, particularly in optimal transport, metric measure spaces, synthetic curvature bounds, and diffusion processes. His work on synthetic Ricci curvature bounds, developed in competition with Cédric Villani, has been highly influential. He received the ERC Advanced Grant (2016-2022) for research on metric measure spaces and Ricci curvature, and was a Plenary Speaker at the 2020 European Congress of Mathematics. His leadership roles include Vice Chairman of Collaborative Research Center SFB 611 (2002–2012), Managing Director of the Institute for Applied Mathematics (2007–2010), and Coordinator of the Hausdorff Center for Mathematics (2012–2019). Awards include the Heisenberg Fellowship (1994) and recognition through numerous invited lectures and editorial roles. His mentorship has shaped the careers of prominent researchers such as Nicola Gigli and Jan Maas.
Ljubisa Stankovic is a Full Professor at the University of Montenegro with extensive academic and political experience. He has served as Rector of the University of Montenegro (2003-2008), Member of the National Academy of Sciences and Arts (CANU) since 1996, and Ambassador of Montenegro to the United Kingdom since 2010. As an IEEE Fellow (2012), he has made significant contributions to signal processing research. His research focuses on Signal Processing , particularly Time-Frequency Analysis , Data Processing in Joint Time and Frequency Domain , Analysis of Non-Stationary Signals , and Radar Signal Processing . With about 300 technical papers published (83 in leading international journals, mainly IEEE editions) and several textbooks in Signal Processing, his work has substantially influenced the field. The analysis of his recent publications reveals a consistent focus on advanced time-frequency methods applied to radar systems, non-stationary signal analysis, and emerging applications in machine learning and quantum processing. His research shows evolution from theoretical foundations toward practical implementations in communications, radar, and biomedical applications. His notable scientific achievements include: Member of the National Academy of Sciences and Arts (1996) Highest State award of Montenegro '13. jul' (1997) Fellow of the IEEE (2012) Fulbright fellowship (1984-1985) Alexander von Humboldt fellowship (1997) Volkswagen award grant (2001) Scientific Achievement Award by Montenegrin Academy of Science and Art (1991) Stankovic has held significant editorial positions including Associate Editor for IEEE Transactions on Image Processing, IEEE Signal Processing Letters, and IEEE Transactions on Signal Processing since 2003. He was also a member of the IEEE Signal Processing Society's Technical Committee on Theory and Methods (2002-2008). His research group received a Volkswagen Foundation research grant (2001-2003), demonstrating his ability to secure competitive funding. Beyond academia, he has held prominent political positions including Vice-president of Montenegro (1989-1991) and Member of Yugoslav Parliament (1992-1996).
Christa Cuchiero is a Professor at the Department of Statistics and Operations Research , University of Vienna , and an elected member of the Austrian Young Academy (Junge Akademie) since 2020. Her research bridges rigorous mathematics and cutting-edge applications in finance, machine learning, and stochastic analysis. Education: Christa earned her M.Sc. in 2006 from TU Wien with a thesis on affine interest-rate models, her Ph.D. in 2011 from ETH Zürich on affine and polynomial processes, and completed her Habilitation at the University of Vienna in 2018 on high-dimensional finance beyond classical paradigms. Research Interests: Her work centers on affine and polynomial processes , stochastic portfolio theory , signature methods , and infinite-dimensional stochastic analysis . Recent projects explore signature-based neural SDEs for option calibration, measure-valued diffusions for energy markets, and universal approximation properties of signature transforms. Awards & Recognition: Among her accolades are the FWF START Award 2019 , the Bruti-Liberati Visiting Fellowship 2018 , the ETH Medal 2012 for an outstanding Ph.D. dissertation, and the Prix de l’Institut Europlace de Finance 2017 for the best paper in finance. Contact: christa.cuchiero@univie.ac.at , Kolingasse 14-16, 05.47, 1090 Wien, Austria.
Andreas Rauber is an Associate Professor in the Department of Data Science at Technical University of Vienna. He serves as Curriculum Coordinator for Bachelor and Master programs in Business Informatics and Data Science, and chairs the Curriculum Commission for Business Informatics. His research focuses on Information Systems Engineering, Logic and Computation, and Visual Computing, addressing challenges in data management, digital preservation, and reproducibility in e-science. He leads projects like OS Trails and FAIR-AI, emphasizing FAIR principles and trustworthy research infrastructures. Rauber has contributed to over 150 publications, including works on data citation frameworks, adversarial ML defenses, and reproducibility in IR. His work bridges technical innovation with policy, exemplified through roles in the EOSC Support Office Austria and RDA Austria initiatives. Key projects include establishing FAIR data practices across universities and advancing digital preservation through repositories like DBRepo. He coordinates international collaborations, such as the EU-funded EOSC-Life and EGI Advanced Computing projects. His teaching spans courses in machine learning, information retrieval, and research methods, fostering next-generation data scientists.
Professor Dong Xu is a Tenured Professor in the Department of Computer Science at the University of Hong Kong (HKU), part of the School of Computing and Data Science. He holds a B.Eng. and Ph.D. from the University of Science and Technology of China (USTC). His career includes tenured roles at Nanyang Technological University and the University of Sydney, alongside postdoctoral research at Columbia University. His research focuses on Artificial Intelligence, Computer Vision, Multimedia, and Machine Learning , with applications in autonomous driving, AR/VR, medical image analysis, and video surveillance. Xu has authored over 150 papers in top journals and conferences, including CVPR, ICCV, and IEEE Transactions. He actively contributes to the academic community as an editorial board member for journals like ACM Computing Surveys and IEEE Transactions, and through leadership roles in conferences such as ACM Multimedia and ICME. Notable awards include Fellowships from IEEE and IAPR, and the IEEE Signal Processing Society Distinguished Lecturer title (2021–2022). Education: B.Eng. (USTC, 2001), Ph.D. (USTC, 2005) Professional Service: Program Coordinator of ACM Multimedia 2024, Guest Editor of over ten special issues.
Christoph Dellago is a full Professor of Computational Physics at the Faculty of Physics of the University of Vienna, where he has been a faculty member since 2003. He currently serves as Director of the Erwin Schrödinger Institute for Mathematics and Physics, Head of the Computational and Soft Matter Physics Group, and Project lead of EuroCC Austria - National Competence Centre for Supercomputing. Previously, he served as Dean of the Faculty of Physics (2009-2012) and Coordinator of the Doctoral College Advanced Functional Materials (DCAFM). Full Professor, Faculty of Physics, University of Vienna (2003-present) Director, Erwin Schrödinger Institute for Mathematics and Physics (2017-present) Head, Computational Physics and Soft Matter Group (2024-present) Coordinator, Doctoral College Advanced Functional Materials (DCAFM) Austrian Representative, Council of CECAM Dellago received his PhD in Physics from the University of Vienna in 1996, followed by postdoctoral research at UC Berkeley as a Schrödinger Fellow of the Austrian Science Foundation. His research focuses on developing computational methods to study rare events in condensed matter systems, particularly transition path sampling methodology for simulating nucleation, chemical reactions, and biomolecular reorganizations. He has pioneered the application of machine learning to molecular structure recognition and potential energy surfaces. Recent work examines self-assembly of nanocrystals, biopolymer folding, aqueous interfaces, phase separation in alloys, thermo-polarization, cavitation, and freezing phenomena. Analysis of Dellago's recent publications (2023-2025) reveals a strong emphasis on machine learning applications in computational physics, particularly neural network potentials for simulating water interfaces, crystal defects, and phase transitions. His work bridges traditional statistical mechanics with modern computational techniques, creating powerful tools for studying complex dynamical processes that occur on timescales far beyond conventional molecular dynamics simulations. The publications demonstrate increasing integration of machine learning with rare event sampling methods, reflecting the cutting-edge direction of computational statistical mechanics. Förderpreis der Stiftung Futura zur Förderung junger Südtiroler im Ausland (1997) The Raymond and Beverly Sackler Prize in the Physical Sciences (2005) UNIVIE Teaching Award of the University of Vienna (2014) Dellago leads an active research group with multiple PhD students and postdocs, focusing on computational statistical mechanics. His group develops trajectory-based sampling methods and machine learning approaches for molecular simulation. He has secured significant funding through EuroCC Austria and various research platforms including the Research Platform Accelerating Photoreaction Discovery and the Research Platform Erwin Schrödinger International Institute for Mathematics and Physics. His research has been supported by numerous grants enabling advanced computational infrastructure for high-performance simulations. The Dellago Group operates within the Computational and Soft Matter Physics division at the University of Vienna, with strong connections to the Research Network Data Science. The group collaborates extensively with international research institutions and maintains close ties with the Erwin Schrödinger Institute, which Dellago directs. Their research environment combines theoretical physics, computational chemistry, and machine learning expertise to tackle fundamental questions in condensed matter physics and soft matter systems.
M. Anton Ertl is an Associate Professor at TU Wien's Faculty of Informatics, Department of Compilers and Languages. His roles include teaching courses such as 'Compilers,' 'Efficient Programs,' and 'Stack-based languages.' He specializes in compiler back-ends, Forth, interpreters, programming languages, operating systems, and computer architectures. Ertl's research focuses on optimizing interpreters, compiler design, and hardware security, particularly addressing vulnerabilities like Spectre. His research interests span interpreter performance, compiler optimization, and Forth language evolution. Recent work includes exploring hardware-based Spectre mitigation, decompilers for Gforth, and stack-based language efficiency. He actively contributes to the EuroForth conferences as an editor and presenter. Ertl supervises students on topics like compiler construction, reverse engineering, and interpreter optimization. His academic contributions include over 50 publications, with a focus on virtual machine performance, compiler back-ends, and Forth-related advancements. He is a key figure in the Gforth project and regularly teaches advanced courses on programming languages and computer systems.
Thomas Gärtner is a Professor at the Institute of Logic and Computation within the Faculty of Informatics at Vienna University of Technology, leading the Machine Learning research group (E194-06). His work bridges theoretical machine learning with practical applications in chemistry, biology, and network analysis. His primary research focuses on graph neural networks (GNNs) and geometric deep learning, with significant contributions to GNN expressivity, graph transformations, and kernel methods for structured data. He explores fundamental questions about the limitations of message-passing architectures while developing practical enhancements like path-based extensions and expectation-complete representations. His chemical informatics work applies these techniques to binding affinity prediction, reaction classification, and solvent selection, demonstrating real-world impact in computational chemistry. Analysis of his 15 most recent publications (2023-2025) reveals three dominant research thrusts: theoretical GNN advancements (35% of articles), chemical informatics applications (40%), and novel learning frameworks (25%). The theoretical work increasingly addresses expressivity limitations through graph transformations and path-based approaches, while chemical applications show growing sophistication in molecular representation. Recent publications also indicate expanding interest in foundation models for graphs and robustness verification. He actively supervises master's students including Fabian Traxler (binding affinity prediction), Maximilian Plattner (SGD optimization), Fabian Jogl (graph transformations), and Thomas Schmied (reinforcement learning). His research is conducted through the Network Lab at TU Wien, where he serves as Principal Investigator for the Structured Data Learning with Generalized Similarities project.
Fabian Jogl is a PreDoc Researcher at the Vienna University of Technology (TU Wien) within the Faculty of Informatics, holding dual affiliations in the Department of Databases and Artificial Intelligence (Institute E192) and the Department of Machine Learning (Institute E194). His work centers on theoretical and applied aspects of graph neural networks under the StruDL (2023–2027) and VHH (2019–2023) research projects. His research focuses on the expressivity limits of graph neural networks, particularly investigating whether enhanced expressivity translates to better predictive performance. Key areas include outerplanar graph analysis, path-based GNN architectures, global feature integration, and connections to the Weisfeiler-Lehman hierarchy. He examines structural properties of GNNs through homomorphisms and graph transformations to advance graph representation learning. Recent publications reveal trends toward unifying theoretical expressivity frameworks with empirical validation across diverse graph datasets. His work bridges geometric deep learning for cell complexes and practical applications like historical film analysis via the Historian dataset. Jogl actively contributes to major conferences including NeurIPS, ICML, and LoG while co-supervising student projects in machine learning algorithms. As a core member of the StruDL project, he investigates deep learning model structures for graph data, extending prior work on historical film annotation under the VHH project. His technical contributions include novel GNN simulation techniques and methods for enhancing message-passing architectures through graph transformations.
Nathanael Berestycki is a University Professor (Univ.-Prof.) in the Department of Mathematics within the Faculty of Mathematics. His research spans multiple areas of probability theory and mathematical physics with a particular focus on stochastic processes and their connections to statistical mechanics and quantum gravity. Professor Berestycki's research interests center around probability theory, with significant contributions to the understanding of Gaussian free fields, Liouville quantum gravity, and random processes. His work explores deep connections between statistical mechanics and mathematical physics, particularly in the context of conformal invariance and scaling limits. He has made substantial contributions to the theory of dimers, Brownian motion, random graphs, and branching processes, often revealing unexpected connections between seemingly disparate areas of mathematics. Analysis of his recent publications shows a consistent focus on the intersection of probability theory and mathematical physics, with particular emphasis on conformal invariance properties, scaling limits of discrete models, and the mathematical structure of quantum gravity. His work often bridges theoretical developments with concrete applications in statistical mechanics, demonstrating how deep mathematical structures emerge from seemingly simple probabilistic models. The breadth of his research is reflected in publications across top probability and mathematical physics journals. Throughout his career, Berestycki has maintained a high level of productivity with consistent publication output across leading journals in probability theory and mathematical physics. His collaborative work with researchers across institutions demonstrates strong connections within the mathematical community, particularly in the European probability research network.
Norbert Mauser is a full Professor in the Department of Mathematics at the University of Vienna, where he has been affiliated since 1999. His research bridges mathematical analysis, computational physics, and applied mathematics with a focus on developing and analyzing numerical methods for complex physical systems. Mauser's research interests center on mathematical physics, particularly partial differential equations arising in quantum mechanics and magnetism. His work spans Schrödinger-type equations, many-body quantum systems, micromagnetics, and more recently, the integration of machine learning techniques with physics-based modeling. He has made significant contributions to the mathematical analysis of quantum systems, numerical methods for micromagnetics, and computational approaches to Bose-Einstein condensates. His recent publications (2023-2025) reveal a growing emphasis on machine learning applications in micromagnetics, with multiple papers on physics-informed machine learning for magnetic energy minimization and spin wave dynamics. This represents an evolution from his earlier foundational work on Schrödinger equations and quantum systems toward more applied computational approaches that integrate AI with physical modeling. His research consistently demonstrates strong mathematical rigor combined with practical computational implementations. Mauser leads or participates in multiple significant research projects including 'Adaptive Splitting for Magneto-Hydrodynamics in Astrophysics' (2022-2026), 'Taming Complexity in Partial Differential Systems' (2017-2026), and 'Numerical simulation of A-type and white dwarf stars' (2021-2023). He has an extensive collaboration network across Europe, frequently working with researchers in computational physics and applied mathematics. His academic activities include organizing conferences such as 'Inverse-Design Magnonics' (2024) and presenting invited talks on absorbing boundary conditions for quantum wave equations. With over 80 publications spanning more than two decades, Mauser maintains an active research program that continues to evolve with contemporary challenges in computational mathematical physics.
Iris Agresti is a researcher affiliated with the Faculty of Physics at Politecnico di Milano , specializing in quantum computing, quantum information, and machine learning. Her work bridges quantum optics and computational advancements, with recent publications in high-impact journals like Nature Photonics and Physical Review Research . Education: PhD in Physics. Research focuses on photonic processors for quantum machine learning, quantum state superposition, and integrated photonic systems for generating GHZ entangled states. Her 2025 article explores kernel-based quantum-enhanced machine learning, while 2024 work investigates time-reversed quantum evolution and high-fidelity photon entanglement. She has participated in international collaborations and workshops, including visits to Politecnico di Milano, and is part of funded projects like Photonic Reservoir Computing for Quantum Correlation Sets (2022–2025). Her work garners attention from news outlets, social media, and academic platforms like Mendeley.
Michael Fellows is an Elite Professor of Informatics at the University of Bergen, Norway (2016–present). He has held prominent academic positions at Charles Darwin University, Australia; University of Newcastle, Australia; University of Victoria, Canada; and Victoria University, New Zealand. He is a leading figure in parameterized complexity theory and computer science education. Education : Ph.D. in Computer Science, University of California (1985) M.A. in Mathematics, University of California (1982) Research Interests : Dr. Fellows has pioneered parameterized complexity theory and kernelization algorithms. His work bridges theoretical computer science with interdisciplinary applications in mathematical sciences communication. The Google Scholar articles suggest a secondary focus on sustainability science, life cycle assessment (LCA), and regional energy systems planning. Scientific Awards : 2018 Toppforsk Award (Norwegian Research Council) 2016 Order of Australia, Companion to the Queen (AC) 2014 Honorary Fellow of the Royal Society of New Zealand 2014 Inaugural EATCS Fellow 2014 EATCS–IPEC Nerode Prize 2014 ETH Zurich International Gold Medal of Honor 2007 Alexander von Humboldt Research Prize Advising and Grants : He has supervised notable PhD students including Lars Jaffe (University of Bergen) and Elena Prieto-Rodriguez (Newcastle). Research funding includes U.S. NSF grants, Australian Research Council Discovery Grants, and the 2018 Norwegian Toppforsk Award. His editorial roles span the Journal of Computer and System Sciences and ACM Transactions on Algorithms .
