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Pierre CorbineauView profile

Assistant Professor

• Formal Proof Systems
• Certified Security Protocols
• Interactive Theorem Proving
• +3 more

Pierre Corbineau is an Assistant Professor in the Distributed and Complex Systems Group at the VERIMAG laboratory, teaching at Polytech'Grenoble engineering school within Joseph Fourier University. Previously, he held postdoctoral positions at Radboud University Nijmegen and completed his PhD at Laboratoire de Recherche en Informatique (LRI) in the Démons team. Education: PhD in Computer Science (2005), University Paris 11 Master's thesis (2001), University Paris 7 Research Interests: Focuses on certified security protocols, formal proof systems, and web-based collaborative theorem proving. Develops declarative proof languages (e.g., C-zar for Coq) and contributes to automated reasoning in type theory. Explores first-order logic proof search and equational reasoning. Teaching: Manages courses in microcontrollers, object-oriented programming, and UNIX system programming. Serves as pedagogic manager for Year 3 in the 3I department. Previously taught logic for mathematics aggregation preparation. Software Contributions: Developed C-zar (part of Coq since v8.1), a PTS type checker, and probabilistic binary tree implementations. Contributed to Coq's core functionality through reflective proof techniques and formal validation frameworks.

Heerko GroefsemaView profile

Assistant Professor

• Business Process Management
• Formal Verification
• Regulatory Compliance
• +3 more

Heerko Groefsema is an Assistant Professor at the Department of Distributed Systems, Bernoulli Institute, University of Groningen. He holds a PhD in Computer Science (2016) and has academic degrees from the same university and Hanzehogeschool Groningen. His research focuses on regulatory compliance verification, business process variability, and formal methods for business process models. He collaborates with CSIRO Data61 as a visiting researcher and serves as Interim Chair of the Board of Admissions for the MSc Computing Science program. Research interests include: business process compliance, formal verification techniques, process variability, service composition, and declarative specifications. His work addresses challenges in automated compliance checking, process model analysis, and variability management across industries. Recent articles emphasize online compliance monitoring for event streams, declarative process families, and cross-instance compliance evaluation. He has contributed to software tools like ProVariant and BPM Verification. He has supervised numerous master's and bachelor's projects, focusing on process modeling tools and compliance verification. His work has been published in top venues including CAiSE, TSE, and IEEE Transactions on Services Computing.

• Declarative Programming Languages
• Logic Programming
• Answer Set Programming
• +7 more

Dr. Ajay Bansal is a Teaching Professor and Associate Program Chair of the Software Engineering program at Arizona State University's School of Computing and Augmented Intelligence. He joined ASU in 2011 and previously served as a visiting assistant professor at Georgetown University. His academic journey includes a Ph.D. in Computer Science from the University of Texas at Dallas, an MS from Texas Tech University, and a B.Tech. from NIT Warangal. His research focuses on declarative programming languages, including Logic Programming, Answer Set Programming, and Knowledge Representation. He also explores Computing Education, particularly Individualized Learning and Game-based approaches. Notable achievements include the Test of Time Award at ICLP 2016 for coinductive logic programming and multiple teaching awards, including the Fulton Teaching Excellence Award (2019, 2022). Dr. Bansal has over 80 peer-reviewed publications spanning journals, conferences, and book chapters. His work bridges theoretical contributions (e.g., stableKanren integration) and practical applications like automated assessment systems. He actively contributes to courses such as Intro to Programming Languages and Database Management. Education: Ph.D. Computer Science, University of Texas-Dallas (2007) M.S. Computer Science, Texas Tech University (2002) B.Tech. Computer Science and Engineering, NIT Warangal, India (1999) Awards: Test of Time Award (ICLP 2016) Fulton Teaching Excellence Award (2019, 2022) Best Teacher Award (Top 5%) (6x) Advising & Grants: While no specific student names are listed, his roles involve academic advising and curriculum development. Industry experience includes software engineering roles at SIEMENS India, Metallect Corp., and Tyler Technologies. Labs/Teams: Engaged in educational technology initiatives and declarative programming research groups.

• Artificial Intelligence
• Knowledge Representation
• Logic Programming
• +6 more

Marc Denecker is a Professor at the Department of Computer Science, KU Leuven, affiliated with the Faculty of Engineering Science and the Declarative Languages and Artificial Intelligence (DTAI) research group. He contributes to KU Leuven's Institute for Artificial Intelligence (Leuven.AI). Current promotor for projects like A Category-Theoretic Perspective on Approximation Fixpoint Theory (2024-2028) and Proof systems for first-order logic extended with inductive definitions (2023-2027). Co-promotor for AI in Industry: Learning and Reasoning for Automation (2021-2025) and IMPULS-AI-2021 (2021-2024). His research focuses on formal logic, knowledge representation, and artificial intelligence. Recent work includes epistemic logic for decision-making under uncertainty, approximation fixpoint theory, justification theory, and symmetry-based satisfiability optimization. Publications span venues like AAAI, SAC, and LPNMR conferences, as well as journals such as Artificial Intelligence . Key subfields include logic programming, inductive definitions, model expansion, and constructive knowledge formalization. His teaching involves courses on complex systems (G0B23A/H0N05A), knowledge representation (H02C3A), automata (G0P84A), and logic for computer science (G0T42D).

Luc De RaedtView profile

Professor

• Artificial Intelligence
• Machine Learning
• Data Mining
• +3 more

Luc De Raedt is a Full Professor of Computer Science at KU Leuven, where he serves as the Director of Leuven.AI - KU Leuven Institute for Artificial Intelligence and Head of the Subdivision Declarative Languages and Artificial Intelligence within the Faculty of Engineering Science. He is also a Wallenberg Guest Professor at Örebro University's Center for Applied Autonomous Sensor Systems. Professor De Raedt's research focuses on the intersection of Artificial Intelligence, Machine Learning, and Logic Programming. His work spans probabilistic logic programming, neurosymbolic AI, statistical relational learning, and applications in various domains. He leads the Declarative Languages and Artificial Intelligence research unit (DTAI) at KU Leuven, which has been at the forefront of integrating symbolic and subsymbolic AI approaches. His research has evolved from traditional Inductive Logic Programming to Statistical Relational Learning and more recently to Neurosymbolic AI, which combines neural networks with symbolic reasoning for more transparent and trustworthy AI systems. His recent publications demonstrate a strong focus on neurosymbolic AI, with work spanning theoretical foundations as well as practical applications in areas like constraint learning, relational reinforcement learning, and probabilistic programming. His research integrates learning and reasoning in AI, with particular emphasis on creating systems that can both perceive patterns and perform logical reasoning. ERC Advanced Grant 2015 ERC Advanced Grant 2023 EurAI Fellow AAAI Fellow ELLIS Fellow Former IJCAI Trustee Elected Member of the Royal Flemish Academy of Belgium Wallenberg Guest Professor Professor De Raedt leads numerous research projects including "Bayesian Neurosymbolic AI for Trustworthy Machine Learning," "Neurosymbolic AI," "Theoretically Sound Neurosymbolic AI," and "Deep Probabilistic Logics." He is also involved in the Flanders Artificial Intelligence Research program (FAIR) as a work package leader. His research group includes PhD students and postdocs working on various aspects of neurosymbolic AI and probabilistic programming, with significant funding from European Research Council grants. He leads the Declarative Languages and Artificial Intelligence research unit (DTAI) at KU Leuven, which consists of multiple research teams focusing on different aspects of AI, machine learning, and logic programming. The unit has a strong international presence and collaborates with researchers worldwide, maintaining leadership in probabilistic logic programming through systems like ProbLog.

• Artificial Intelligence
• Machine Learning
• Constraint Programming
• +4 more

Siegfried Nijssen is a Professor at the Department of Computer Science within the Faculty of Engineering Science at KU Leuven. He is a core member of the Declarative Languages and Artificial Intelligence (DTAI) research group at the Arenberg campus and affiliated with Leuven.AI, the university-wide Institute for Artificial Intelligence. His academic position is designated as 'professor BOF', reflecting a specialized research-focused appointment. Nijssen's research centers on the integration of declarative programming paradigms with machine learning, particularly through constraint programming frameworks. Key focus areas include interpretable rule learning, neural-symbolic integration, and constraint-based optimization for combinatorial problems. His work bridges theoretical computer science with practical applications in bioacoustics, pandemic response modeling, and network analysis, emphasizing transparency and reliability in AI systems. Analysis of his 2021-2024 publications reveals a strong trajectory toward interpretable AI, with significant contributions like RL-Net (combining neural networks with rule-based reasoning) and novel approaches to NP-hard optimization using structured perceptrons. His research consistently targets the intersection of symbolic reasoning and statistical learning, addressing critical challenges in constraint imposition, model explainability, and stochastic optimization. Nijssen currently leads two major research initiatives: 'Declarative Languages for Imposing Constraints on Machine Learning Models' (2025-2027) and the long-term 'Declarative Programming for Machine Learning (DeclaLearn)' project (2025-2035), demonstrating sustained leadership and funding in his specialized domain. These projects extend his foundational work on constraint-based machine learning frameworks. Based at the DTAI research group, Nijssen contributes to KU Leuven's AI ecosystem through collaborative research in logic programming, constraint solving, and data mining. His work with Leuven.AI positions him at the forefront of institutional efforts to advance trustworthy and constraint-aware artificial intelligence systems.

• Programming Language Theory
• Functional Programming
• Logic Programming
• +3 more

Tom Schrijvers is a Professor at the Department of Computer Science, Faculty of Engineering Science, KU Leuven. His research focuses on programming language theory, functional programming, logic programming, and computational effects. Advancing multi-stage programming with computational effects (2024–2028) Generating Educational Feedback with Program Analysis (2024–2028) Programino: An Educational Programming Platform (2023–2027) eTeacher: Interactive web-based programming education platform (2023–2025) AmPERSand: Programming Education Runtime System (2023–2026) His recent publications emphasize bidirectional transformations, effect handlers, and programming education tools. He collaborates extensively with British institutions like Imperial College London and works on the book Language Engineering in Haskell with Dr. Nicolas Wu.

Enrico TassiView profile

Researcher

• Interactive Theorem Proving
• Formal Verification
• Type Theory
• +5 more

Enrico Tassi is a researcher at Inria, France, affiliated with the STAMP team. His work focuses on the technology of formal proofs, particularly type theory, interactive theorem proving, and its application to formalizing mathematics. He is a key contributor to the development of tools like Coq-Elpi , Hierarchy-Builder , and the small scale reflection Coq extension. His research spans proof assistant architecture, software design, and formal verification, with a strong emphasis on free software principles. Enrico completed his Ph.D. at the University of Bologna in 2008, where he designed the Matita interactive theorem prover. He contributed to the formalization of the Odd Order Theorem and the Paral-ITP project, aiming to scale Coq for large mathematical libraries. His software projects include Elpi , a meta-language for proof assistants, and past contributions to Debian as a developer (2006–2016), focusing on integrating the Lua language into Debian systems. His publications highlight innovations in unification algorithms, tactical design, and proof structuring techniques. Articles address challenges in bi-directional type inference, pattern-based proof command focusing, and nonuniform coercion implementations. His work on small-scale reflection and Hierarchy-Builder demonstrates expertise in algebraic hierarchies and formal library management. Current efforts target enhancing OCaml-based systems through Elpi's high-level programming capabilities. Key Collaborations : Mathematical Components team (INRIA), D.A.M.A. Project Major Tools : Coq-Elpi, Hierarchy-Builder, Matita Formalization Efforts : Ordered Uniformities, Odd Order Theorem, Finite Group Theory

• Artificial Intelligence
• Argumentative Dialogue
• Behaviour Change
• +6 more

Dr. Simon Wells is a Lecturer at the School of Computing Engineering and the Built Environment, Edinburgh Napier University. His research bridges Artificial Intelligence, Argumentative Dialogue, and Human-Computer Interaction, focusing on formal models of reasoning and communication. He has developed computational systems enabling argumentation between humans and machines, applied across scientific, educational, and policy domains. University of Dundee (2011) University of Aberdeen (2012) Edinburgh Napier University (2014–present) His work integrates Behaviour Change and Interaction Design through gamification and persuasive technology, particularly in sustainable transport systems. Projects like SUPERHUB and West of Scotland Herring Hunt highlight his interdisciplinary approach, combining AI with environmental and cultural studies. Recent publications analyze the synergy between formal dialogue models and Large Language Models, offering insights into argument visualization, socio-technical trust, and segmentation-based behavior interventions. Key research areas include argumentation theory, evolutionary robotics, and digital ethnography. PhD Supervisor: Yagmur Yigit, Dana Khartabil Co-Supervisor: Thomas Farrenkopf Funded by the William Grant Foundation and Natural Environment Research Council, his projects span from citizen-science tools in Brazil to historical socio-economic studies in Scotland. Collaborations include institutions like the Open Microscopy Environment and SICSA.

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.

Nikolaj BjørnerView profile

Researcher

• Symbolic Solving Techniques
• SMT Solvers
• Program Verification
• +10 more

Nikolaj Bjørner is a Partner Researcher at Microsoft Research , affiliated with the Research in Software Engineering (RiSE) group. His work centers on symbolic solving techniques, particularly through the development of the Z3 SMT solver , which underpins program verification, test case generation, and cloud network management. He has also contributed to distributed file systems and network verification . Academic Rank : Researcher Key Collaborations : Leonardo de Moura (Z3), George Varghese (network verification), Mooly Sagiv (controller verification) Research Interests : Bjørner’s work spans SMT solvers (Z3, muZ, SPACER), network verification (SecGuru, NoD, ddNF), and distributed systems (DFSR, RDC). His methods bridge logical reasoning, optimization, and network reliability. Article Trends : His publications focus on SMT solving (e.g., Z3, muZ, SPACER), network verification (ddNF, TeaVar), and quantifier instantiation (MBQI). These works integrate model-based reasoning and theory combination to address scalability in software and networking. Awards : 2021 ACM Fellow 2021 CAV Award (pioneering SMT contributions) 2019 Herbrand Award (Z3) 2017 Skolem Award (E-matching) 2016 Best Paper (HVC, ddNF) 2015 ACM SIGPLAN Software System Award (Z3) Collaborations & Students : Bjørner has mentored interns and collaborators like Daniel Selsam (neural networks for SAT), Nuno P. Lopes (Alive, NoD), and Rachee Singh (Shoofly, traffic engineering). He co-organized the 2022 Shonan workshop on SAT and served as program chair for conferences like PSI and FMCAD.

Neng-Fa ZhouView profile

Professor

• Programming Languages
• Constraint Programming
• Logic Programming
• +6 more

Neng-Fa Zhou is a Professor of Computer and Information Science at Brooklyn College and the Graduate Center of the City University of New York (CUNY). He holds a BS from Nanjing University (1984), and MS and PhD from Kyushu University (1988, 1991). Before joining CUNY, he served as an Associate Professor at Kyushu Institute of Technology (1991-1999) and held visiting positions at Yale, Alberta, Tokyo Tech, and Melbourne. Specializes in programming languages, constraint logic programming, and compiler design Developed Picat and B-Prolog languages with constraint-based graphics libraries Contributed to SAT encodings, multi-agent pathfinding, and declarative programming Scientific Awards: Most Practical Paper Award at PADL 2017 Award in ASP Solver Competition for BPSolver (2011)

Federico BergentiView profile

Associate Professor

• Artificial Intelligence
• Software Engineering
• Multi-agent Systems
• +4 more

Federico Bergenti is an Associate Professor in Computer Science at the University of Parma, affiliated with the Department of Industrial Systems and Technologies Engineering (DISTI) and the Department of Mathematics, Physics, and Computer Science. He serves as Chair of the AI Lab since 2015 and is actively involved in teaching Artificial Intelligence, Software Engineering, and related courses across multiple degree programs. His educational background includes a Laurea degree (M.Sc.) in Electronic Engineering from the University of Parma (1998) and a Ph.D. in Information Technologies from the same institution (2002). Prior to his academic career, he worked at CSELT S.p.A. (1998-1999) and CNIT (2000-2006). Bergenti's research primarily focuses on Artificial Intelligence and Software Engineering, with special emphasis on multi-agent systems. His work spans agent communication languages, architectures for agent-based middleware, reusability in agent systems, and more recently, agent programming languages based on constraint logic programming. He is among the founders of the JADE initiative and remains active in the Agent-Oriented Software Engineering research community. Analysis of his recent publications reveals a consistent research trajectory centered on agent-oriented programming (particularly JADEScript), indoor positioning systems, neural-symbolic integration, and mathematical modeling of multi-agent dynamics using kinetic theory approaches. His work demonstrates strong interdisciplinary connections between computer science, mathematics, and engineering applications. Professionally, Bergenti has coordinated various scientific initiatives, served on the Senior Program Committee of the AAMAS international conference since 2006, hosted the IEEE WETICE conference in Parma in 2014, and served as Program Chair for WETICE 2016. He has participated in numerous European Commission-funded research projects under the 5th and 6th Framework Programmes. He leads the AI Lab at the University of Parma, where his team develops practical applications of agent-based systems, including indoor localization technologies, health assistance systems, and social network modeling. His research combines theoretical foundations with real-world implementations, particularly through the JADE multi-agent framework.

• Concurrent Systems
• Formal Methods
• Modal Logic
• +4 more

Carlos Olarte is an Associate Professor at the LIPN (Laboratoire d'Informatique de Paris Nord) within the Institut Galilée at Université Sorbonne Paris Nord . His research focuses on concurrent systems , formal methods , and rewriting logic , with affiliations to the AVISPA research group . He has contributed to tools like PTA2Maude , PITPN2Maude , and SiLVer , addressing formal verification and logic-based systems. He holds grants including NATO Science for Peace (SymSafe, 2023) and CNPq projects (LOGOCOSMICS, 2019–2022). His work spans theoretical computer science, with recent publications in Tableaux , JLAMP , and Coordination . He actively participates in academic service, including PC chair roles for PPDP 2025 and local organization of Petri Nets 2025 . His research tools emphasize formal analysis of real-time systems, modal logics, and strategic reasoning. He has developed frameworks for linear logic theorem proving and declarative system specification.

Carlos DamásioView profile

Associate Professor

• Logic Programming
• Knowledge Representation
• Non-monotonic Reasoning
• +2 more

Carlos Damásio is an Associate Professor at the Faculty of Science and Technology , NOVA University Lisbon , affiliated with the SSDI section. His office is located in room P2/12, with contact details: telephone +351 212 948 300 ext. 10758 and email cd@fct.unl.pt . His research spans Logic Programming , Knowledge Representation , and Non-monotonic Reasoning , focusing on theoretical foundations and practical applications in Artificial Intelligence . He investigates computational logic frameworks for knowledge-intensive systems, semantic reasoning, and declarative problem-solving paradigms.