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• dynamical systems
• symbolic dynamics
• subshifts
• +24 more

Ville Salo is an Associate Professor at the University of Turku's Department of Mathematics and Statistics. His research spans dynamical systems (especially symbolic dynamics, subshifts, and cellular automata), group theory (automorphism groups, geometric group theory), automata theory, and topology (including algebraic and general topology). He maintains a comprehensive list of open problems in symbolic dynamics, reflecting his deep engagement with foundational questions in these areas. Teaching Activity (2017–2025): Logiikka (Logic), spring 2025 & 2023 Lebesguen mitta ja integraali (Lebesgue measure and integral), fall 2023 Topologian perusteet (Basic topology), fall 2022 & 2019 Cellular automata, spring 2022 & 2018 Symbolic dynamics II, spring 2019 Johdatus automaattien teoriaan (Introduction to automata theory), spring 2018 & 2017 Algoritminen matematiikka (Algorithmic mathematics), spring 2017 He is fluent in Finnish and English, with proficiency in several other languages. Outside academia, he maintains math blogs, creates programming projects (including esoteric languages), and runs competitively.

• Modular development of specification languages
• Algorithms for models of computation
• Coalgebra
• +4 more

Alexandra Silva is a Professor of Computer Science at Cornell University with prior affiliations as a Royal Society Wolfson Fellow and Professor of Algebra, Semantics, and Computation at University College London . She leads a research group focusing on the modular development of specification languages and algorithms for models of computation, emphasizing coalgebra as a unifying mathematical framework. Research Interests Her work spans foundational and applied areas in theoretical computer science, including: Coalgebraic methods for formal verification Automata theory and learning algorithms Probabilistic programming and semantics Programming language design (e.g., NetKAT, Kleene Algebra with Tests) Concurrency theory and distributed systems Algebraic structures in computation Recent publications address network verification (StacKAT), symbolic automata learning, probabilistic regular expressions, and outcome logic for correctness/incorrectness reasoning. She is actively involved in organizing academic events like OPLSS 2025 and co-authoring foundational works in Formal Aspects of Computing and Theoretical Computer Science . Scientific Awards Distinguished Paper Award (ACM SIGPLAN POPL, 2020) Best Paper Award (RTA, 2015) She teaches courses on Kleene Algebra with Tests (KAT) and verification at summer schools like Marktoberdorf 2025 , and her research includes collaborations on probabilistic network verification (ProbNV) and stochastic system modeling.

Paolo G. GiarrussoView profile

Researcher

• Programming Language Theory
• Domain-Specific Languages
• Distributed Systems
• +5 more

Paolo G. Giarrusso is a researcher at the Institute for Programming Languages and Software Engineering within the Faculty of Informatics at the University of Tübingen . Previously, he was a Ph.D. student at the University of Marburg , where he defended his thesis Optimizing and Incrementalizing Collection Queries by AST Transformation in January 2018.

• Causal Machine Learning
• Mechanistic Machine Learning
• Dynamical Systems
• +6 more

Niki Kilbertus is a Professor in the Department of Informatics at the Technical University of Munich and a group leader at Helmholtz AI (Helmholtz Munich). They are also affiliated with MCML, the Konrad Zuse School relAI, and the Munich Unit of ELLIS. Since 2024, they have been a member of the Junge Akademie and received the Leopoldina Prize for Young Scientists. In 2025, they were awarded an ERC Starting Grant and achieved tenure at TUM. Professor Kilbertus's research focuses on causal machine learning, mechanistic ML, dynamical systems, and AI for science. Their work spans theoretical foundations of causal inference and practical applications across scientific domains. They have made significant contributions to causal effect estimation, causal discovery in stochastic processes, learning differential equations, and fair machine learning. Their research often bridges computer science with physics, biology, and climate science, demonstrating the interdisciplinary nature of their work. Professor Kilbertus has published extensively in top machine learning venues including NeurIPS, ICML, and ICLR, with numerous publications in 2024-2025. Their recent work shows a strong trend toward causal discovery in continuous-time systems, intervention modeling, and physics-informed machine learning applications. Scientific Awards: Leopoldina Prize for Young Scientists (2024) ERC Starting Grant (2025) Professor Kilbertus actively supervises multiple PhD students and collaborates with researchers across institutions including Max Planck Institutes and Helmholtz centers. They serve as an Action Editor for TMLR and regularly review for major ML conferences. The research group is well-funded through the ERC grant and institutional support from TUM and Helmholtz AI, enabling active recruitment of new PhD students and postdocs. Based at Technical University of Munich and Helmholtz AI, Professor Kilbertus's team works at the intersection of theoretical machine learning and scientific applications, with particular strengths in causal reasoning for complex dynamical systems.

Oleg DavydovView profile

Professor

• Meshless Finite Difference Method
• Finite Element Method
• Scattered Data Fitting
• +4 more

Professor Oleg Davydov holds a Professorship for Numerical Analysis at the Department of Mathematics, University of Giessen, Germany. His research focuses on developing advanced numerical methods with strong theoretical foundations and practical applications. He maintains an active research program with numerous recent publications and international collaborations. Position: Professor of Numerical Analysis Institution: University of Giessen, Department of Mathematics Contact: Heinrich-Buff-Ring 44, 35392 Giessen, HRZ Room 117 Email: oleg.davydov@math.uni-giessen.de Homepage: https://oleg-davydov.de/ Professor Davydov's research interests center around meshless numerical methods, approximation theory, and computational mathematics. His primary focus areas include: Meshless Finite Difference Method - Developing robust meshless techniques that avoid the need for structured grids Finite Element Method - Particularly Bernstein-Bézier finite elements and specialized approaches for complex geometries Scattered Data Fitting - Creating efficient algorithms for approximating data on irregular domains Approximation Theory - Investigating theoretical properties of splines, radial basis functions, and other approximation tools His research has resulted in several software packages including mFDlab (Meshless Finite Difference Method), BBFEM (Bernstein-Bézier Finite Elements), and TSFIT (Two-Stage Scattered Data Fitting), demonstrating the practical implementation of his theoretical work. Analysis of Professor Davydov's recent publications shows a consistent focus on improving meshless methods, particularly in stencil selection, error analysis, and applications to complex problems. His work spans both theoretical developments (like error bounds and optimal approximation orders) and practical implementations (for fluid dynamics, manifold learning, and interface problems). A notable trend is the increasing sophistication of adaptive techniques and the handling of challenging geometries. Professor Davydov has supervised several doctoral students to completion, including: Gaelle Andriamaro Fabien Rabarison Abid Saeed Wee Ping Yeo His research group appears to maintain active collaborations with institutions worldwide, as evidenced by his extensive co-authorship network. The group focuses on developing both theoretical foundations and practical implementations of numerical methods, with particular attention to problems involving irregular domains, singularities, and complex geometries. Students in his group would gain experience in both theoretical analysis and software development for numerical methods.

Stefan PorschenView profile

Professor

• Discrete Mathematics
• Mathematical Logic
• Algebra
• +3 more

Prof. Dr. Stefan Porschen is a Professor at HTW Berlin's Department of Computer Science, Communication and Economics. His primary academic roles include: University Lecturer for Quantitative Finance & Data Science (Master's) Examination Board Member for Business Mathematics (Bachelor's) Audit Committee Member for Financial Services - Risk Management His research explores foundational mathematical domains with emphasis on: Discrete structures and algebraic frameworks Computational logic and complexity theory Algorithmic optimization techniques This work bridges theoretical mathematics with computational applications in computer science and economics. Professor Porschen holds multiple institutional responsibilities including committee memberships for academic quality assurance and curriculum development.

Jürgen DassowView profile

Professor

• Formal languages
• Regulated rewriting
• Lindenmayer systems
• +7 more

Jürgen Dassow is a Professor for Theoretical Computer Science at the Faculty of Computer Science, Otto-von-Guericke-University of Magdeburg, Germany. He has held this position since 1992 and previously served as Rector of the university from 1993 to 1996. His academic career spans over five decades, beginning with studies in mathematics at the University of Rostock in the 1960s. Professor Dassow's research focuses on formal languages, regulated rewriting, Lindenmayer systems, grammar systems, syntactical complexity, and biocomputing . His work has significantly contributed to theoretical computer science, particularly in the mathematical foundations of language theory and automata. He has maintained a prolific publication record with over 200 scientific papers and 4 monographs, including the influential 1989 work 'Regulated Rewriting in Formal Language Theory' co-authored with Gheorghe Păun. His recent research (2020-2023) continues to explore operational complexity, closure-involution operations, contextual grammars, and networks of evolutionary processors. These publications demonstrate his sustained engagement with cutting-edge theoretical problems in computer science. Professor Dassow has also been instrumental in organizing the international Descriptional Complexity of Formal Systems (DCFS) workshop series and has edited numerous proceedings volumes. Professor Dassow has been actively involved in the academic community through approximately 100 lectures at conferences and universities across 18 countries. His work bridges theoretical computer science with biological computing models, reflecting the interdisciplinary nature of modern theoretical research. His research group at the Otto-von-Guericke-University continues to advance knowledge in formal language theory, with recent publications addressing contemporary challenges in descriptional complexity and bio-inspired computational models. Professor Dassow's career exemplifies long-term dedication to advancing the theoretical foundations of computer science while maintaining relevance to emerging computational paradigms.

Günter BarczikView profile

Professor

• Computational Design
• Digital Representation Theory
• Algebraic Geometry in Architecture
• +7 more

Professor Günter Barczik serves as a Full Professor at the University of Applied Sciences Erfurt within the Faculty of Architecture and Urban Planning, holding the Professorship for Design, Digital Representation Theory and Computational Design. He has been in this position since September 2011 and previously served as an Assistant Professor at Brandenburg University of Technology Cottbus-Senftenberg from October 2004 to October 2010. His office is located at Schlüterstraße 1, Room 414, and he also serves as the Equal Opportunities Officer for the institution. Barczik's research interests center on computational approaches to architectural design, with particular focus on digital representation theory, immersive technologies, and the application of algebraic geometry in architectural contexts. His work explores how mathematical concepts can expand architectural vocabulary and how new technologies can transform traditional design processes. He investigates the intersection of computational tools with creative thinking, sustainable architecture practices, and visual communication methods. Analysis of Barczik's publication history reveals a consistent trajectory exploring the integration of computational methods with architectural design. His work demonstrates a progression from fundamental explorations of algebraic surfaces in architecture (2009-2012) toward more applied investigations of immersive technologies, team dynamics in design processes, and the evolving relationship between designers and digital tools. A recurring theme throughout his research is the examination of how computational techniques can enhance traditional design thinking while addressing contemporary challenges in urban planning and architectural representation. With 22 publications accumulating 5,886 reads and 65 citations, Barczik has established himself as a significant contributor to computational design discourse. His work connects architecture with mathematics, computer science, and interdisciplinary team dynamics, reflecting a holistic approach to design education and practice. His research demonstrates consistent engagement with both theoretical frameworks and practical applications of computational design methods in architectural education and professional practice.

Annegret WengView profile

Professor

• Financial Mathematics
• Insurance Mathematics
• Number Theory
• +4 more

Annegret Weng serves as Professor and Dean of the School of Applied Mathematics and AI at Stuttgart University of Applied Sciences since 2009, with board membership in the German Society for Financial and Insurance Mathematics (DGVFM) since 2023. Her career bridges industry expertise from Allianz and SV Sparkassenversicherung with academic leadership in quantitative disciplines. Diploma in Mathematics, University of Frankfurt PhD, University of Essen (1999) Postdoctoral Research, Universities of Essen and Mainz (1999-2001) Her research integrates pure mathematics with financial applications, focusing on number theory (De Bruijn sequences, cyclic matrices) and actuarial science (Solvency II compliance, disability risk modeling). Recent work explores AI's role in educational assessment and regulatory frameworks for insurance algorithms, demonstrating consistent innovation across theoretical and applied domains. Publication trends (2015-2024) reveal dual expertise: advancing actuarial methodologies through parameter uncertainty modeling while pioneering AI-driven educational tools. Her work consistently connects recreational mathematics (e.g., magical properties of linear recursions) with industry-critical applications in insurance risk assessment. As head of the Laboratory for Quantitative Methods of Actuarial Mathematics , she leads research in computational risk modeling. Her industry engagement includes the German Actuarial Association's Big Data in Life Insurance Working Group (since 2024) and DGVFM's School Working Group (since 2019), driving collaboration between academia and insurance regulators.

Miriam PrimbsView profile

Professor

• Applied Mathematics
• Numerical Methods
• Mathematical Simulation
• +4 more

Professor Miriam Primbs is a faculty member at Hochschule Ruhr West (HRW) in the Institute of Natural Sciences, where she teaches Mathematics and Simulation. She has been a professor at HRW since 2011 and coordinates the HRW research focus MARTA since 2022. Professor Primbs studied Mathematics and Physics (Diplom and 1. Staatsexamen) and earned her PhD in Mathematics at the University of Duisburg-Essen. Her career includes a research stay at IMATI CNR in Italy, teaching on the school ship Thor Heyerdahl, and working in Research and Development at Siemens Energy Mülheim before joining HRW. This blend of academic and industry experience informs her teaching and research approach. Her research focuses on applying mathematical and numerical methods to technical problems across engineering disciplines, from mechanics to electrical engineering. As coordinator of the MARTA research focus, she leads various projects including those funded by BMBF and collaborations with industry partners like Siemens Energy. Her scholarly work spans wavelet analysis, numerical methods, signal processing, and power systems analysis. Professor Primbs teaches Engineering Mathematics I and II, Mathematics for E-Commerce, MATLAB, Scientific Simulation, and LaTeX Basics. She employs a flipped classroom approach for foundational courses, providing students with tailored materials and prompt feedback through weekly exercises. In higher semesters, she emphasizes project-based learning and specialized topics. Currently supervising doctoral student Martin Wachs in cooperation with University of Duisburg-Essen Member of the Senate Committee for Research and Transfer at HRW Encourages industry collaboration for practical research applications Professor Primbs maintains strong connections between academic research and industrial applications, leveraging her Siemens Energy experience to bridge theory and practice in mathematical engineering.

Simon ApersView profile

Researcher

• Quantum Algorithms
• Theoretical Computer Science
• Random Walks
• +4 more

Simon Apers is a CNRS researcher at IRIF (Institut de Recherche en Informatique Fondamentale) at Université Paris Cité. He focuses on quantum algorithms with broader interests in theoretical computer science, including random walks, graph theory, and combinatorial optimization. His primary research interests span quantum computing, theoretical computer science, and algorithms. Apers has made significant contributions to quantum walks, quantum property testing, graph algorithms, and computational complexity. His work often bridges quantum information with classical theoretical computer science, exploring how quantum techniques can improve classical algorithms and solve problems more efficiently. He has developed quantum speedups for various computational tasks including sampling, optimization, and graph problems. Simon Apers serves as a program committee member for prestigious conferences including TQC'25, QIP'25, TQC'23, SODA'23, and ESA '21. He also serves as an editor for the journal Quantum. His teaching activities include courses at Sorbonne University (2022-present) on Advanced Quantum Algorithms, MPRI (2021-present) on Quantum Algorithms and Complexity, and Bad Honnef (2022). His publication record shows a strong trend toward quantum algorithms for graph problems, quantum walks, and connections between quantum computing and classical theoretical computer science. He frequently collaborates with researchers from various institutions across Europe and has published in top venues including FOCS, STACS, ESA, PRL, and JMLR. Simon Apers actively mentors students and has open positions for PhD students and postdocs in quantum algorithms at IRIF. His research group is involved in cutting-edge work at the intersection of quantum computing and theoretical computer science, with significant contributions to quantum walks, property testing, and computational complexity.