Boolean Satisfiability Professors and Supervisors in Austria

Johannes Peter Wallnerمشاهده پروفایل

دانشیار

Graz University of TechnologyAustria

Johannes Peter Wallner is an Associate Professor at Graz University of Technology (TU Graz), working in the Institute of Software Engineering and Artificial Intelligence within the Faculty of Computer Science and Biomedical Engineering. He leads the Knowledge Representation and Reasoning (KRR) research group and has previously been a researcher at TU Wien's DBAI group and the Constraint Reasoning and Optimization group at the University of Helsinki. Dr. Wallner's research focuses on knowledge representation and reasoning, artificial intelligence, argumentation, abduction, belief change, inconsistency handling and measurement, computational social choice, computational complexity, Boolean satisfiability, and answer set programming. His work bridges theoretical foundations with practical applications, particularly in developing computational models for argumentation systems. He has made significant contributions to structured argumentation frameworks, including assumption-based argumentation and ASPIC+. His recent publications demonstrate a strong trend toward advancing algorithmic approaches to probabilistic argumentation, abstraction techniques in argumentation systems, and applications of argumentation in domains like healthcare. He has been particularly active in exploring the computational complexity of various argumentation semantics and developing efficient algorithms for reasoning tasks. Dr. Wallner has received multiple prestigious awards including being selected for the IJCAI 2024 Early Career Track (only 12 researchers globally selected), being named a Top Scholar by ScholarGPS in 2024 (top 0.5% worldwide in AI), and receiving the AI 2000 Most Influential Scholar Honorable Mention in Knowledge Engineering in 2021 and 2022. As Principal Investigator, Dr. Wallner has secured significant research funding from the Austrian Science Fund (FWF), including two major projects: "A Novel Computational Workflow for Argumentation in AI" (grant P 35632, 358,848 €) and "Extending Belief Change to Advance Dynamics in Argumentation" (grant P30168-N31, 353,438 €). He is highly active in the academic community, serving on program committees for major AI conferences including AAAI, IJCAI, KR, and ECAI, and was a member of the Program Committee Board of IJCAI (2022-2024). He leads the Knowledge Representation and Reasoning (KRR) research group at TU Graz, which develops both theoretical foundations and practical implementations for computational argumentation systems. The group has contributed to several software systems including CEGARTIX (a SAT-based argumentation system), Vispartix (visualization of argumentation frameworks), ADFsys (an ASP-based argumentation system for abstract dialectical frameworks), and others that implement various argumentation frameworks.

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Krishnendu Chatterjeeمشاهده پروفایل

استاد

Institute of Science and Technology AustriaAustria

Krishnendu Chatterjee is a Professor at the Institute of Science and Technology Austria (IST Austria) , Department of Computer Science. His research spans formal verification, probabilistic systems, game theory, and evolutionary dynamics, with over 300 peer-reviewed publications in top venues such as DISC, AAAI, LICS, PNAS, Nature , and Journal of the ACM . His research focuses on developing theoretical foundations and practical algorithms for analyzing complex systems, including Markov decision processes, stochastic games, probabilistic programs, and evolutionary models. He has made significant contributions to topics such as reachability analysis, termination of probabilistic programs, synthesis of controllers, and evolutionary game dynamics. Chatterjee's work is highly interdisciplinary, bridging computer science, mathematics, and biology. He has collaborated extensively with leading researchers worldwide and has been involved in editorial roles and program committees for major conferences in formal methods and theoretical computer science.

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Adrian Rebola Pardoمشاهده پروفایل

پژوهشگر

Vienna University of TechnologyAustria

Adrian Rebola Pardo is a Researcher at the Institute of Computer Engineering (E192-04) within the Faculty of Informatics at TU Wien. His work focuses on formal methods, automated reasoning, and SAT solving, with an emphasis on proof theory and verification. He holds a PhD in Computer Science from TU Wien (2021), where he developed interference-based proof systems for SAT solvers. **Education**: PhD in Computer Science, TU Wien (2021) **Research Interests**: His research centers on advancing SAT solving techniques, proof complexity, and formal verification. Key areas include quantified Boolean formulas, DRAT proof systems, and efficient proof generation. His work intersects computational logic and theoretical computer science, with applications in automated theorem proving and verification frameworks. **Grants & Projects**: LCS Project (2017–2025): Interpolants and Interference BITVECTOR Project (2016–2020): Exploring SAT refutation verification techniques **Advising**: Supervised Jakob Altmanninger's 2019 diploma thesis on SAT proof generation and DRAT checking. **Labs/Teams**: Member of the Formal Methods in Systems Engineering group at TU Wien.

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Kazuo Iwamaمشاهده پروفایل

استاد

Graz University of TechnologyAustria

Kazuo Iwama is a Professor at the School of Informatics, Kyoto University, where he has been employed since 1997. He previously served as a Professor at Kyushu University's Department of Information Science (1992–1997) and held various academic positions, including Associate Professor and Visiting Associate Professor, at institutions such as Kyoto Sangyo University and UC Berkeley's Computer Science Division. Education Ph.D., Electrical Engineering, Kyoto University (1980) M.E., Electrical Engineering, Kyoto University (1975) B.E., Electrical Engineering, Kyoto University (1980) His research spans theoretical computer science, with a focus on algorithms, computational complexity, and quantum computing. Key areas include stable matchings, approximation algorithms, Boolean satisfiability, and quantum network coding. Scientific Awards 2008 Honorary Doctorate from University of Latvia 2005 Member, Science Council of Japan

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Florentina Voborilمشاهده پروفایل

پژوهشگر ارشد

Vienna University of TechnologyAustria

Florentina Voboril is a Research Fellow in the Algorithms and Complexity group at Technische Universität Wien. Her research explores how Large Language Models can solve Constraint Programming instances efficiently, with additional focus on SAT-solving and algorithm design. She investigates applications of AI in combinatorial optimization problems and develops innovative approaches to algorithmic challenges. Voboril has developed methods for SAT-based local improvement in string problems and generates streamlining constraints using LLMs. Her work bridges theoretical computer science with practical applications in computational biology and AI-assisted programming.

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Leslie Ann Goldbergمشاهده پروفایل

استاد

Graz University of TechnologyAustria

Leslie Ann Goldberg is Professor of Computer Science at the University of Oxford and Senior Research Fellow at St Edmund Hall. She has held academic positions at the University of Liverpool (2006-2013) and the University of Warwick (1995-2006), and previously worked at Sandia National Labs (1992-1995). Currently serving as Head of Department (sabbatical planned for 2025-26), she leads research in theoretical computer science with focus on algorithms and complexity theory. Her research interests span algorithms for approximately counting and randomly sampling combinatorial structures , analysis of Markov-chain based sampling algorithms , complexity of counting , and stochastic processes in computer science . She specializes in randomized algorithms, particularly their mathematical analysis regarding runtime and accuracy. Current projects include contentions resolution via backoff protocols, random sampling and approximate counting algorithms, counting complexity theory, and analyzing processes on networks like the Moran process. Professor Goldberg has published over 100 papers in top theoretical computer science venues, with recent work focusing on spin systems, graph homomorphisms, subgraph counting, and network processes. Her publications show consistent output in premier venues including ICALP (where she has won three Best Paper Prizes), SODA, STOC, and RANDOM. ERC Advanced Grant 'Mapping the Complexity of Counting' (2014-2019) Best Paper Prize at ICALP 2012, 2010, and 2006 UK Distinguished Dissertations in Computer Science (1991-1992) She actively supervises PhD students and collaborates with researchers including Andreas Galanis, Marc Roth, John Lapinskas, and Paulina Smolarova. Her work bridges theoretical computer science with statistical physics, combinatorics, and network theory, with applications to database systems, evolutionary dynamics, and communication protocols.

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Martina Seidlمشاهده پروفایل

استاد

Johannes Kepler University LinzAustria

Martina Seidl is a Professor at the Johannes Kepler University Linz , serving as Head of the Institute for Symbolic Artificial Intelligence . She leads research initiatives in formal verification, quantified Boolean formulas, and symbolic AI. Key Projects : Industrial problem solving using symbolic AI (2025–2026), Cluster of Excellence "Bilateral AI" (2024–2029), LOGTECHEDU (2018–2020) Leadership Roles : PhD Committee member at TU Wien, Associate Editor for the Artificial Intelligence Journal Her research focuses on automated reasoning , QBF solving , and formal methods in computer science. She has developed tools like PyQBF and Booleguru for propositional logic and constraint solving. Recent publications analyze semantic feature model differences, solution counting for QBF families, and tree-based model counters. She contributes to educational programs through lectures on formal models, SAT solving, and neurosymbolic AI. Scientific activities include organizing workshops like "Alles Logisch?" and participating in program committees. Current funded projects emphasize interdisciplinary AI applications and logic-based educational technologies.

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Szemerédi Endreمشاهده پروفایل

استاد

Graz University of TechnologyAustria

Endre Szemerédi is a Hungarian mathematician and computer scientist, currently serving as a Permanent Research Fellow at the Alfréd Rényi Institute of Mathematics (Hungarian Academy of Sciences) and holding the New Jersey Professor of Computer Science position at Rutgers University since 1986. His career spans prestigious institutions including Stanford, Caltech, and the Institute for Advanced Study. Expertise: Discrete mathematics, combinatorics, theoretical computer science, and number theory. Education: M.Sc. (1965, Eötvös Loránd University), Ph.D. (1970, Moscow State University under Israel Gelfand). Research legacy encompasses foundational contributions to combinatorics, graph theory, and computational complexity. His work on Hamiltonian cycles in random graphs , parallel sorting networks , and the Blow-up Lemma revolutionized extremal combinatorics and algorithm design. Scientific honors include: 2012 Abel Prize and Széchenyi Prize 2008 Rolf Schock Prize and Steele Prize 1982 Member of Hungarian Academy of Sciences , 2010 Member of US National Academy of Sciences 1975 Pólya Prize , 1973 Rényi Prize , multiple Grünwald Prizes .

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Tomáš Peitlمشاهده پروفایل

عضو هیئت علمی

Vienna University of TechnologyAustria

Tomáš Peitl is a University Assistant at the Vienna University of Technology's Institute of Logic and Computation. His research focuses on quantified Boolean formulas (QBF), proof complexity, SAT solving, and dependency schemes. He teaches courses on Algorithms and Data Structures, Algorithmics, and Structural Decompositions. Dr. Peitl has developed specialized tools including the QBF solver Qute which implements dependency learning and reflexive resolution-path dependency schemes. His current research examines decision heuristics in QCDCL and hardness characterizations of QBF resolution systems. Recent publications explore symmetry breaking techniques in quantified graph search, QCDCL decision strategies, and the generation of hard SAT instances. He maintains collaborations through conferences like SAT and IJCAI, and contributes to theoretical foundations of automated reasoning.

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Uwe Eglyمشاهده پروفایل

دانشیار

Vienna University of TechnologyAustria

Uwe Egly is an Associate Professor at the Department of Knowledge-Based Systems, Faculty of Informatics, Technische Universität Wien (TU Wien). His research focuses on automated reasoning, proof theory, knowledge representation, and computational logic, with a strong emphasis on quantified Boolean formulas (QBFs), argumentation frameworks, and applications of AI in engineering. He leads projects funded by the Austrian Science Fund (FWF) and the Vienna Science and Technology Fund (WWTF), including the Boolean project (2011–2019) and FAME (2011–2014). Egly is known for developing QBF solvers like DepQBF and contributing to SAT-solving techniques. He teaches courses such as Abstract Argumentation , Formal Methods in Computer Science , and Quantum Computing . His research interests span proof complexity, satisfiability checking, and AI-driven algorithms for path planning. He has advised numerous students on theses involving quantum algorithms, QBF solver optimizations, and argumentation frameworks. Egly’s work bridges theoretical computer science with practical applications, including contributions to deformation monitoring systems and circuit synthesis using SAT-based methods. He is involved in international workshops and conferences, such as SAT, FMCAD, and Dagstuhl Seminars, and has edited proceedings for events like SAT 2014 . His collaborations include projects on scenario-based testing of UML diagrams and semantics-aware model versioning. Egly’s interdisciplinary approach integrates logic, artificial intelligence, and computational methods to solve complex theoretical and applied problems.

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Friedrich Slivovskyمشاهده پروفایل

پژوهشگر

Vienna University of TechnologyAustria

Friedrich Slivovsky is a researcher at the Institute of Logic and Computation within the Faculty of Informatics at Technische Universität Wien (Vienna University of Technology). His work focuses on theoretical and practical aspects of computational logic, with particular expertise in Quantified Boolean Formulas (QBFs), Propositional Model Counting (#SAT), and Knowledge Compilation. His research interests span the theoretical foundations and practical applications of computational logic. Slivovsky investigates the complexity of logical reasoning problems, develops efficient algorithms for solving them, and creates practical tools that implement these theoretical advances. His work bridges the gap between theoretical computer science and practical applications in areas like hardware verification, artificial intelligence, and electronic design automation. Analysis of his publication trends reveals a consistent focus on QBF solving techniques, with increasing emphasis on circuit minimization, proof complexity, and practical solver engineering. His recent work (2023-2024) shows a strong focus on circuit minimization techniques, combining QBF and SAT approaches to solve complex optimization problems in hardware design. Earlier work (2019-2021) emphasized dependency schemes, certification methods, and theoretical foundations of QBF solving. Slivovsky leads several significant software projects that have become important tools in the computational logic community: Qute : A dependency learning QBF solver with GitHub repository showing active development (latest commit December 2024) Unique : A preprocessor for (D)QBF that computes unique Skolem and Herbrand functions Pedant : A certifying DQBF solver These projects demonstrate his commitment to translating theoretical advances into practical tools that benefit the broader research community.

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Alexey Ignatievمشاهده پروفایل

دانشیار

Vienna University of TechnologyAustria

Alexey Ignatiev is an Associate Professor in the Optimisation research group at Monash University's Faculty of Information Technology. Previously, he was a postdoctoral researcher and researcher at the University of Lisbon's Faculty of Sciences, focusing on SAT/SMT-based decision procedures. He holds a Ph.D. from the Matrosov Institute for System Dynamics and Control Theory (Russian Academy of Sciences), where his thesis explored parallel CDCL-BDD integration. His research emphasizes formal methods in AI, including explainable AI (XAI), SAT-based reasoning, and optimization for applications like software upgradability, model-based diagnosis, and fault localization. His work spans over 100 publications, with notable contributions to MaxSAT solving (RC2 solver), neuro-symbolic frameworks (NEUSIS), and rigorous explanations for machine learning models. He has collaborated extensively with institutions like the University of Lisbon and Monash University, contributing to advancements in formal verification and interpretable machine learning.

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