Hans Tompits is an Associate Professor in the Department of Knowledge-Based Systems at Technische Universität Wien (Vienna University of Technology). His research focuses on computational logic, declarative logic programming, and formal methods, with a particular emphasis on Answer-Set Programming (ASP). He coordinates the Master's program in Logic and Computation and leads projects in areas such as formal methods for optimization, fault-tolerant autonomous systems, and algorithmic composition. His work bridges theoretical advancements with practical applications, including tools like SeaLion (an ASP IDE with debugging support) and dlvhex (an ASP-based semantic web reasoner). He has contributed to foundational topics like program equivalence, debugging techniques, and integration of ASP with external systems. His recent projects address challenges in autonomous vehicle architectures, music composition algorithms, and safety-critical system design. Tompits has published extensively on topics ranging from nonmonotonic reasoning and modal logics to the development of declarative programming tools. His interdisciplinary approach spans computer science, mathematics, and AI, with applications in both academic and industrial contexts.
Nelson Nicolas Higuera Ruiz is a PreDoc Researcher at the Vienna University of Technology, affiliated with the Faculty of Informatics' Knowledge-Based Systems research group. His work bridges logic programming and deep learning for explainable AI. Research Focus: Neurosymbolic AI, Visual Question Answering (VQA), Answer Set Programming (ASP), and hybrid reasoning systems Projects: Leads optimization research in the LCS (2017–2025) project, developing neurosymbolic approaches for intelligent systems Key Contributions: Pioneering adaptive large-neighbourhood search algorithms for ASP optimization, modular neurosymbolic architectures, and contrastive explainability frameworks for VQA Collaborations: Active in international workshops and conferences including IJCAI, AAAI, and CLeaR, frequently collaborating with researchers like Thomas Eiter and Johannes Oetsch Publications: Focus on neurosymbolic integration, optimization algorithms, and explainability across AI, logic programming, and computer vision domains
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.
Thomas Eiter is a Full Professor at the Institute of Logic and Computation, Technical University of Vienna (TU Wien), where he serves as Head of Research Unit. He is a Full Member of the Division of Mathematics and Natural Sciences since 2022 and holds leadership roles within the university. His research focuses on knowledge representation and reasoning, computational logic, algorithms and complexity in AI, declarative problem solving, nonmonotonic logic programming and databases, and reasoning about actions and change. His work bridges theoretical foundations with practical applications in artificial intelligence, particularly in logic programming and knowledge-based systems. He has made significant contributions to Answer Set Programming (ASP), developing frameworks like DLV and HEX programs that enable sophisticated reasoning capabilities. His recent publications demonstrate a strong focus on stream reasoning (LARS framework), knowledge forgetting, modular reasoning systems, and the integration of logic programming with ontologies. His research shows consistent contributions to both theoretical foundations and practical implementations of AI systems over several decades. ACM Fellow (2020) Fellow of the European Association for AI (2006) Distinguished Paper Award of the 17th International Joint Conference on Artificial Intelligence (IJCAI, 2001) Prominent Paper Award of the Artificial Intelligence Journal (2013) Test of Time Award (10 years) of the International Conference on Logic Programming (2013) Eiter has led and participated in numerous research projects, both internationally funded (such as LogiCS@TUWien, Humane AI, AI4EU) and nationally funded (including projects like BILAI, TAIGER, and several FWF-funded initiatives). His research unit has received substantial support from European Commission programs (H2020) and Austrian funding agencies (FWF, FFG, WWTF). He is actively involved in the academic community as a member of the Austrian Academy of Sciences (ÖAW), Academia Europaea, and has served on the Executive Council of AAAI. His research unit maintains strong connections with international collaborators and has developed influential systems like the DLV answer set programming system.
Alexander Beiser is a Researcher at TU Wien's Department of Databases and Artificial Intelligence, part of the Faculty of Informatics. His work focuses on neurosymbolic reasoning, logic programming systems like Answer Set Programming (ASP), and hybrid grounding techniques to optimize computational workflows. He teaches the 2025S Algorithms and Data Structures course (186.866) as a VU lecture. His research bridges formal methods with practical applications in AI, emphasizing user interaction frameworks (e.g., clinguin) and improving efficiency in logic-based systems. Key areas include advancing neurosymbolic integration with LLMs, optimizing ASP-driven systems through hybrid grounding, and developing tools for interactive logic programming. Recent contributions address bottlenecks in grounding processes via splitting and rewriting strategies, aiming to enhance scalability in complex reasoning tasks. Alexander is based at Favoritenstrasse 9, Room HA0302, and can be reached via alexander.beiser@tuwien.ac.at. His work reflects a blend of theoretical advancements and applied systems engineering in AI and database technologies.
Daria Stepanova is a researcher at the Institute of Information Systems , Technische Universität Wien. Her work focuses on Answer-Set Programming (ASP), inconsistency resolution in hybrid knowledge bases, and optimization algorithms for scheduling and scene generation. Education: PhD (Dr.techn.) in Technical Sciences, MSc in Computer Science. Research Interests: Answer-Set Programming for scheduling and optimization Inconsistency handling in Description Logic Programs Hybrid reasoning systems combining logic and semantic methods Applications of automated reasoning in manufacturing and gaming Publications highlight trends in ASP-driven scheduling, inconsistency repair techniques, and semantic scene generation using contextual reasoning and algebraic measures. Labs & Teams: Active member of the Network Lab at TU Wien Collaborator on projects like ALASPO and Angry-HEX
Stefan Woltran is a Professor at TU Wien, affiliated with the Research Group Databases and Artificial Intelligence. His work focuses on formal methods in AI, particularly in abstract argumentation frameworks, knowledge representation, and computational logic. He leads research initiatives exploring semantics analysis, preference handling in argumentation, and algorithmic optimizations for complex reasoning tasks. His contributions bridge theoretical foundations with practical applications in logic programming and automated reasoning. Research Interests: Abstract Argumentation (semantics, expressiveness, complexity) Answer Set Programming (grounding optimization, formal properties) Knowledge Representation (formal systems, computational complexity) Algorithm Design (tree decompositions, dynamic programming) Publications reflect advancements in argumentation theory, logic-based AI, and algorithmic problem-solving. He supervises PhD students in these areas and collaborates on projects addressing bottlenecks in automated reasoning systems.
Wolfgang Faber is a researcher at Vienna University of Technology (TU Wien) specializing in Knowledge-Based Systems within the Faculty of Informatics. Holding the position of Privatdozent, he has completed his habilitation and is qualified to teach at the university level. His academic work centers on Answer Set Programming (ASP), Logic Programming, and Knowledge Representation, with significant contributions to theoretical foundations in these fields. Dr. Faber's research spans multiple areas of computational logic: Answer Set Programming and its theoretical underpinnings Epistemic logic programs and equivalence properties Knowledge representation and reasoning systems Nonmonotonic reasoning formalisms Integration of logic programming with external data sources His publication history reveals a sustained focus on theoretical aspects of ASP, with particular emphasis on program equivalence concepts (strong equivalence, uniform equivalence), computational complexity analysis, and applications to knowledge representation. Over two decades, his research has evolved from foundational work in disjunctive logic programming to specialized topics in epistemic reasoning within ASP frameworks. Dr. Faber has led and participated in numerous research projects including START (2014-2022) on Uniform Equivalence of Epistemic Logic Programs, SemDat (2012-2016), and multiple projects related to hybrid knowledge bases and Answer Set Programming from 2005-2015. His collaborative research extends across European institutions, evident through extensive co-authorship with researchers like Thomas Eiter and Stefan Woltran. While specific awards aren't documented in the available information, his sustained research output and leadership in multiple projects indicate significant recognition within his field. Dr. Faber has made substantial contributions to major systems in the field, most notably the DLV system for knowledge representation and reasoning, which has influenced both academic research and practical applications of Answer Set Programming.
Alice Tarzariol is a Researcher at the Institut für Artificial Intelligence und Cybersecurity within the Faculty of Technical Sciences at Alpen-Adria-Universität Klagenfurt. She specializes in computational logic, optimization algorithms, and their applications in artificial intelligence. Her research focuses on advancing Answer Set Programming (ASP) techniques for complex problem-solving, including production scheduling, symmetry breaking, and constraint learning. She also explores interdisciplinary applications such as cancer data analysis through logic programming frameworks. Her work emphasizes improving algorithmic efficiency and developing tools for combinatorial optimization, formal verification, and bioinformatics. She contributes to projects like the institute’s efforts to strengthen regional knowledge transfer and serves on the Curricularkommission für das Erweiterungscurriculum Gender Studies. Her research spans both theoretical advancements in computational logic and practical implementations in industrial and medical domains. Key themes in her publications include symmetry detection and lifting, constraint satisfaction, and inductive logic programming. Recent work highlights declarative approaches to production scheduling and efficient symmetry-breaking methods. Her research has implications for automated decision-making, systems modeling, and data-driven healthcare solutions.
Univ.-Prof. Wolfgang Faber is a Professor at the Alpen-Adria-Universität Klagenfurt (AAU), affiliated with the Institute for Artificial Intelligence and Cybersecurity. He holds academic positions in both computer science and engineering disciplines. His research focuses on artificial intelligence, computational intelligence, and theoretical computer science, with particular expertise in answer set programming (ASP), knowledge representation, and formal methods. His work integrates areas like ontology reasoning, action reversibility, and hybrid knowledge bases. Education details are not explicitly stated in the provided text, but his professional trajectory reflects advanced qualifications in engineering and computer science. Research priorities include database systems, semantic technologies, and formal languages, often addressing challenges in meta-reasoning and computational logic. His contributions span theoretical advancements and practical applications, such as ASP-based tools for planning and reasoning systems. Notable research trends include the development of efficient ASP solvers, evaluation of Datalog tools for OWL 2 QL meta-reasoning, and exploration of epistemic logic programs. His work on action reversibility in STRIPS planning and paracoherent answer set computation highlights his focus on bridging formal logic with real-world problem-solving. Collaborations and projects involve international conferences and workshops, including contributions to ICLP, JELIA, and Reasoning Web venues. Wolfgang Faber's professional engagement extends to academic service roles, including membership in the AAU Senate. His research is supported through grants and collaborations, though specific grant details are not provided here. He maintains an active presence in academic communities via ORCID and Google Scholar profiles, with a prolific publication record in top-tier conferences and journals.
Sanja Lukumbuzya is a PostDoc Researcher at the Knowledge-Based Systems group (E192-03) of Vienna University of Technology. Her work bridges formal logic and practical data systems, focusing on ontology-mediated data management, description logics, and business process challenges. Research Interests Ontology-Based Data Access (OBDA) Description Logics and Datalog Rewriting Process Mining and Workflow Analysis Hybrid Reasoning Systems Incomplete Data Handling Computational Complexity in Knowledge Bases Affiliations Vienna University of Technology, Austria Researcher in projects KtoAPP (2018–2025) and OMEGA (2017–2022) Publications Trends Her recent work addresses algorithmic complexity in expressive logics (2024), coNP expressiveness in ontology queries (2023), and practical BPM challenges (2023). Earlier studies focused on bounded predicates (2021), hybrid ASP-ontology frameworks (2020), and dishonesty modeling (2018).
Tobias Geibinger is a PreDoc Researcher at the Institute of Logic and Computation of Vienna University of Technology , supported by a DOC Fellowship from the Austrian Academy of Sciences. His work focuses on explainability in Answer-Set Programming (ASP), particularly for advanced language features and hybrid ASP systems. Education: BSc, Dipl.-Ing. (Master's equivalent) in Computer Science Projects: CD Laboratory for AI and Optimization (2017–2025), KIRAS-PrEMI (2019–2022), ARTIS (2017–2025) His research combines logic programming with constraint programming and hybrid methods to solve industrial scheduling problems. This includes automated test laboratory scheduling, photolithography job scheduling, and pandemic-era physician scheduling. Recent work investigates neurosymbolic integration and contrastive explanation frameworks. Selected publications analyze ASP optimization techniques for parallel machine scheduling, large-neighborhood search strategies, and nonmonotonic paraconsistent logics. These contributions align with broader trends in declarative AI and logic-based optimization. Scientific Recognition: ASAI Master Thesis Prize (2023)
Thomas Eiter is a Full Professor at the Vienna University of Technology (TU Wien) in the Department of Knowledge-Based Systems, Faculty of Informatics. His research focuses on artificial intelligence, knowledge representation and reasoning, logic programming, computational logic, and neurosymbolic AI integration. He leads projects in declarative problem-solving, intelligent agent systems, and stream reasoning frameworks like LARS. Eiter has contributed to foundational work in answer set programming (ASP), algebraic reasoning, and their applications in scheduling, robotics, and real-time data processing. He has extensive international collaborations, including EU-funded projects like HumanE-AI-Net and the Austrian Science Fund (FWF) initiatives. His work emphasizes bridging symbolic AI with modern machine learning techniques, particularly in visual question answering and neural-symbolic systems. Eiter has supervised numerous PhD students and maintains active roles in academic leadership, including editorial boards of journals like Theory and Practice of Logic Programming . Key contributions include development of the DLVHEX system for hybrid knowledge representation, optimization frameworks for ASP, and methodologies for stream reasoning in dynamic environments. His research also addresses ethical AI through projects like the TAIGER initiative, focusing on training AI agents with ethical rules.
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.
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.
