Przemysław Wałęga is a Senior Lecturer in the Centre for Fundamental Computer Science at Queen Mary University of London and a Researcher in the Department of Computer Science at the University of Oxford. His research focuses on Artificial Intelligence , particularly Knowledge Representation and Reasoning and Temporal Reasoning . Education : PhD in Logic (University of Warsaw), BEng and MS in Mechatronics (Warsaw University of Technology), BS in Philosophy (University of Warsaw). Research Highlights : Development of the temporal formalism DatalogMTL and its implementation in the Metric Temporal Reasoner MeTeoR at Oxford; work on interval temporal logics, computational complexity, and expressive power; application of Answer Set Programming for semantic video analysis, qualitative reasoning in robotics, and AI in game environments like Angry Birds. Roles : Senior Lecturer at Queen Mary (UK system), Senior Researcher at Oxford. Contact : przemyslaw.walega@cs.ox.ac.uk
Prof. Martin Gebser is a University Professor and Deputy Director at the Institute for Artificial Intelligence and Cybersecurity, University of Klagenfurt. His work bridges theoretical advancements in Answer Set Programming (ASP) with practical applications in industrial scheduling, semiconductor manufacturing, and explainable AI systems. Institute for Artificial Intelligence and Cybersecurity, University of Klagenfurt His research focuses on Answer Set Programming and its extensions for complex scheduling problems, particularly in semiconductor production. Key areas include: Multi-shot ASP solving for job-shop decomposition Hybrid AI systems integrating reinforcement learning and logic programming Explainable AI for battery health monitoring and semiconductor dispatching Recent publications emphasize temporal planning, constraint learning, and real-world data integration. He has developed customizable simulators and optimization frameworks for industrial applications.
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.
Zeynep Gözen Saribatur Yaman is a PostDoc Researcher at the Department of Databases and Artificial Intelligence, Technische Universität Wien (TU Wien). Her role is supported by the Austrian Science Fund (FWF) as a Projektassistentin (Dr.in techn.). She is affiliated with the DBAI group and contributes to multiple research projects including AURA (2022–2026), DynaCon (2017–2020), AI4EU (2019–2021), and HumanE-AI-Net (2020–2024). Her primary affiliation is with TU Wien’s Faculty of Informatics, where she focuses on advancing explainable AI through abstraction techniques in logic-based systems. Zeynep holds a Doctorate in Technical Sciences (Dr.techn.) from TU Wien (2019), where her dissertation addressed Abstraction for reasoning about agent behavior with answer set programming . She also holds an MSc in a relevant field, though its specifics are not explicitly detailed in the text. Her research spans multiple funded initiatives, emphasizing both theoretical contributions and applied work in robotics and agent systems. Her research interests revolve around abstraction mechanisms in Answer Set Programming (ASP), argumentation frameworks , and their applications to explainable AI , robotics planning , and agent behavior modeling . She explores techniques to reduce complexity in logic-based systems while preserving critical reasoning aspects, with a focus on making AI systems more transparent and understandable. Zeynep has contributed to several projects aiming to enhance AI reasoning through abstraction. Her work bridges formal methods and practical AI challenges, such as reasoning about dynamic environments and multi-agent systems. She actively participates in international conferences and workshops, including KR, AAMAS, ICAPS, and EPIA, where she presents advancements in knowledge representation and reasoning. Her advising record is not explicitly stated in the provided texts. She has collaborated on grants from FWF, EU Horizon 2020, and other competitive funding bodies. Her research also intersects with cognitive factories and hybrid reasoning systems for robotics applications. As part of the DBAI group at TU Wien, she contributes to the development of AI tools and methodologies that prioritize comprehensibility and scalability. Her lab affiliations include the Knowledge-Based Systems Group (DBAI), where she works on theoretical and applied AI challenges.
Dr. Peter Schüller is a Professor affiliated with the Department of Knowledge-Based Systems at TU Wien (Vienna University of Technology). He holds the academic title of Privatdozent (Priv.-Doz.) and has a background in Technical Engineering (Dipl.-Ing. Dr.techn. / Bakk.techn.). His roles include academic research, consulting services for intelligent automation, and partnership with Potassco Solutions. He is based at Favoritenstrasse 11, Room HG0312, and can be contacted via peter.schueller@tuwien.ac.at or contact@peterschueller.com. Education : Master's Thesis: 'Reconstructing borders of manually torn paper sheets using integer linear programming' (2008) PhD (Dr.techn.) in Informatics Habilitation (Privatdozent) qualification Research Interests : Schüller specializes in declarative problem solving through Answer Set Programming (ASP), with focus areas including hybrid knowledge integration systems, inconsistency management, and applications in robotics, traffic optimization, and industrial automation. His work bridges theoretical advances in computational logic with practical implementations in enterprise software architecture and database systems. Projects & Grants : He has led projects funded by the Austrian Research Promotion Agency (FFG), Austrian Science Fund (FWF), and Vienna Science and Technology Fund (WWTF). Key projects include: 'Dynamic knowledge-based (re)configuration of cyber-physical systems' (2017–2020) 'Integrated Evaluation of Answer Set Programs' (2015–2018) 'Inconsistency Management for Knowledge-Integration Systems' (2009–2012) Consulting & Partnerships : Provides services in enterprise software design, database optimization, GDPR compliance, and hybrid knowledge systems. Official partner of Potassco Solutions. Collaborates on initiatives like AI4EU and HumanE-AI-Net. Labs & Teams : Active in TU Wien's Knowledge-Based Systems Group. Involved in developing the DLVHEX and Hexlite solvers.
Stefan Bruckner is Professor of Visualization at the University of Bergen, specializing in biomedical visualization, volume rendering, and visual data exploration. His work develops novel techniques for analyzing complex scientific datasets across meteorology, medicine, and materials science. Dr. Bruckner's research group develops interactive visual analytics tools for weather forecasting, medical diagnostics, and ensemble data analysis. His methodological innovations include GPU-accelerated rendering, visual parameter exploration, and uncertainty visualization. He received the 2011 Eurographics Young Researcher Award for contributions to illustrative visualization. Professional service includes program committee roles for IEEE VIS, Eurographics, and ECRTS conferences. His pedagogical contributions span visualization, computer graphics, and programming languages at institutions including École normale supérieure and École polytechnique.
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.
Nysret Musliu is an Associate Professor and Head of the Christian Doppler Laboratory for Artificial Intelligence and Optimization for Planning and Scheduling at TU Wien (Vienna University of Technology). He holds a PhD in Computer Science from TU Wien (2001) and a Habilitation (2007). His research focuses on AI techniques, optimization, scheduling, and timetabling, with applications in workforce scheduling, combinatorial optimization, and constraint satisfaction. Education: PhD in Computer Science, TU Wien, 2001 Habilitation in Applied Computer Sciences, TU Wien, 2007 MSc (Dipl.-Ing) in Computer Science, University of Prishtina, 1996 Research Interests: His work addresses AI-driven solutions for scheduling problems, including staff scheduling, timetabling (e.g., high school timetabling), and combinatorial optimization. He has developed algorithms for tree/hypertree decompositions and hybrid methods combining constraint programming with metaheuristics. Grants & Leadership: Project leader of the Christian Doppler Laboratory (2017–2024) Funded projects: ARTE (employee scheduling), Softnet Austria, Hypertree Decompositions Professional Activities: Conference chairs for CPAIOR 2021/2020 and PATAT 2018 Editorial roles in Constraints Journal and Journal of Mathematical Modelling and Algorithms Program committee member for IJCAI, AAAI, and GECCO Teaching: He teaches courses on artificial intelligence, problem-solving techniques, and machine learning at TU Wien. Lab & Teams: Leads the Christian Doppler Laboratory and collaborates with industry partners like Bosch and Ximes Corp.
Mantas Simkus is an Assistant Professor at TU Wien's Institute of Logic and Computation, affiliated with the Database and Artificial Intelligence Group. He previously held an Associate Professor position at Umeå University (Sweden) within the Wallenberg AI, Autonomous Systems and Software Program (WASP). He leads the FWF-funded project 'KtoAPP: Compiling Knowledge into Applications' and contributes to the Cluster of Excellence 'Bilateral Artificial Intelligence'. His research focuses on logic-based data management, knowledge representation, and nonmonotonic reasoning, with applications in semantic web technologies and ontology engineering. Education: Bachelor's in Computer Science from Vilnius University. Research interests include logic programming, computational complexity, description logics, and their integration with databases. He explores techniques for efficient query answering, schema validation (e.g., SHACL), and reasoning under incomplete information. His work bridges theoretical foundations (e.g., complexity analysis, formal semantics) with practical systems (e.g., ontology-mediated query processing). Key projects include 'KtoAPP' (2018–2025), investigating automated knowledge compilation, and contributions to the 'SemDat' and 'OMEGA' initiatives. He teaches courses on deductive databases and semi-structured data at TU Wien. He actively participates in academic service: co-chair of RuleML+RR 2024, editorial board member of the Artificial Intelligence journal, and former co-chair of DL 2019. His research group collaborates on topics like graph databases, answer set programming, and hybrid knowledge representation systems.
Stefan Szeider is a full professor and chair of the Algorithms and Complexity Group at the Faculty of Informatics, Technische Universität Wien (TU Wien). He also serves as a visiting scientist at UC Berkeley's Simons Institute for the Theory of Computing. His academic journey includes positions at the University of Durham (UK) and the University of Toronto (Canada), and he earned his Mathematics PhD from the University of Vienna in 2001. Dr. Szeider's research focuses on designing efficient algorithms for problems in Artificial Intelligence, automated reasoning, and combinatorial optimization. He leads several initiatives, including the Vienna Center for Logic and Algorithms (VCLA), and has secured funding from the ERC, EPSRC, FWF, and others. His Erdős number is 2, reflecting his collaborative network in mathematics and computer science. Key achievements include the first ERC Starting Grant awarded to an Austrian computer scientist (2009), and awards such as the Highlighted Paper Award at SAT 2023 and Best Paper at CP 2020. He advises numerous PhD students and postdocs, fostering the next generation of researchers in algorithms and complexity. Notable contributions extend beyond academia to public outreach, including initiatives like the 'Algorithms Think Differently' educational program and the 'Algorithms in 60 Seconds' video competition. His work bridges theoretical foundations and practical applications, influencing both academic and real-world computational challenges.
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.
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.
Prof. Nysret Musliu is an Associate Professor at TU Wien's Department of Databases and Artificial Intelligence within the Faculty of Informatics. His primary affiliation is with the E192-02 research area, focusing on optimization, scheduling, and AI applications. He leads projects like 'Artificial Intelligence in Employee Scheduling' and 'Predictive Analytics for Emergency Call Infrastructure,' demonstrating expertise in combinatorial optimization and real-world problem-solving. Academic Rank: Associate Professor Institution: TU Wien Key Projects: AI-driven scheduling, constraint programming, metaheuristics Research interests include scheduling algorithms, constraint programming, and hybrid optimization methods. His work bridges theoretical advancements with industrial applications, addressing challenges in manufacturing, healthcare, and transportation. Recent publications emphasize hyper-heuristics, large neighborhood search, and AI integration for complex scheduling problems. Notable contributions include systematizing test laboratory scheduling and developing exact methods for oven scheduling. His research group collaborates on projects involving personnel scheduling, production leveling, and parallel machine optimization.
Thomas Eiter is a Professor at TU Wien's Institute of Logic and Computation. His research focuses on declarative programming paradigms, knowledge representation, and artificial intelligence. He leads projects in neurosymbolic systems, answer set programming (ASP), and stream reasoning, with applications in visual question answering, scheduling optimization, and semantic scene generation. Eiter has contributed to foundational work in ASP semantics, computational complexity, and hybrid reasoning frameworks. His work bridges logical formalisms with practical AI challenges, emphasizing explainability and scalability. Projects like ALASPO and neurosymbolic integration showcase his focus on advancing both theoretical and applied aspects of AI. Projects: HumanE AI Network, WASP, REWERSE Research Themes: Neurosymbolic AI, Answer Set Programming, Stream Reasoning Notable achievements include pioneering work on semiring-based reasoning frameworks and developing efficient ASP solvers like Alpha. His contributions span over 471 publications, emphasizing interdisciplinary applications in computer vision, robotics, and automated planning.
Johannes Oetsch is a researcher at TU Wien's Forschungsbereich Knowledge Based Systems within the Faculty of Informatics. His work focuses on Answer Set Programming (ASP) , neuro-symbolic computing , and visual question answering systems . He holds a Diplom-Ingenieur (Dipl.-Ing.) and a Doctor of Technical Sciences (Dr.techn.) in informatics. Key research areas include: Integration of large language models with symbolic reasoning frameworks Optimization techniques in ASP for scheduling problems Explainability mechanisms for neuro-symbolic systems Recent work emphasizes visual question answering using graph-based representations and contrastive explanation methods. He has contributed to the development of ALASPO , an adaptive optimization framework for ASP solvers. His research also explores applications in manufacturing scheduling and automated testing of logic programs. Notable contributions include: Neuro-symbolic pipelines combining ASP with vision-language models Lexicographical makespan optimization in parallel machine scheduling Large-neighbourhood search strategies for ASP-based optimization
