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Ecaterina Sava-HussView profile

Professor

• Stochastic Processes
• Probability Theory
• Random Walks
• +5 more

Ecaterina Sava-Huss is a Full Professor in Mathematics at the University of Innsbruck, Austria, affiliated with the Department of Mathematics within the Faculty of Mathematics, Computer Science and Physics. She holds a PhD from Graz University of Technology (2010) and a Habilitation in Mathematics (2019). Previously, she served as an associate professor and tenure-track assistant professor at the University of Innsbruck, and as an assistant professor and visiting scholar at institutions including TU Graz and Cornell University. Her research focuses on stochastic processes, particularly random walks and rotor walks on graphs, fractals, and aggregation models. Key interests include the interplay between structural properties of infinite state spaces and stochastic processes. She has organized conferences such as the Austrian Stochastics Days and is the outreach coordinator for the Institute of Mathematics, engaging in public science initiatives like the MIP Day and Girls' Day. Her funding includes an FWF Grant P34129 (2021–2025) on growth models and quasi-random walks, and a prior Erwin Schrödinger Fellowship (2015–2016). She has supervised numerous PhD and Master’s students, with research topics ranging from branching processes to quantum probability. Her work has been published in journals such as Advances in Applied Probability , Bernoulli , and Journal of Fractal Geometry .

• Partial Differential Equations
• Computational Micromagnetism
• Numerical Analysis
• +5 more

Michael Feischl is a Professor for Computational PDEs at TU Wien (since 2022) and holds an ERC Consolidator Grant for his project "New Frontiers in Optimal Adaptivity" (2024–2029). His research focuses on partial differential equations with random coefficients, computational micromagnetism (Landau-Lifshitz-Gilbert equation), and optimal adaptive mesh refinement techniques. He leads the Computational PDEs research group within the Institute of Analysis and Scientific Computing. Education and career highlights include roles as Associate Professor at TU Wien (2019–2022), W2 Professor at University of Bonn (2017–2018), and Junior Research Group Leader at KIT (2015–2017). His work bridges numerical analysis, computational physics, and machine learning, with a strong emphasis on rigorous mathematical foundations and algorithmic efficiency. Research interests include: Adaptive finite element and boundary element methods Stochastic modeling and uncertainty quantification Computational methods for micromagnetic simulations Machine learning applications in numerical analysis His recent work explores optimal adaptivity for time-dependent PDEs, neural network-based solvers, and efficient discretization strategies for complex physical systems. Key contributions include advancements in a posteriori error estimation and hierarchical training of neural networks.

Karimi Hamid RezaView profile

Professor

• Fault Diagnosis
• Vibration Systems
• Control Systems
• +7 more

Hamid Reza Karimi is a Professor of Applied Mechanics at the Department of Mechanical Engineering, Politecnico di Milano, Italy. He previously served as Full Professor at the University of Agder, Norway, and held research positions in Germany, USA, and other institutions. Current Affiliation: Politecnico di Milano, Italy Previous Affiliation: University of Agder, Norway Dr. Karimi is a leading expert in control systems, fault diagnosis, and mechatronics. His research focuses on modeling, control, and health monitoring of complex industrial systems, particularly wind turbines and automotive systems. He has published over 500 ISI-indexed journal papers and contributed to 9 edited books. Scientific Awards: Web of Science Highly Cited Researcher in Engineering (2016-2021) Fellow of The Asia-Pacific Artificial Intelligence Association (2022) Distinguished Fellow of The International Institute of Acoustics and Vibration (2021) August-Wilhelm-Scheer Visiting Professorship Award (2015) He serves as Chief Editor and Associate Editor for multiple international journals and has participated as keynote speaker in numerous conferences. His work emphasizes interdisciplinary collaboration between academia and industry in Europe.

Bárány ImreView profile

Research Professor

• Discrete Geometry
• Combinatorics
• Convex Geometry
• +7 more

Imre Bárány is a Research Professor at the Alfréd Rényi Institute of Mathematics, Hungarian Academy of Sciences, and holds the Astor Professor of Pure Mathematics position at University College London (half-time). His career spans over four decades with visiting roles at Yale University, MSRI, Microsoft Research, and others. Bárány graduated from Eötvös University (1971), earned his PhD (1982) and Doctor of Sciences (1993) from the Hungarian Academy of Sciences, and became a member of the Hungarian Academy of Sciences in 2010. His research interests include discrete geometry, combinatorics, convexity, algebraic topology, and their applications in computer science and operations research. Key areas of focus are random polytopes, lattice points, Helly-type theorems, and geometric optimization. Bárány’s recent work, such as his 2020 paper on Carathéodory-Helly-Tverberg theorems and 2016 analysis of curve-hyperplane interactions, showcases advancements in geometric combinatorics. Over 170 publications span journals like Discrete & Computational Geometry and Annals of Probability. Awards: Rényi Prize (1988), Hungarian Academy Prize (1998), ERC Advanced Grant (2011), AMS Fellow (2012), Szechenyi Prize (2016). Grants & Leadership: Led the ERC-funded 'Discrete and Convex Geometry' project. Editorial roles include Discrete & Computational Geometry (since 1991) and Combinatorica (Editor-in-Chief since 2020).

Maximilian ThiessenView profile

Researcher

• Graph Neural Networks
• Active Learning
• Learning Theory
• +1 more

Maximilian Thiessen is a PhD student in machine learning at Technische Universität Wien , supervised by Thomas Gärtner. He is affiliated with the machine learning research unit and collaborates with the Laila lab in Milan. Research Interests : Learning with graphs Active learning frameworks Convexity theory in ML Computational learning theory Recent Research Trends include: (1) Expressive GNN architectures for outerplanar graphs (2025), (2) Generalized boosting theory through game frameworks (2024), (3) Efficient monophonic halfspace learning (2024), (4) Abstention mechanisms in contextual bandits (2024), (5) Global feature extensions in GNNs (2023), and (6) Expectation-complete graph representations (2023). Scientific Awards : 2024: DOC Fellowship from Austrian Academy of Sciences 2023: Best Poster Award at G-Research's ICML Poster Party Community Contributions : Organizer of Mining and Learning with Graphs (MLG) workshops at ECMLPKDD 2022-2024, co-organizer of Graph Learning on Wednesdays (GLOW) reading group, and session chair at ECMLPKDD'23.

Juraj HromkovičView profile

Professor

• Theoretical Computer Science
• Algorithms
• Complexity Theory
• +6 more

Juraj Hromkovič is a Full Professor of Computer Science at the Swiss Federal Institute of Technology (ETH Zurich) , holding this position since 2004. His academic career spans multiple institutions, including Professorships at Christian Albrechts University in Kiel (1994-1997), RWTH Aachen (1997-2003), and earlier roles at Comenius University and the University of Paderborn. Education : 1977–1982: Studied Computer Science at Comenius University, Bratislava 1982: Dr. rer. nat. (PhD equivalent) from Comenius University 1986: CSc (Ph.D.) in Theoretical Cybernetics 1990: Dr. Sc. in Mathematics and Physics Research Interests focus on Theoretical Computer Science , particularly Algorithms , Complexity Theory , and Communication Protocols . His work explores Randomized Algorithms , Approximation Techniques , and Automata Theory , with notable contributions to understanding exponential gaps in computational models and optimizing information dissemination in networks. His publications span books, journal articles, and conference papers addressing foundational and applied aspects of computing. Scientific Awards include membership in the Slovak Academy of Sciences (2001) and two awards from the Slovak Literature Foundation for Scientific Literature (2002, 2004). He has supervised 11 PhD dissertations and one habilitation, demonstrating his commitment to academic mentorship.

Jaroslav NesetrilView profile

Professor

• Combinatorics
• Graph Theory
• Algorithmics
• +8 more

Professor Jaroslav Nesetril is affiliated with Charles University's Faculty of Mathematics and Physics since 1970 and serves as Director of the Institute of Theoretical Informatics (ITI) since 2000. He holds a full professor position since 1993 and has maintained adjunct roles at University of Waterloo (2000-2003) and visiting positions at prestigious institutions including University of Bonn (John von Neumann Professor) and University of Chicago. Fields of scholarship: Combinatorics, Graph Theory, Algorithmics, and Discrete Mathematics Leadership roles: Department Chairman (1986-1999), Director of ITI (2000-present) His research focuses on combinatorial structures, graph homomorphisms, and computational complexity. As a leading figure in discrete mathematics, he has contributed extensively to Ramsey theory, sparsity, and combinatorial number theory through both research and editorial leadership. Scientific honors include: Doctor Honoris Causa from Universite Bordeaux I (2009) and University of Alaska (2005) Gold medal from Charles University's Faculty of Mathematics and Physics (2011) State prize of Czechoslovakia (1985) Memberships in Academia Europaea (2012) and Hungarian Academy of Sciences (2013) He has served on editorial boards of major journals including Combinatorica, Journal of Graph Theory, and Random Structures and Algorithms, while maintaining active research leadership through DIMATIA Centre and European networks like COMBSTRU.

Jin-Yi CaiView profile

Professor

• Theoretical Computer Science
• Computational Complexity Theory
• Holographic Algorithms
• +3 more

Jin-Yi Cai is a distinguished Professor of Computer Science and Steenbock Professor of Mathematical Sciences at the University of Wisconsin at Madison, where he has been a faculty member since 2000. Previously, he held academic positions at State University of New York at Buffalo (Professor 1996-2000, Associate Professor 1993-1996), Princeton University (Assistant Professor 1989-1993), and Yale University (Assistant Professor 1986-1989). He has also been a Radcliffe Institute Fellow at Harvard University (2007-2008) and a Guggenheim Fellow and Visiting Professor at the University of Toronto (1999-2000). Dr. Cai earned his Ph.D. in Computer Science from Cornell University in 1986, an M.A. in Mathematics from Temple University in 1983, and a Certificate in Mathematics from Fudan University in 1981. His academic journey spans prestigious institutions across the United States and demonstrates a consistent trajectory of scholarly excellence. Professor Cai's research focuses on theoretical computer science, particularly computational complexity theory, with significant contributions to holographic algorithms, counting constraint satisfaction problems, and graph homomorphisms. His work bridges computer science and mathematics, developing sophisticated algorithms and proving fundamental complexity results. His research has evolved from foundational work in structural complexity and oracle separations to specialized work in holographic algorithms and counting problems, demonstrating both depth and breadth in theoretical computer science. His publication record shows a consistent output of high-impact research, with major contributions spanning over three decades. His work on holographic algorithms represents a particularly innovative strand of research that has opened new avenues in computational complexity. The progression of his research demonstrates increasing specialization in counting problems while maintaining connections to broader theoretical frameworks in computer science and mathematics. 2022 Simons Fellowship 2022 CCF Award for Overseas Outstanding Contribution 2022 Fellow, American Mathematical Society (AMS) 2021 Fulkerson Prize in Discrete Mathematics 2021 Gödel Prize in Theoretical Computer Science 2014 Steenbock Professorship, UW Madison 2001 ACM Fellow 1998 John Simon Guggenheim Fellowship 1994 Sloan Fellowship Professor Cai has served as Editor of the Journal of Computer and System Sciences and Associate Editor of the Journal of Computational Complexity. His work has been recognized with numerous prestigious fellowships including the Guggenheim Fellowship, Sloan Fellowship, and Humboldt Research Award. His research has had significant impact in theoretical computer science, earning him the Gödel Prize and Fulkerson Prize, two of the most prestigious awards in theoretical computer science and discrete mathematics respectively.

Emo WelzlView profile

Professor

• Foundations of computer science
• Computational geometry
• Combinatorial models for optimization
• +5 more

Emo Welzl has been a Full Professor of Computer Science at the Institute for Theoretical Computer Science, ETH Zurich since 1996. His academic journey spans institutions including Free University of Berlin, University of Denver, Rijks University Leiden, and collaborations through graduate programs like the Berlin-Zurich Graduate Program in 'Combinatorics, Geometry, and Computation'. 1996–Present: Full Professor of Computer Science, ETH Zurich 1987–1996: Full Professor of Mathematics (theory of computation), Free University of Berlin Education Diplom in Applied Mathematics, Graz University of Technology (1981) Ph.D. in Formal Languages, Graz University of Technology (1983) Habilitation in Foundations of Computer Science, Graz University of Technology (1988) Research Interests Welzl's work focuses on foundations of computer science , with emphasis on algorithms , data structures , and computational geometry . Key subfields include combinatorial optimization , randomized methods , and discrete geometry . His recent interests extend to satisfiability problems. Honours and Awards Max Planck Prize (1992) Gottfried Wilhelm Leibniz-Prize (1995) ACM Fellow (1998) Member of Leopoldina (2005), Academia Europaea (2006), and Berlin-Brandenburg Academy (2007) Service and Leadership He has chaired program committees for major conferences like the ACM Symposium on Computational Geometry (1995), ICALP (2000), and European Symposium on Algorithms (2007). He served on editorial boards of journals including the Journal of Discrete and Computational Geometry and ACM Transactions on Computation Theory .

Gregory GutinView profile

Professor

• Combinatorial Optimization
• Algorithms
• Graph Theory
• +2 more

Gregory Gutin is a Professor of Computer Science at Royal Holloway, University of London, UK. He has held academic positions at Brunel University (Lecturer in Mathematics, 1996), Odense University (Visiting Lecturer in Computer Science, 1995; Postdoctoral Researcher, 1993), and was a PhD student at Tel Aviv University's School of Mathematics (1991). His career spans roles as a School Teacher in Gomel (Byelorussia, 1979), Researcher in Byelorussian institutions (1982-1987), and academic staff in the UK, Denmark, and Israel. He earned a PhD in Mathematics from Tel Aviv University, with prior research roles in Byelorussia (geology, oil, mathematics). His work bridges theoretical and applied computer science, focusing on combinatorial optimization, parameterized algorithms, and information security. Dr. Gutin's research centers on combinatorial optimization and parameterized algorithms , with applications in graph theory , constraint satisfaction , and access control in information security. His publications address arc routing problems, workflow satisfiability, and probabilistic methods for parameterized complexity, contributing both to foundational theory and practical implementations. His selected publications highlight a focus on fixed-parameter tractable algorithms for constraint satisfaction, arc routing in operations research, and access control mechanisms. These works solved open problems in algorithm design and influenced subsequent research in parameterized complexity and security systems. Best Paper Award at ACM SACMAT 2016 Best Paper Award at ACM SACMAT 2015 Royal Society Wolfson Research Merit Award 2014 Kirkman Medal 1996 Wolf Prize for PhD Students 1992 Dr. Gutin has collaborated extensively with researchers like Magnus Wahlstrom, Anders Yeo, and David Karapetyan. His work on workflow satisfiability introduced novel constraint classes used in access control systems, and he co-authored the influential textbook Digraphs: Theory, Algorithms and Applications (2009). The Royal Society award in 2014 recognized his sustained contributions to algorithmic research.

Philipp GrohsView profile

Professor

• Mathematical Data Science
• Deep Learning Algorithms
• High-Dimensional Partial Differential Equations
• +7 more

Prof. Dr. Philipp Grohs is a full professor of Mathematical Data Science at the University of Vienna and head of the Mathematical Data Science group at RICAM (Austrian Academy of Sciences). He holds a MSc from TU Vienna (2006) and a PhD from the same institution (2007). After postdoc positions at TU Graz, KAUST, and ETH Zurich, he became an assistant professor at ETH Zurich in 2011 before moving to the University of Vienna in 2016. His research focuses on designing efficient algorithms for signal processing, computational sciences, and finance, with recent contributions to understanding deep learning algorithms and solving high-dimensional PDEs using machine learning. He has received the ETH Latsis Prize (2014) and was selected for an Alexander von Humboldt Professorship (2019). Research Interests: Mathematical foundations of deep learning High-dimensional PDEs and their numerical solutions Signal and image processing (phase retrieval, Gabor systems) Approximation theory and function spaces Computational finance and mathematical modeling Recent Research Trends: His work explores theoretical guarantees for neural network performance, particularly in overcoming dimensionality challenges. Notable contributions include phase retrieval algorithms, analysis of DNN expressivity, and applications of deep learning in quantum chemistry and epidemiology modeling. Awards: ETH Latsis Prize (2014) Alexander von Humboldt Professorship (2019) Advising & Projects: Leads research initiatives such as the 'Data Science' hub at Vienna, and has coordinated projects on hybrid computational sciences and explainable AI models. Active in supervising interdisciplinary research teams across mathematics and computer science. Labs/Teams: Directs the Mathematical Data Science group at RICAM and oversees computational research collaborations with institutions like KAUST and ETH Zurich. Engages in applied projects like group testing strategies for SARS-CoV-2 and neural network-based electronic structure calculations.

• Mathematics
• Cryptography
• Computer Science
• +8 more

Ross Bowden is a Lecturer in Cryptography at the School of Computer Science, University of Bristol. His research focuses on mathematical foundations of cryptography, including endomorphism, hash functions, polynomial systems, and graph theory. He is affiliated with the Bristol Doctoral College. Research interests: His work intersects computer science and mathematics, particularly in cryptographic hash functions, polynomial equation systems, and supersingular isogeny graphs. His recent publications address open problems in these domains. Publications: Active in advancing cryptographic algorithms and mathematical structures, with contributions to supersingular isogeny graphs and polynomial-based cryptographic challenges. Contact: Email ross.bowden@bristol.ac.uk or rb15152@bristol.ac.uk .

Mher SafaryanView profile

Researcher

• Optimization theory and algorithms
• Machine learning
• Federated learning
• +7 more

Dr. Mher Safaryan is a postdoctoral researcher at the Institute of Science and Technology Austria (ISTA), affiliated with Prof. Dan Alistarh's research group since 2022. Previously, he held postdoctoral positions at King Abdullah University of Science and Technology (KAUST) from 2019-2022 and served as a research technician there from 2016-2019. He earned his Ph.D. in Mathematics from Yerevan State University in 2018 under Prof. Grigori Karagulyan's supervision. Education: Ph.D. in Mathematics (2018, YSU) Past Affiliations: KAUST (2016-2022), Neural Magic/Red Hat (industrial secondment) His research focuses on optimization theory and algorithms for machine learning , particularly in developing communication/computation/memory-efficient methods for large-scale training and federated learning. Key contributions include LDAdam (low-dimensional gradient statistics optimization), GradSkip (accelerated local gradient methods), and Unified Scaling Laws for compressed representations. He has published in top venues like NeurIPS, ICML, ICLR, TMLR, and The Journal of Geometric Analysis. Scientific Awards: Marie Skłodowska-Curie Fellowship (MSCA COFUND IST-BRIDGE) His work bridges machine learning optimization with mathematical foundations from his earlier research in real harmonic analysis. Current collaborations include Prof. Dan Alistarh (ISTA), Dr. Alexandre Marques (Neural Magic), and Prof. Peter Richtárik (KAUST).

Gabriel KronbergerView profile

Professor

• Symbolic Regression
• Genetic Programming
• Machine Learning
• +3 more

Gabriel Kronberger is a Professor at Hagenberg University of Applied Sciences, specializing in Symbolic Regression, Genetic Programming, and Machine Learning. His work bridges theoretical advancements with industrial applications in mechatronics and engineering systems. Active in evolutionary computation and symbolic regression since 2006 Lead researcher at the Josef Ressel Center for Symbolic Regression Developed techniques for alarm flood reduction in critical infrastructure His research focuses on Symbolic Regression , where he explores algorithmic enhancements like redundant parameter reduction and equality graph integration. He applies these methods to material science (e.g., tensile strength prediction) and automotive engineering (e.g., powertrain modeling). Recent publications demonstrate a trend toward interactive tools (rEGGression) and hybrid approaches combining genetic programming with machine learning systems (neural networks, random forests). All 158 publications emphasize practical implementations in industrial contexts. He has organized key conferences like Genetic and Evolutionary Computation Conference (2017-2020) and led 6 major research projects from 2013 to 2026, including EREMA Recycling 4.0 and McTronic educational initiatives.

Lukas SteinbergerView profile

Associate Professor

• Statistical Machine Learning
• Differential Privacy
• High-dimensional Data Analysis
• +3 more

Lukas Steinberger serves as Associate Professor for Statistical Machine Learning in the Department of Statistics and Operations Research at the University of Vienna's Faculty of Business, Economics and Statistics. He is also affiliated with the university's Data Science research network. Prior to his current position, he was a post-doctoral researcher with Angelika Rohde at the University of Freiburg and held a temporary Full Professorship in the Department of Mathematics at the Technical University of Munich. Steinberger's research program centers on statistical inference under differential privacy, high-dimensional data analysis, and uncertainty quantification in statistical learning. His work bridges theoretical statistics with practical machine learning applications, particularly focusing on how to perform valid statistical inference while preserving data privacy. His research has significant implications for fields requiring sensitive data analysis including healthcare, finance, and social sciences where privacy constraints are paramount. His publication record demonstrates consistent contributions to top-tier statistics journals including multiple papers in the Annals of Statistics. His recent work shows a clear trajectory toward developing theoretically sound methods for privacy-preserving data analysis that maintain statistical efficiency. The research themes across his publications reveal strong connections between differential privacy mechanisms, high-dimensional statistical theory, and practical machine learning applications. Scientific Awards: Award for dissertations in the field of theoretical statistics (2016) Steinberger maintains an active research agenda with numerous conference invitations and seminar presentations internationally, reflecting his standing in the statistical community. His current research project "Classification - Preprocessed and high-dimensional data" (2021-2025) indicates ongoing funding and research direction. He serves as Vice-Director of Studies for the Business, Economics and Statistics program and teaches courses including Statistics 2, Computational Statistics, and Classification, Clustering and Discrimination at the master's level. As part of the Data Science research network at University of Vienna, Steinberger collaborates with interdisciplinary teams working at the intersection of statistics, computer science, and domain-specific applications. His research group focuses on developing theoretically grounded approaches to statistical machine learning problems under privacy constraints.