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.
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.
Sebastian Ordyniak is an Associate Professor in the Department of Algorithms and Complexity at TU Wien. His research focuses on parameterized complexity, algorithms, computational complexity, and applications in artificial intelligence and graph theory. He holds a PhD and the prestigious START Prize (2014–2022), a renowned Austrian award for outstanding researchers. Key projects include the ERC-funded 'Parameterized Complexity of Local Search' (2010–2014) and ongoing initiatives like 'Parameterized Analysis in Artificial Intelligence' (2021–2026). His work bridges theoretical foundations with practical applications, such as algorithmic fairness, machine learning interpretability, and graph drawing. Research highlights include contributions to SAT solving, backdoor analysis, and clustering algorithms. He has advised at least one student, Hossein Maleki, on practical algorithms for deletion to small components. His interdisciplinary approach integrates logic, computational geometry, and multi-agent systems.
Adrian Rebola Pardo is a researcher at the Institute for Symbolic Artificial Intelligence at Johannes Kepler University Linz (Austria). His work focuses on computational logic, formal verification, and SAT solving, with recent contributions to quantified Boolean formulas and proof systems optimization. He is actively involved in the Cluster of Excellence 'Bilateral Artificial Intelligence' (2024-2029) and teaches courses like 'Computational Logic for AI' and 'Formal Models in AI'. Research Interests : Symbolic AI, computational logic, formal verification, SAT solving, quantified Boolean formulas, and algorithm optimization Recent Article Trends : Publications focus on quantifier shifting techniques, proof complexity reduction, and DRAT proof system improvements Academic Activities : Speaker at multiple international conferences including IJCAR 2024 and SAT 2023
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.
Martin Kronegger serves as a Senior Lecturer in the Department of Automation Systems at Vienna University of Technology (TU Wien), where he teaches courses including Fundamentals of Digital Systems, Computer Engineering Projects, and Scientific Projects in Computer Science for the 2025W and 2026S semesters. His academic profile demonstrates active engagement in both teaching and research within the Faculty of Electrical Engineering and Information Technology. Dr. Kronegger's research centers on theoretical foundations of artificial intelligence with emphasis on parameterized complexity, automated planning, and answer set programming. His work bridges theoretical computer science with practical applications in knowledge representation and reasoning, as evidenced by publications in premier venues like Artificial Intelligence journal and AAAI conferences. Key research themes include: Backdoor techniques for planning problems Parameterized complexity analysis of AI problems QBF solving for conformant planning SAT-based verification methods for software models His publication landscape reveals consistent contributions to parameterized complexity theory applied to planning and logic programming from 2011-2019, with significant work on multiparametric analysis of answer set programming and SAT-based approaches to planning problems. The research trajectory shows deepening theoretical contributions while maintaining connections to practical AI applications. Dr. Kronegger has supervised at least one diploma thesis on planning solvers and has participated in major research projects including FAIR (2013–2018), START (2014–2022), and X-TRACT (2014–2018). His project work demonstrates sustained collaboration with research groups at TU Wien focused on computational logic and automated reasoning. Based in the Automation Systems research group (E191-03) at TU Wien's Treitlstraße campus, he maintains active research collaborations through projects like the START program and contributes to the international answer set programming community as evidenced by involvement in the Fourth Answer Set Programming Competition.
Tomáš Peitl is a researcher and university assistant at the Institute of Logic and Computation, Vienna University of Technology, within the Algorithms and Complexity group. His roles include teaching courses like Algorithms and Data Structures and Structural Decompositions and Algorithms. He holds a PhD in Computer Science from TU Wien (2019) and has held postdoctoral positions at Friedrich Schiller University (Jena, Germany) and TU Wien, funded by FWF grants. His research focuses on Quantified Boolean Formulas (QBF), Dependency QBF, SAT solving, proof complexity, and algorithm development for formal verification. He has contributed to software tools like Qute (a QBF solver) and SAT Modulo Symmetries. Education: PhD in Computer Science (2015–2019, TU Wien), MSc in Mathematics (2013–2015, Comenius University, Bratislava), BSc in Mathematics (2010–2013, Comenius University). Research interests span theoretical aspects (e.g., proof complexity, computational complexity) and practical applications (e.g., SAT/QBF solver development). His work bridges algorithmic theory and real-world problem-solving. Recent articles explore dependency schemes in QBF, resolution paths, and SAT-based approaches to graph theory problems. Notable awards include the Best Paper Award at CP 2020 and the FWF Erwin Schrödinger Fellowship. Peitl collaborates on projects like the Austrian Science Fund (FWF) grant on DQBF theory and has developed tools for shortest proof calculation (short.py) and symmetry-handling in SAT solving. His contributions highlight advancements in automated reasoning and computational logic.
Dr. Leroy Chew is a Research Fellow at the Institute of Logic and Computation at Technische Universität Wien. He specializes in theoretical computer science with focus areas in proof complexity, quantified Boolean formulas (QBF), and SAT solving. His educational background includes a PhD from the University of Leeds and postdoctoral research at Carnegie Mellon University. Dr. Chew's research explores the boundaries of computational complexity and formal verification systems. His current projects include developing novel proof systems for quantified formulas and expansion-based approaches for constraint satisfaction problems. He leads research funded by the ESPRIT Grant on QBF Proofs and Certificates. His publication record demonstrates consistent contributions to formal verification and computational logic, with recent advances in strategy extraction techniques and dual proof systems. He maintains academic collaborations across Europe and the United States, and has served on program committees for major conferences including SAT and QBF Workshops. Dr. Chew has received the EPSRC Postdoctoral Prize Research Fellowship and continues to develop computational tools for the research community, including proof generators and strategy extraction software.
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.
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.
Leroy Nicholas Chew is a PostDoc Researcher and FWF Projektassistent at the Vienna University of Technology (TU Wien). He is affiliated with the Department of Algorithms and Complexity within the Faculty of Informatics. His roles include contributing to research projects such as QBFPC (2022–2025), Overcoming Intractability in the Knowledge Compilation Map, and REVEAL-AI (2020–2024). These projects reflect his focus on advancing theoretical computer science and automated reasoning methodologies. While specific educational details are not explicitly provided in the text, Leroy Nicholas Chew holds a PhD, as indicated by his role listing. His current position suggests a strong background in computer science and theoretical foundations, consistent with his research activities. His research interests span several key areas in theoretical computer science, including proof complexity, quantified Boolean formulas (QBF), automated reasoning, and knowledge compilation. He explores the hardness of computational problems in logical frameworks, such as analyzing resolution and CDCL proof systems, developing optimal dual proof systems for answer set programming (ASP), and investigating model counting techniques. His work often bridges foundational theory with practical applications in formal verification and algorithm design. Recent publications (2024) highlight advancements in circuits and proofs, model counting, and ASP-QRAT proof systems. Earlier work (2016–2022) addressed QBF resolution calculi, dependency schemes, and certification challenges. These trends underscore his specialization in formal methods and computational logic. No scientific awards are explicitly mentioned in the provided text. In addition to his research, Chew is involved in multiple funded projects. These include the FWF-supported QBFPC (2022–2025), which examines QBF proofs and certificates, and the REVEAL-AI project (2020–2024), focusing on overcoming intractability in knowledge compilation. While specific grant details beyond project funding are not mentioned, his participation underscores his role in collaborative, grant-funded research initiatives. No formal advisees are listed. Chew is part of the Algorithms and Complexity department at TU Wien, collaborating on projects that emphasize proof systems, formal verification, and algorithmic foundations. His work integrates theoretical insights with practical computational methods.
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.
Martina Seidl is a researcher and principle investigator at TU Wien's Institut für Softwaretechnik und Interaktive Systeme (E188). Her main affiliation is within the Faculty of Informatics. She leads the FAME Project (Formalizing and Managing Evolution in Model-Driven Engineering), focusing on advancing formal methods in software engineering. Her research interests include formal verification techniques, SAT/QBF solving algorithms, model-driven engineering, and automated reasoning. She has contributed to advancements in quantified Boolean formula (QBF) solving, parallel computing methodologies for logical problems, and formal methods in software model analysis. Her work spans theoretical computer science and practical applications in automated theorem proving and model checking. Notable contributions include expansion-based QBF solving approaches, parallel solving frameworks, and feature-based classifications of formal verification techniques. Seidl co-organized the QBF Gallery initiative, which curates benchmark suites for quantified Boolean formula competitions. She has also published extensively on clause redundancy optimization, blocked clause analysis, and the integration of formal methods in educational contexts like UML@Classroom.
Bernhard Aichernig serves as a University Professor at the Institute of Formal Models and Verification at Johannes Kepler University Linz. His academic career focuses on bridging theoretical computer science with practical software verification techniques. He actively contributes to the international research community through publications, program committees, and doctoral examinations. Professor Aichernig's research primarily centers on formal methods and model verification, with significant contributions to automata learning and software testing methodologies. His work explores the intersection of theoretical computer science and practical verification techniques, particularly in state-merging approaches for passive learning systems. His research has direct applications in improving software reliability through formal testing frameworks. His recent publication trends indicate a strong focus on advancing automata learning techniques, particularly extending the AALpy framework with passive learning capabilities. This work represents the cutting edge of model inference and formal verification, addressing challenges in state-merging algorithms for complex software systems. His research bridges theoretical foundations with practical testing applications. Professor Aichernig serves as an active member of the academic community through various roles including doctoral examination committees and program committees for major conferences like the NASA Formal Methods Symposium. He has examined PhD theses on advanced reasoning techniques for quantified Boolean formulas, learning Mealy machines with local timers, and deep integration of SAT solving with model checking. He currently participates in the Cluster of Excellence 'Bilateral Artificial Intelligence' project as a Principal Investigator, working alongside prominent researchers in the AI field. This active research project, funded by the Austrian Science Fund (FWF), runs from October 2024 through September 2029 and represents a significant collaborative effort in artificial intelligence research at JKU Linz.
