Find Academic Supervisors

Lukas GerlachView profile

Researcher

• Existential Rules
• Chase Algorithm
• Database Theory
• +6 more

Lukas Gerlach is a Research Associate and doctoral student at the International Center for Computational Logic (ICCL) within the Faculty of Computer Science at TU Dresden. His primary research focuses on existential rules, the Chase algorithm, database theory, logic, and complexity theory. He actively contributes to formal verification using the Lean theorem prover and develops the Nemo rule reasoning toolkit. His academic activities include teaching courses such as Database Theory , Theorem Proving with LEAN , and Complexity Theory . He collaborates on projects like Nemo (scalable Datalog engine) and CPEC (perspicuous computing systems). His work emphasizes theoretical foundations while producing practical implementations for knowledge representation and reasoning. Recent research trends show a strong focus on formalizing logical systems (e.g., infinite trees in Lean), analyzing Chase termination conditions, and improving Datalog-based systems' explainability through tools like EvonNemo. His publications span venues like ITP, PODS, and KR, addressing topics from foundational theory to engineering scalable reasoning systems. Gerlach also contributes to open-source projects such as Adf-bdd for abstract dialectical frameworks and maintains strong ties with the international knowledge representation community through conference involvement.

Francesco ScarcelloView profile

Professor

• Database Theory
• Constraint Satisfaction
• Structural Decomposition Methods
• +5 more

Francesco Scarcello is a Full Professor in Computer Science (SSD ING-INF/05) at the University of Calabria. He is affiliated with the Department of Computer Engineering, Modeling, Electronics and Systems Science (DIMES) and has held his current position since 2001. Education: PhD in Computer Science, University of Calabria (1997) His research spans database theory, constraint satisfaction, computational complexity, graph theory, knowledge representation, and game theory. He is renowned for developing hypertree-width decomposition methods, which identify tractable classes of complex problems in databases and CSPs. His publications focus on structural decomposition techniques, conjunctive queries, and disjunctive logic programming. These works have significantly influenced database theory and artificial intelligence. Scientific Awards: 2008 IJCAI-JAIR Best Paper Prize 2009 ACM PODS Alberto O. Mendelzon Test-of-Time Award Professor Scarcello serves as an Associate Editor for the Artificial Intelligence journal and actively participates in international conference program committees. He co-founded two University of Calabria spin-offs: Artémat and DLVSystem Evo-Bi, focusing on technology transfer in knowledge-based systems.

Annemarie BorgView profile

Assistant Professor

• Nonmonotonic Logic
• Formal Argumentation
• Defeasible Reasoning
• +3 more

Annemarie Borg is an Assistant Professor in the Responsible AI Group at the Department of Information and Computing Sciences, Utrecht University. She maintains an affiliated research position with the Research Group for Nonmonotonic Logic and Formal Argumentation at Ruhr University Bochum, where she completed her PhD. Her core research investigates defeasible reasoning through formal argumentation frameworks, emphasizing computational models of scientific inquiry and explainable AI systems. Borg's research spans: Nonmonotonic Logic : Modeling human-like reasoning under uncertainty. Formal Argumentation : Theoretical foundations for structured and abstract argumentation frameworks. Agent-Based Modeling : Simulating scientific communities and inquiry efficiency. Explainable AI : Developing transparent decision systems for real-world applications (e.g., Dutch National Police). Her publications focus on hypersequent-based argumentation, stability estimation in dynamic systems, and MCS (maximal consistent subsets) reasoning. Recent work emphasizes practical AI explainability, with theoretical rigor in logic design and computational efficiency. No awards, grants, or supervised students are documented in available sources. She collaborates with the Responsible AI Group at Utrecht, focusing on ethically aligned computational systems. Her future directions likely involve refining argumentation-based explanation frameworks for high-stakes AI applications.

Panagiotis RondogiannisView profile

Professor

• Logic Programming
• Nonmonotonic Reasoning
• Functional Programming
• +3 more

Panagiotis Rondogiannis is a Professor in the Department of Informatics and Telecommunications at the National and Kapodistrian University of Athens. He holds degrees from the University of Patras (B.Sc., 1989) and the University of Victoria, Canada (M.Sc. 1991, Ph.D. 1994). His research focuses on Logic Programming, Nonmonotonic Reasoning, Functional/Dataflow Programming, and Semantics of Programming Languages, with a particular emphasis on higher-order extensions and fixpoint theory. His work spans foundational aspects of logic programming semantics, including stable model semantics, categorical fixpoint theory, and non-monotonic reasoning frameworks. Key contributions include advancements in higher-order Datalog, lexicographic preference modeling, and the application of game semantics to logic programs. He has published extensively in top venues like Theory and Practice of Logic Programming (TPLP), ACM Transactions on Computational Logic (TOCL), and International Conference on Logic Programming (ICLP). Rondogiannis has collaborated with leading researchers such as Angelos Charalambidis and William W. Wadge, exploring topics like bilattice-based fixpoint theory and the implementation of recursive dataflow graphs in TensorFlow. His research bridges theoretical foundations with practical applications in areas like knowledge representation and programming language design.

Tomi JanhunenView profile

Professor

• Knowledge Representation and Reasoning
• Answer Set Programming
• Satisfiability Checking
• +6 more

Tomi Janhunen is a Professor in Computing Sciences at Tampere University, specializing in knowledge representation, automated reasoning, and logic programming. He previously served as Adjunct Professor at Aalto University (2019–2024) and maintains a Doctor of Science (Tech.) degree. His research spans answer set programming, satisfiability checking, optimization, and distributed computation. PhD, Aalto University (Doctor of Science (Tech.)) Adjunct Professor of Computer Science (Aalto University, 2019–2024) His research focuses on Answer Set Programming (modularity, verification, optimization), Satisfiability Modulo Theories , Nonmonotonic Logics , and Computational Complexity . He integrates logic programming into real-world applications like preventive maintenance scheduling and AI security systems. Recent work includes translating logic programs into integer programming, developing probabilistic reasoning systems (Plingo), and creating interpretable classifiers for tabular data. His publications emphasize stable model semantics , optimization techniques , and constraint networks . He supervises M.Sc., Lic.Sc., and Ph.D. theses and has completed pedagogical studies. Janhunen actively reviews for journals like Artificial Intelligence Journal and ACM Transactions on Computational Logic .

• Artificial Intelligence
• Computational Argumentation
• Uncertainty Representation
• +3 more

Pietro Baroni is a Full Professor of Computer Science at the Department of Information Engineering, University of Brescia. His research focuses on artificial intelligence, computational argumentation, uncertainty representation, decision support systems, automated diagnosis, and multi-agent systems. He has authored over 110 scientific papers and holds a patent in his field. Key research interests include formal argumentation frameworks, causal reasoning, and their applications in healthcare robotics, energy systems, and decision-making. His work bridges theoretical foundations (e.g., abstract argumentation semantics) with practical implementations in domains like medical automation and sustainable engineering. Notable contributions include advancing automated diagnosis methods for power grids, developing collaborative robotics for personalized medicine, and integrating user preferences into decision support systems. His recent publications emphasize explainable AI, energy efficiency optimization, and interdisciplinary applications of argumentation theory. Baroni’s research has been applied to real-world challenges such as wastewater treatment optimization and environmental footprint assessment. He actively contributes to academic communities through editorial roles and conference organization, including the Logic and Argumentation (CLAR) conference series.

• Artificial Intelligence
• Knowledge Representation and Reasoning
• Answer Set Programming
• +5 more

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.

• Answer Set Programming
• Knowledge Representation and Reasoning
• Cyber-Physical Systems
• +3 more

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.

• Logic Programming
• Computational Complexity
• Nonmonotonic Reasoning
• +4 more

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.

• Computational Logic
• Multi-Agent Reasoning
• Nonmonotonic Reasoning
• +3 more

Dr. Jürgen Dix is affiliated with the Department of Knowledge-Based Systems at TU Wien. His research focuses on advanced computational logic, multi-agent systems, nonmonotonic reasoning, and knowledge representation. He holds a doctorate (Dr.) and is recognized as a university lecturer (Univ.Doz.). Role: Researcher Affiliation: TU Wien, Department of Knowledge-Based Systems His work bridges theoretical foundations of logic programming with practical applications in deductive databases and agent-based reasoning systems. Contact: juergen.dix@tuwien.ac.at

David CarralView profile

Researcher

• Knowledge Representation
• Database Theory
• Artificial Intelligence
• +3 more

Dr. David Carral is a former Research Associate at the International Center for Computational Logic (ICCL) within the Faculty of Computer Science at TU Dresden. His work focuses on Knowledge Representation , Database Theory , and Computational Logic , particularly in the context of ontology-based data access, existential rules, and chase termination analysis. He contributed to projects like CFAED and collaborated on systems such as VLog and ROWL . David supervised theses including "Don’t Repeat Yourself: Termination of the Skolem Chase on Disjunctive Existential Rules" by Lukas Gerlach. His research bridges formal methods with practical applications in AI and database systems. Research interests include ontology design patterns , query rewritability , and efficient reasoning algorithms . He actively participated in conferences like KR and IJCAI, contributing to advancements in nonmonotonic reasoning and hyperproperties. His work integrates description logics, answer set programming, and semantic web technologies to address challenges in knowledge-based systems.

• Formal Methods
• Model-Driven Engineering
• SAT/QBF Solving
• +4 more

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.

Thomas EiterView profile

Professor

• Answer Set Programming
• Knowledge Representation
• Logic Programming
• +7 more

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.

Sarah Alice GagglView profile

Researcher

• Abstract Argumentation
• Answer Set Programming (ASP)
• Knowledge Representation
• +5 more

Dr. Sarah Alice Gaggl is a Researcher at the International Center for Computational Logic (ICCL) within the Technische Universität Dresden , where she has served as Group Leader for Logical Programming and Argumentation since October 2020. She also leads the BMBF-funded project NAVAS - Navigation in the Solution Space of Answer Sets and was a Principal Investigator in the Collaborative Research Center 248 (CPEC) from 2019 to 2022. Her research centers on Abstract Argumentation , Answer Set Programming (ASP) , and Knowledge Representation . Doctorate in Computer Science, Vienna University of Technology, March 2013 Her work bridges Answer Set Programming with practical applications in Knowledge Representation and Nonmonotonic Reasoning , focusing on navigation in plan spaces, efficient algorithms for ASP, and multi-criteria answer set selection. She has supervised numerous theses on topics like Multi-Shot ASP , Abstract Argumentation Frameworks , and Reinforcement Learning in game environments. Recent publications highlight trends in Answer Set Navigation (e.g., PlanPilot , IASCAR ), Algorithm Refinement (e.g., Winning Snake ), and Rule-Based Argumentation (e.g., Grounding Rule-Based Argumentation ). Her contributions span Computational Logic , Automated Reasoning , and Software Systems . She has held editorial and organizational roles for journals and conferences, including the Argument & Computation journal, KR , IJCAI , and ICLP . Dr. Gaggl leads the Logical Programming and Argumentation group and has been affiliated with the Computational Logic Group at TU Dresden since 2013. Her teaching includes courses like Theoretical Computer Science & Logic and Advanced Problem Solving .

Ekaterina ShutovaView profile

Associate Professor

• Natural Language Processing
• Machine Learning
• Multimodal Learning
• +3 more

Ekaterina Shutova is an Associate Professor at the Institute for Logic, Language and Computation (ILLC) within the Faculty of Science at the University of Amsterdam and a Visiting Associate Professor in Stanford University's Computer Science Department. She leads the Amsterdam Natural Language Understanding Lab and the NLP & Digital Humanities research unit while serving as an ELLIS Scholar. Her educational background includes a PhD from the University of Cambridge Computer Laboratory and Pembroke College, preceded by positions as a Leverhulme Early Career Fellow at Cambridge and Research Scientist at UC Berkeley's International Computer Science Institute. Shutova's research centers on natural language processing with machine learning from linguistic and multimodal data. Key interests include few-shot learning for NLP, multilingual systems, language-vision joint modeling, cognitive semantic representation, figurative language processing, and computational social science applications. Her work bridges computational linguistics, cognitive neuroscience, and AI ethics to develop culturally sensitive language technologies. Recent publications reveal strong trends toward multimodal cultural value alignment, metaphor understanding in LLMs, and cognitive modeling of language processing. Her research increasingly addresses societal implications through hate speech detection frameworks and cultural bias mitigation in vision-language systems. Major recognitions include: ERC Consolidator grant for "Towards globally accessible language technology and its alignment to cultural contexts" Outstanding Paper Award at ACL 2023 for work on NLP superhuman performance claims Finalist for TMLR Outstanding Certification for "Beyond the Imitation Game" AI Democratization grant from UvA RPA Human(e) AI for "AI clinics against hate speech" ELLIS Scholar designation Her research program is supported by ERC, Innovate UK, British Academy, and Leverhulme Trust grants alongside industrial funding from Google, Meta, and Deloitte. Current projects include benchmark development for abusive language detection and emotion-inducing communication strategies for misinformation identification. As leader of the Amsterdam Natural Language Understanding Lab, she directs interdisciplinary teams developing culturally aware language technologies while serving as ACL 2025 program co-chair to shape NLP research directions.