Jan Dreier is a Research Fellow at the Institute of Logic and Computation at Vienna University of Technology. His research centers on structural graph theory and algorithmic meta-theorems, particularly exploring the boundaries of tractability for model checking problems. Dreier's work bridges theoretical computer science and discrete mathematics, focusing on graph decompositions, parameterized complexity, and logical expressiveness. Key research themes include monadic stability in graph classes, applications of model theory to computer science, and efficient algorithms for logical queries on structured graphs. His publication record shows consistent focus on graph sparsity concepts and algorithmic applications of logic, with recent work expanding into approximation methods for counting queries. Research demonstrates sophisticated applications of combinatorial methods to fundamental problems in computational complexity.
Huamin Qu is a Chair Professor in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST). He serves as the Founding Dean of the Academy of Interdisciplinary Studies (AIS), Founding Head of the Division of Emerging Interdisciplinary Areas (EMIA), and was the Founding Acting Head of Computational Media and Arts (CMA) at HKUST(GZ). Qu directs the VisLab and coordinates the Human-Computer Interaction (HCI) group. He obtained his BS in Mathematics from Xi'an Jiaotong University and MS/PhD in Computer Science from Stony Brook University. Qu's research integrates Data Visualization , Human-Computer Interaction , and Human-Centered AI , with applications in urban informatics, social networks, and explainable AI. His work focuses on developing interactive systems for big data analytics, visual storytelling, and AI-driven decision support. Research extends to multimodal communication, fintech, and augmented reality applications. His publications emphasize visual analytics for complex datasets (mobility, social media, financial), interaction techniques for immersive environments, and AI-enhanced visualization tools. Recent works explore explainable AI interfaces and large-scale data communication frameworks. IEEE Visualization Academy (2020) IEEE VGTC Technical Achievement Award AI 2000 Most Influential Scholar (2019, 2023, 2024) 21 Best Paper/Honorable Mention awards IBM Faculty Award (2009) APICTA Merit Award (2015) Yelp Dataset Grand Prize (2018) Qu has advised 48 PhD graduates (21 now faculty at institutions like UC Davis, University of Minnesota, Texas A&M) and 30 MPhil students. He secured major grants including RGC theme-based projects (digital citizenship, air pollution), UGC AoE (slope safety), and China's 973 Program. As VisLab director, he leads 20+ researchers in visualization/HCI projects adopted by Microsoft, IBM, Huawei, and Tencent.
Maria Chudnovsky is a Professor of Mathematics at Princeton University and a former Professor of IEOR and Mathematics at Columbia University (2006-2014). Her research focuses on graph theory and combinatorics, with significant contributions to structural graph theory, perfect graphs, and algorithmic applications. Education: B.A. Summa Cum Laude (1996) and M.Sc. (1999) from Technion, Ph.D. (2003) from Princeton University Her work addresses fundamental problems in graph coloring, forbidden induced subgraphs, and combinatorial optimization, including the proof of the Strong Perfect Graph Theorem and development of algorithms for detecting graph structures. Notable awards include the MacArthur Foundation Fellowship (2013-2017), D.R. Fulkerson Prize (2009), and Henry Burchard Fine Professor of Mathematics (2022). She has held prestigious fellowships such as the Clay Mathematics Institute Research Fellowship (2003-2008). Grants: NSF DMS-EPSRC Grant DMS-2120644 (2021-2024), US Army Research Office Grant W911NF-16-1-0404 (2016-2020), and multiple NSF grants She has advised numerous PhD/MSc students and postdocs, including Sophie Spirkl, Mingxian Zhong, and Tara Abrishami. Her outreach includes popular science communication on YouTube's Numberphile and participation in initiatives promoting women in STEM.
Wojciech Rytter is a full professor at the Institute of Informatics, Department of Mathematics and Informatics at the University of Warsaw, Poland, holding this position continuously since October 1971. His academic career includes significant international appointments as full professor at New Jersey Institute of Technology (2002-2004), Liverpool University (1997-2002), and Bonn University (1994-1995), and as visiting professor at University of California, Riverside (1992-1993) and University of Warwick (1985-1986). He earned his MSc in 1971, PhD in 1975, habilitation in 1985, and was awarded the scientific degree of professor in 1997, all from Warsaw University. Professor Rytter's research focuses on the design and analysis of computer algorithms, with particular expertise in automata and formal languages, parallel algorithms, and text algorithms. His work spans efficient sequential and parallel algorithms, automata theory, complexity of recognition and parsing of context-free languages, pattern matching, algorithmics of WWW, parallel combinatorial computing, graph-theoretic algorithms, and algorithmics of highly compressible objects. His theoretical contributions have practical applications in computational biology, bioinformatics, and text processing systems. His recent publications (2022-2025) demonstrate continued activity in string algorithms, particularly in pattern matching, string covers, and combinatorics on words, with a strong focus on theoretical computer science with applications in bioinformatics. 200 problems on automata, languages, computations (Cambridge University Press 2023) 125 Problems in Text Algorithms (Cambridge University Press, 2021) Jewels of Stringology (World Scientific, 2002) Fast parallel algorithms for matching problems in graphs (Oxford University Press 1998) Text algorithms (Oxford University Press 1994) Professor Rytter has collaborated extensively with researchers including Jakub Radoszewski, Tomasz Walen, Tomasz Kociumaka, and Maxime Crochemore. He is a member of the Academy of Europe (elected 2011, Informatics section) and has authored or co-authored more than 130 publications. He maintains an active research laboratory focused on string algorithms and combinatorics on words at the University of Warsaw.
Claudia Plant is a Professor in the Faculty of Computer Science , leading the Research Group Data Mining and Machine Learning . Her research focuses on clustering algorithms, data mining, and machine learning applications in areas like biomedical data, wind energy, and causality inference. She has contributed to projects such as Knowledge-infused Deep Learning for Natural Language Processing (2020–2028) and Hybrid Computational Sciences (2021–2021). Plant has authored over 160 publications, with recent work emphasizing deep learning, anomaly detection, and GPU-optimized algorithms. She actively engages in academic activities, including talks on clustering methods and interdisciplinary projects like Governing Algorithms: The Politics of Data and Decision-Making . Her research interests span clustering algorithms , graph neural networks , causality discovery , and ethical digital transformation . Notable projects include causal analysis of wind farm dynamics and AI-enhanced education tools. Plant’s work bridges computational methods with societal challenges, such as empowering marginalized communities through ethical technology adoption.
Goulnara Arzhantseva is a full Professor at the Department of Mathematics within the Faculty of Mathematics at the University of Vienna. With an active research career spanning over a decade, she has established herself as a prominent figure in geometric group theory and related fields. Her work demonstrates continuous scholarly output with publications appearing consistently from 2011 through 2024. Professor Arzhantseva's research focuses primarily on geometric group theory, with particular emphasis on discrete groups, metric geometry, approximation properties of groups, small cancellation theory, expanders, and sofic groups. Her work bridges pure mathematics with applications in metric geometry and functional analysis. The breadth of her research is evident in her collaborations with numerous mathematicians across different institutions and countries. Her publication record shows a clear trajectory of increasingly sophisticated work in geometric group theory. The most recent publications (2022-2024) focus on metric approximations of discrete groups, spectral gap properties, origami expanders, and acylindrical hyperbolicity in cubical small cancellation groups. These works represent cutting-edge research at the intersection of group theory, geometry, and combinatorics. Scientific Awards: Prize mentioned in search results (4 total, specific names not provided) Prize mentioned in search results (4 total, specific names not provided) Prize mentioned in search results (4 total, specific names not provided) Prize mentioned in search results (4 total, specific names not provided) Professor Arzhantseva has been actively involved in numerous research projects, with records showing 7 projects between 2011-2024. Her collaborative approach is evident in her extensive publication record, which includes significant contributions to prestigious mathematics journals. While specific details about her advising activities are not provided in the available information, her substantial research output suggests active mentorship of graduate students in geometric group theory.
Michael J. Schlosser is a faculty member at the Faculty of Mathematics , University of Vienna. His research focuses on combinatorics, number theory, and special functions, with a particular emphasis on hypergeometric and q-series, elliptic extensions, and rook theory. He has authored numerous publications in collaboration with prominent mathematicians such as Victor Guo, Meesue Yoo, and Christian Krattenthaler. Research Interests : Combinatorics and hypergeometric series Elliptic functions and their applications Partition theory and supercongruences Matrix inversions and determinant evaluations Students : Josef Küstner (Ph.D., 2022) Christian Stump (Ph.D., 2008) Editorial Roles : Associate Editor, Journal of Mathematical Analysis and Applications Editorial Board Member, The Ramanujan Journal Editorial Board Member, Journal of Algebraic Combinatorics
Johannes Brandstetter is an Associate Professor at the Institute for Machine Learning at Johannes Kepler University Linz (JKU) where he leads the "AI for data-driven simulations" research group. He is also Co-founder and Chief Scientist at Emmi AI, bridging academic research with industrial applications in AI-driven physics simulation. Brandstetter earned his PhD after working at CERN's CMS experiment on Higgs boson physics. In 2018, he transitioned to machine learning, joining Sepp Hochreiter's research group in Linz. From 2021-2023, he worked at the Amsterdam Machine Learning Lab under Max Welling and Microsoft Research, developing expertise in Geometric Deep Learning and neural surrogates for partial differential equations. He returned to JKU in October 2023 to establish his own research group. His research spans Machine Learning, Deep Learning, and Physics-Informed Machine Learning with focus areas including Neural PDE solvers, Computational Fluid Dynamics, and Climate Modeling. Brandstetter believes AI is poised to revolutionize industrial-scale simulations, potentially saving thousands of compute hours across engineering domains. His work integrates computer vision, numerical simulation, and engineering components to advance data-driven approaches. Recent publications reveal a strong trend toward foundation models for scientific applications, particularly in atmospheric modeling (Aurora), geometric deep learning, and neural surrogates for complex physical systems. His interdisciplinary work spans computer vision, climate science, computational physics, and engineering, demonstrating the versatility of his research approach. Principal Investigator for "AlKa-DL: Alpine karst spring discharge prediction" (FWF-funded, 2024-2027) Principal Investigator for Cluster of Excellence "Bilateral Artificial Intelligence" (FWF-funded, 2024-2029) Co-PI for "Fast, efficient and flexible CFD simulation through generative AI" (FFG-funded, 2025-2026) As an educator and researcher, Brandstetter actively engages with the scientific community through invited talks at major conferences including presentations on "Closing the Gap Between Scientific Foundation Models and Real-World Applications" (March 2025) and "Scientific Machine Learning for Science and Engineering" (February 2025).
Ivan Viola is an Associate Professor at the Institute of Computer Graphics and Algorithms, part of the Faculty of Informatics at TU Wien, Austria. He holds a leave of absence until December 2024 while also being affiliated with King Abdullah University of Science and Technology (KAUST) as an Associate Professor funded by the Vienna Research Groups program. His research focuses on visualization techniques in medicine, biological sciences, and earth sciences, with a specialty in illustrative visualization and DNA-nanotechnology applications. Viola has contributed over 100 scientific works and serves as a reviewer and panelist for major conferences in computer graphics and visualization. Education: M.Sc. (2002) and Ph.D. (2005) in Computer Graphics from TU Wien. Postdoctoral research at the University of Bergen (2006-2011), where he became Full Professor before returning to TU Wien. Research Interests: Whole-cell visualization Molecular modeling Interactive 3D environments Biomedical visualization Data-driven colormap techniques Awards: IEEE VIS 2017 Best Paper Honorable Mention, 'Best Overall Concept' for CellView, and multiple visualization awards. Active in EuroVis and IEEE VIS organizing roles. Grants & Supervision: Leads the Visualization Group at TU Wien, supervising student projects and master’s theses. Involved in grants like the Vienna Research Groups program. Labs/Teams: Visualization Group at TU Wien, collaborating on projects like CellView and Molecumentary.
Mihyun Kang is a Professor in the Institute of Discrete Mathematics at Graz University of Technology (TU Graz), leading the Combinatorics Group. She holds significant academic positions and has been recognized with a Heisenberg Fellowship (German Research Foundation) and a Friedrich Wilhelm Bessel Research Award (Alexander von Humboldt Foundation). Her research focuses on combinatorics, discrete probability, and algorithms, with a specialization in random graph theory. She contributes to editorial boards, including Random Structures & Algorithms . Her research interests emphasize high-dimensional graphs, percolation processes, and structural properties of random graphs. She explores topics such as phase transitions, graph enumeration, and algorithmic applications in combinatorial problems. Her work bridges theoretical foundations with practical applications in algorithm design and probabilistic modeling. Dr. Kang’s scientific achievements include pioneering studies on hypergraphs, bootstrap percolation, and the evolution of random graph processes. Her recent publications highlight advancements in understanding graph components, connectivity thresholds, and universality phenomena in random structures. She collaborates widely, contributing to conferences and international initiatives like the SFB “Discrete random structures: enumeration and scaling limits.” Her professional activities include advising doctoral students and supervising research projects at TU Graz. She leads the Combinatorics Group, which hosts the Graz Combinatorics Seminar and actively engages in academic outreach. Her contributions extend to textbook authorship, including Diskrete Mathematik für die Informatik , and she maintains an active presence in discrete mathematics education and research.
Anna Beer is a researcher in the Faculty of Computer Science, specializing in data mining and machine learning with a focus on density-based clustering, spectral clustering, and interactive clustering frameworks. She holds a BSc and MSc in computer science and maintains an ORCID profile (https://orcid.org/0000-0002-6890-997X) for her research contributions. Research Themes: Development of clustering algorithms (e.g., DISCO, Scar, LUCKe), fairness in density-based clustering (FairDen), and applications to molecular dynamics and climate research (DROPP). Collaborations: Works with colleagues like Ira Assent, Christian Plant, and Lars Krieger, with recent contributions to conferences like ICLR 2025. Activities: Presented research on density-connectivity distance at a 2023 oral contribution. Publications: 9 publications since 2019, including 3 in 2025 and 6 in 2024, covering topics from cluster evaluation to deep active learning strategies.
Alexander Selzer is a PreDoc Researcher in Databases and Artificial Intelligence at Vienna University of Technology. His research focuses on query optimization, database systems, and AI-driven data processing. His work bridges theoretical computer science and practical database engineering, emphasizing efficient algorithms for modern data challenges. Recent projects explore machine learning applications in query performance and distributed computing frameworks. He mentors students in database optimization research, including projects on structure-guided query techniques and SQL-based system enhancements.
Zhang Yi-Cheng is a Full Professor of Theoretical Physics at the University of Fribourg, Switzerland, since 1992. His academic career includes visiting professorships at Nordita (Denmark) and INFN (Italy), and postdoctoral research at Brookhaven National Lab (USA). He specializes in interdisciplinary fields such as Econophysics , Statistical physics , and Complex network sciences , focusing on applications in financial markets, social systems, and global trade networks. His research explores topics like market dynamics, network structures, and algorithmic ranking systems. Notable awards include the 2011 Honorary Director of the Complexity Sciences Research Center and recognition as a 2011 Chinese '1000 Talents' awardee . His work bridges physics-based methodologies with socio-economic systems, addressing challenges in information-driven economies and networked societies. Zhang has contributed to influential studies on ranking algorithms, percolation theory in networks, and the interplay between economic complexity and trade. His interdisciplinary approach has led to advancements in understanding systemic risks, market inefficiencies, and the role of information in shaping global economic interactions.
Kurt Mehlhorn is a distinguished academic in computer science, affiliated with the Universität des Saarlandes and the Max-Planck-Institut für Informatik . He has served as Professor of Computer Science since 1975 and as Director of the Max Planck Institute since 1990. His work bridges theoretical and applied computer science, with a focus on algorithmic design and implementation. Born : 29 August 1949, Ingolstadt, Germany Education : TU München (1968–1971), Cornell University (1971–1974), PhD in Computer Science (1974) Mehlhorn’s research spans foundational and applied domains, including Data Structures , Graph Algorithms , Computational Geometry , and Software Libraries . His contributions to algorithm engineering and EU-funded projects like ALCOM and GALIA have advanced the field. He co-founded Algorithmic Solutions GmbH in 1995 to commercialize algorithmic tools. Notable scientific awards : Leibniz-Award (1986) Humboldt-Award (1989) ACM Paris Kanellakis Theory and Practice Award (2011) Academia Europaea Erasmus Medal (2014) His editorial leadership includes roles at Algorithmica , SIAM Journal of Computing , and ACM Transactions on Algorithms . He has held advisory positions at institutions like IST Austria , ETH Zürich , and Simons Foundation , while directing research clusters such as the Cluster of Excellence in Multimodal Computing and the Indo Max Planck Center .
Emmanuel Breuillard is a Professor of Pure Mathematics at the University of Oxford. Previously, he held positions as Sadleirian Professor at the University of Cambridge (2017-2021), Professor at Université Paris-Sud (2008-2017), and Associate Hadamard Professor at École Polytechnique (2006-2008). His academic journey includes a PhD from Université Paris-Sud and Yale University, supervised by Frédéric Paulin and Gregory Margulis, and a Master in Mathematics from Cambridge. PhD: Université Paris-Sud (2004), supervised by Frédéric Paulin; Yale University, supervised by Gregory Margulis Master: University of Cambridge (2000) Ecole Normale Superieure (1997-1999) His research spans Combinatorics , Geometric Group Theory , Ergodic Theory , and Number Theory , focusing on approximate groups, expansion in finite simple groups, and Diophantine geometry. His recent work analyzes Elekes-Szabo problems in projective geometries. He has authored 50+ publications in prestigious journals, addressing topics like random polynomials, C*-simplicity, and uniform independence in linear groups. Fellow of the Royal Society (2024) ERC Consolidator Grant (2014-2019) Prix Charles-Louis de Freycinet (2013) European Mathematical Society Prize (2012) ERC Starting Grant (2008-2013) Cours Péccot, Collège de France (2006)
