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
Torsten Schaub is a Professor at the Institute of Computer Science , University of Potsdam. His research focuses on Answer Set Programming (ASP) , constraint solving, temporal reasoning, and combinatorial optimization, with applications in multi-agent pathfinding, product configuration, and course timetabling. Key contributions include ASP-based tools for industrial-scale optimization problems, metric temporal logic implementations, and frameworks for dynamic equilibrium logic. Recent work explores efficient design space exploration, stream reasoning, and multi-shot ASP solving for complex domains. His publications emphasize hybrid ASP systems , integrating constraints and temporal logic, with co-authors across Europe and Asia. He actively develops tools like clingo and Clingraph for practical ASP applications in logistics, bioinformatics, and robotics. The articles reveal a trend toward multi-agent systems (e.g., pathfinding algorithms) and temporal extensions in ASP, combining formal logic with real-world problem-solving. Sub-fields include constraint satisfaction, logical abduction, and declarative modeling for optimization tasks.
Dr. Jorge Fandinno is an Assistant Professor of Computer Science at the University of Nebraska at Omaha since 2020. His academic journey includes an Alexander von Humboldt Fellowship at the University of Potsdam (Germany) and a Postdoctoral Fellowship at the Toulouse Institute of Computer Science Research (France). He earned his Ph.D. in Computer Science from the University of Corunna (Spain) in 2015. Current Role: Assistant Professor, Computer Science Department, University of Nebraska at Omaha (2020–present) Previous Roles: Alexander von Humboldt Fellow (University of Potsdam, Germany), Postdoctoral Fellow (Toulouse Institute of Computer Science Research, France) Dr. Fandinno’s research focuses on Artificial Intelligence , particularly in Knowledge Representation and Reasoning , Answer Set Programming , and Epistemic Logic . His work bridges theoretical and practical aspects, including the development of formal semantics, deductive systems, and applications in causal reasoning and constraint handling. Recent publications emphasize automated reasoning, strong equivalence verification, and integrating quantitative information into logic programming frameworks. His scholarly output includes over 60 publications in prestigious venues such as Artificial Intelligence , Journal of Artificial Intelligence Research , and conferences like AAAI, IJCAI, and LPNMR. He has received three best technical paper awards at LPNMR (2015, 2017, 2019) and mentored students who won best student papers at JELIA and ICLP. Notably, he secured a NSF CAREER award (2024) for his project on Answer Set Programming for Quantitative Information. Scientific Awards: NSF CAREER award (2024) Best technical paper, LPNMR 2015 Best technical paper, LPNMR 2017 Best technical paper, LPNMR 2019 Best student paper, JELIA 2019 (supervised student) Best student paper, ICLP 2020 (supervised student) Dr. Fandinno’s publications span theoretical advancements in logic programming, epistemic reasoning, and practical applications in causal analysis and constraint satisfaction. His recent work explores automated reasoning tools (e.g., Anthem 2.0 ), recursive aggregates, and complexity assessments in ASP.
Mario Alviano is a Full Professor in Computer Science (INF/01) at the University of Calabria, Department of Mathematics and Computer Science. He leads the LAIA lab (Laboratorio di Applicazioni dell'Intelligenza Artificiale) and serves as co-PI in the PRIN project PRODE ('Probabilistic Declarative Process Mining'). Current projects: FAIR ('Future AI Research'), Tech4You ('Technologies for climate change adaptation'), SERICS ('SEcurity and RIghts in the CyberSpace'), CAL.HUB.RIA , RADIOAMICA , and STROKE 5.0 His research focuses on Answer Set Programming (ASP), particularly in optimization, nonmonotonic reasoning, and applications to logistics, healthcare, and cybersecurity. He has authored over 120 publications in top venues like AIJ, AAAI, and IJCAI. Recent academic contributions includes work on: Temporal Many-valued Conditional Logics Weighted Knowledge Bases with Typicality Explainable AI via xASP and ASP Chef Defeasible Reasoning Scalability Notable awards: Artificial Intelligence Award 'Marco Somalvico' (2017) ICLP Best Paper Awards (2015, 2016) LPNMR Best Paper Award (2022) CILC Best Paper Award (2023)
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.
Yannis Dimopoulos is a Professor in the Department of Computer Science at the University of Cyprus. He has previously held research positions at the Max-Planck Institute for Computer Science in Saarbrücken and the University of Freiburg in Germany. He earned his B.Sc. and Ph.D. in Computer Science from the Athens University of Economics and Business. His primary research interests include: Knowledge representation and reasoning Planning Nonmonotonic reasoning Constraint satisfaction Machine learning His recent research, based on publications from 2017 to 2024, focuses on the theoretical and computational aspects of abstract argumentation, particularly control argumentation frameworks, probabilistic extensions, and the integration of argumentation with Boolean networks and negotiation under incomplete information. He has also contributed significantly to Answer Set Programming (ASP) for planning, developing the plasp 3 framework for effective ASP-based planning solutions. His scholarly work appears in top-tier venues such as AAAI, IJCAI, ECAI, KR, AAMAS, and journals like Artificial Intelligence and Autonomous Agents and Multi-Agent Systems. His most frequent collaborators include Pavlos Moraitis, Jean-Guy Mailly, Antonis C. Kakas, and Wolfgang Dvorák. No scientific awards or honors are mentioned in the provided text. Yannis Dimopoulos has advised or collaborated with several researchers, including Jean-Guy Mailly, Pavlos Moraitis, Nabila Hadidi, and Muhammad Adnan Hashmi, though formal student-advisor relationships are not explicitly detailed. His work often involves theoretical and computational modeling, and while specific grants or funding sources are not listed, his sustained publication record suggests active research support. He is actively involved in research teams and collaborations focused on argumentation, multi-agent systems, and automated reasoning, as evidenced by his extensive co-authorship network.
Pedro Cabalar is Full Professor at the Department of Computer Science of the University of Corunna, Galicia, Spain, and current coordinator of the inter-university Master in Artificial Intelligence (Universities of A Coruña, Santiago de Compostela and Vigo). He also serves as Area Editor (Theory Foundations) for Theory and Practice of Logic Programming and as Standard Editor for the Artificial Intelligence journal. Education: PhD in Computer Science, University of Corunna, 2001 Master in Computer Science, Politéchnic University of Madrid, 1993 Bachelor in Computer Science (3-year degree), University of Santiago de Compostela / University of Corunna, 1989 Research interests revolve around Knowledge Representation & Reasoning , especially Answer Set Programming , non-monotonic reasoning , temporal and modal logics , and causal reasoning . He investigates theoretical foundations (equilibrium logic, temporal extensions, deontic operators) and practical systems (telingo, eclingo, aspBEEF), with applications ranging from planning and diagnosis to explainable AI and healthcare decision support. His recent articles (2023-2025) exhibit a clear trend toward temporal and metric extensions of ASP , explainability , and hybrid reasoning systems , often combining logic programming with deontic or probabilistic features. Scientific awards & recognition: University of Corunna Dissertation Award, 2003 Best Paper Award at LPNMR 2019 Best Student Paper at ICLP 2020 Best Student Paper at JELIA 2019 Grants & projects: He currently leads or co-leads nationally funded Spanish projects (GEISER 2024-2028, ARLEKIN 2021-2024) and has coordinated EU COST actions (DigForASP) as well as earlier MINECO projects on temporal ASP and medical reasoning (TARDIS, MERLOT, FEAST, etc.). PhD supervision: He has successfully supervised three PhD theses (Martín Diéguez, Jorge Fandiño, Brais Muñiz) and continues to advise students within the Information Retrieval Laboratory (IRLab) and the Spanish node of Potassco Solutions.
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.
Dr. Marina De Vos is a Senior Lecturer in the Department of Computer Science at the University of Bath. She leads research in knowledge representation and normative systems, with affiliations to multiple research centers including the Centre for Mathematical Biology and UKRI CDT in Accountable AI. Education: Postgraduate Certificate in Learning and Teaching in Higher Education, University of Bath (2010) Doctor of Science in Computer Science, Vrije Universiteit Brussel (2002) Master of Computing, Vrije Universiteit Brussel (1998) Research focuses on declarative programming paradigms, particularly Answer Set Programming (ASP) and normative multi-agent systems. Her work enables intuitive problem description in knowledge representation languages, with applications spanning structural engineering, music composition, legal reasoning, and policy modeling. Additional research interests include inductive machine learning, explainable AI, hybrid AI systems, and game theory. Recent publications demonstrate strong focus on norm synthesis in multi-agent systems, contextual reasoning, and governance frameworks for autonomous systems. This reflects a consistent pattern of applying formal computational methods to social and ethical dimensions of AI. Professional activities include keynote presentations at international conferences, workshop chair roles for major AI events, and external PhD examination responsibilities across European institutions.
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
Christopher Alexander is a Researcher at GE Research , focusing on formal methods and safety-critical systems. His work bridges Computer Science with Aerospace Engineering , emphasizing rigorous verification techniques. Research Areas : Answer Set Programming (ASP) SMT Solving Compliance Checking Conflict Detection Formal Verification Optimization in Autonomous Systems Recent publications highlight his contributions to 2023 Symposium on Practical Aspects of Declarative Languages and NASA Formal Methods 2024 . His work spans theoretical frameworks for deep learning and practical applications in aerospace systems.
Dr. Krysia Broda is an Honorary Senior Lecturer in the Department of Computing at Imperial College London's Faculty of Engineering. She directs the HiPEDS CDT Programme and coordinates the PhD Teaching Scholarship Programme, actively supervising doctoral candidates. Her office is located at 180 Queen’s Gate, London SW7 2BZ, U.K., and she can be contacted via phone (+44 20 7594 8426) or email. Her research bridges logic programming , automated reasoning , and neural-symbolic integration , with applications in computational biology and multi-agent systems. Key focus areas include: Abductive/Inductive Logic Programming Answer Set Programming (ASP) for knowledge representation Probabilistic reasoning in biological networks Teleo-reactive agent policies Her publications emphasize logic-based methods in AI, spanning theoretical foundations and real-world applications like gene regulation analysis and legal case inference. Recent work shows a trend toward integrating probabilistic models with symbolic AI for complex system validation. She leads the Structured and Probabilistic Intelligent Knowledge Engineering (SPIKE) group and collaborates with the Machine Learning Group. Current projects involve distributed abductive reasoning and ASP-based theory refinement.
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.
