Dr. Peter J. Robinson is an Honorary Research Fellow at the School of Electrical Engineering & Computer Science, The University of Queensland. His academic career spans over three decades, focusing on foundational research in programming languages, formal methods, and distributed systems. His work includes contributions to Qu-Prolog, a multi-threaded Prolog implementation, and TeleoR, a robotic task programming framework. Research interests include agent-based systems, blockchain security for aerospace applications, software verification, concurrent programming, and education technology. Notable projects include the Pedro publish/subscribe server and MyPyTutor, an interactive Python learning tool. He has collaborated on railway safety protocols and spacecraft control systems using blockchain. Publications span journals like Formal Aspects of Computing and conferences such as IEEE Symposium on Computers and Communications. Technical reports include work on unification algorithms and multi-agent verification frameworks. He has advised on projects involving IoT architectures and swarm intelligence simulations.
Manuel V. Hermenegildo is a Professor at the Universidad Politécnica de Madrid, Spain. He is a prominent researcher in the field of programming languages and static analysis, with significant contributions to logic programming, program verification, and formal methods. His work focuses on advancing static analysis techniques, compiler optimization, and energy-efficient computing. He has co-authored numerous papers and organized international conferences such as LOPSTR and SAS, demonstrating his leadership in academic and research communities. His research explores topics including abstract interpretation, runtime checking, and parallel logic programming systems. He is a key contributor to the Ciao Prolog system, emphasizing comprehensive tool integration and formal methods. His research interests span static analysis frameworks, program verification, resource usage analysis, and energy efficiency in computing. He has developed methodologies for optimizing program performance while ensuring correctness, with applications in both theoretical and applied domains. His work often bridges the gap between high-level program analysis and low-level hardware constraints, particularly in embedded systems. Recent trends in his publications include advancements in static cost analysis, dynamic inference of invariants, and tools for incremental assertion checking. His collaborations with institutions like the University of Copenhagen and the University of Kent reflect a global network in advancing computational logic and software engineering. He has been actively involved in academic service, including editorial roles for conference proceedings and journals. His contributions highlight a commitment to both foundational research and practical tools for the programming language community.
Nada Amin is an Assistant Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS), where she leads the metareflection lab . Her research combines programming languages (PL) and artificial intelligence (AI), focusing on neuro-symbolic systems that are correct by construction, with applications in program synthesis, verification, and precision medicine. Harvard John A. Paulson School of Engineering and Applied Sciences (2019–Present) University Lecturer in Programming Languages at University of Cambridge (2017–2019) Doctoral and postdoctoral work at EPFL (2011–2017) Her research spans three core themes: Safer : Type systems and formal verification (Coq, Dafny, Frama-C) Faster : Multi-stage programming and interpreter collapsing techniques Easier : Neuro-symbolic AI for program manipulation and biological reasoning Key publications include: 2025: Modular Imperative with LLMs (LMPL), Multi-stage Relational Programming (PLDI) 2024: Persimmon for extensible variant types (OOPSLA) 2023: LURK for recursive knowledge (ICFP), Dolorem language growth pattern (ECOOP) Scientific honors include: Teaching Assistant Team Award (EPFL, 2015) Michigan Cambridge Research Initiative Grant (2018) ArsDigita Prize (1999) She has served on program committees for GPCE (co-chair), ICFP, PLDI, and organized workshops in metaprogramming and logic programming. Her teaching portfolio includes graduate courses on neurosymbolic programming, program synthesis, and advanced PL theory.
Hector Levesque is a Professor in the Department of Computer Science at the University of Toronto. His research focuses on knowledge representation and reasoning in artificial intelligence, including formalization of concepts like belief, goals, and intentions, as well as tractable automated reasoning methods. He has authored influential textbooks and monographs such as The Logic of Knowledge Bases and Machines like Us . Levesque's work spans theoretical contributions to practical applications, including cognitive robotics and AI with common sense. Beyond academia, he is an active composer and creator of musical software, blending his technical expertise with artistic expression through projects like Rach2 and Three Adagios . His educational contributions include undergraduate and graduate textbooks used globally, supported by supplementary materials like slides and Prolog programs. Though no formal awards are listed, his prolific publications and interdisciplinary efforts highlight his impact in AI and computational theory.
Lan Nguyen Phuong is a Lecturer at the Faculty of Sciences, University of Angers. They hold a Doctorate in Computer Science from the National Polytechnic Institute of Grenoble (1992). Their research focuses on implementing logic and constraint programming languages, with expertise in areas such as compilation, memory management, Prolog, and multicore programming. Research activities include work on redundant coloring edges in graphs, as published in the Electronic Journal of Graph Theory and Applications (2016). They are affiliated with the MAY team and the LARIS public laboratory, contributing to interdisciplinary projects in computational theory and programming language optimization. No specific grants, awards, or advised students are listed in the provided text.
Paulo Jorge Pereira is an Invited Associate Professor in the Department of Information Science and Technology (ISTA) at ISCTE – Instituto Universitário de Lisboa. He has been actively involved in teaching computer science and engineering courses such as Introduction to Programming, Operating Systems, Web Applications and Usability, and Collaborative Systems across undergraduate and postgraduate programs from 2019 through 2026. Master's degree in Computer Engineering, Nova University of Lisbon (2015–2016) Bachelor's degree in Computer Engineering, Nova University of Lisbon (1983–1988) His research and academic interests span computer engineering, logic programming, computer graphics, web applications, and usability. His work bridges theoretical computing with practical software development, particularly in educational and interactive systems. He has contributed to the integration of logic programming with graphical systems, as seen in his early work on Prolog/GKS interfaces. The publication trends show a focus on applied computer science from the late 1980s to the late 1990s, particularly in logic programming and transportation modeling. His works reflect interdisciplinary research combining computer science with engineering logistics and graphical user interfaces. 1 citation in Web of Science 1 citation in Scopus There is no public information indicating that Paulo Jorge Pereira has advised students or received research grants. He appears to be actively contributing through teaching and technical research outputs. His cabinet is located in room D6.10 at ISCTE. He has not been associated with any formal research labs or collaborative research teams in the provided texts, but his publications suggest past collaborations with researchers from institutions in Portugal and abroad, particularly in the fields of computer graphics and transportation modeling.
Dietmar Seipel is a Professor at the University of Würzburg, affiliated with the Department of Computer Science within the Faculty of Mathematics and Computer Science. He has held this position since November 1995, establishing a distinguished academic career spanning over 25 years with significant contributions to logic-based computer science. Professor Seipel's research focuses on Logic Programming and Deductive Databases, with substantial expertise in Knowledge Engineering and Artificial Intelligence. His scholarly work bridges theoretical foundations with practical applications, particularly in rule-based systems, knowledge representation, and declarative programming paradigms. He has consistently advanced the field through both theoretical developments and practical implementations, creating tools that enable more effective knowledge management and reasoning systems. His publication trajectory demonstrates a clear evolution from foundational work in disjunctive logic programming to contemporary applications in knowledge representation and semantic technologies. Recent research shows continued innovation in integrating logic programming with modern programming languages and systems, including Python and JavaScript implementations. His work spans theoretical contributions to practical tool development, with applications across diverse domains including space systems, medical informatics, and business process management. Professor Seipel has made extensive contributions to the academic literature, with publications appearing consistently from the 1980s through to the present. His work has influenced both theoretical developments in logic programming and practical applications in knowledge-based systems. He has been actively involved in academic community building through conference organization, particularly for events related to declarative programming and knowledge management.
Darko Stefanovic is a Professor of Computer Science (and courtesy faculty in Chemical & Biological Engineering) at the University of New Mexico. He has been continuously active in teaching and research since at least 2000, with a focus on programming languages, molecular computing, and scientific simulations. University: University of New Mexico Department: Department of Computer Science Academic Rank: Professor His research interests span molecular computing , DNA nanotechnology , and synthetic biology , with recent publications exploring applications in artificial β-cells , heterochiral translators , and reservoir computing with chemical systems . Many publications involve collaborations with researchers in biochemistry, nanotechnology, and systems biology. Teaching activities include numerous offerings of courses such as Compiler Construction , Programming Paradigms , Software Foundations , and Algorithms and Data Structures at both undergraduate and graduate levels. Course materials often emphasize functional programming (e.g., Standard ML), λ-calculus, and logic programming implementations. Recent publications (2025–2019) demonstrate ongoing work in molecular robotics , DNA-based computation , and synthetic cell engineering . These works frequently appear in venues related to biomolecular computing , nanotechnology , and synthetic biology . Teaching Career spans multiple decades at UNM, covering topics from foundational programming to advanced compiler construction. Course materials show emphasis on formal methods, programming language theory, and computational models, including implementations of λ-calculus interpreters and Prolog systems.
Monika Blattmeier is a Professor in the Department of Mechanical Engineering at the University of Applied Sciences Emden/Leer. Her work bridges business process management with mechanical engineering, focusing on knowledge visualization and pattern-driven process design. Her research interests include knowledge management in business processes, aesthetic design in organizational structures, process modeling using pattern languages, and cultural influences on international business processes. She leads projects like ProlOg (NBank, ESF+) and Visual GP (Interreg). Recent publications highlight trends in business process visualization, pattern recognition, and aesthetic design. Older articles explore materials science topics like laser sintering and fatigue behavior of plastics in automotive manufacturing. Monika is involved in research-based teaching and learning, emphasizing pattern languages. She collaborates with institutions through Erasmus+ and contributes to academic discourse via journals like the Swiss Journal of Business Research and Practice and the Journal of Information Technology. She oversees the Denkraum (Knowledge Visualization Think Tank) and maintains an active presence on LinkedIn.
Etienne Le Quentrec is a Lecturer at the University of Strasbourg, affiliated with the ICube laboratory and the IMAGeS team (Images, Modeling, Learning, Geometry and Statistics). His academic focus spans discrete geometry, mathematical morphology, and image processing. Education: License in Mathematics (Strasbourg, 2014), External Aggregation in Mathematics (Strasbourg, 2016), Master 2 Research and Innovation (Toulouse, 2017), Master 2 MAPI3 (Toulouse, 2018) Research: Thesis on geometric characteristic estimation from image discretization, with applications in topology preservation and error modeling Teaching: Prolog, Python, Algorithms, Data Structures, Applied Numerical Analysis (2018-2021) He actively participates in scientific events including the Discrete Geometry and Mathematical Morphology conferences (2019, 2021) and co-founded the Association of Young Researchers of ICube (AJCI).
Philippe R. Richard is a Full Professor at the Department of Didactics within the Faculty of Education at Université de Montréal. Based in the Marie-Victorin building (Office D516), he specializes in mathematics education with a focus on technological integration. His research examines the intersection of mathematical reasoning and artificial intelligence, particularly in secondary education contexts. Richard's primary research interests include: Mathematics education and artificial intelligence integration Mathematical work and workspace theory Knowledge and reasoning modeling Problem-solving strategies and proof techniques Educational technology for mathematics learning Distance learning and teacher training methodologies His work emphasizes how AI transforms mathematical practice while addressing epistemological and cognitive challenges in educational settings. Analysis of recent publications (2016-2024) reveals consistent themes: the impact of AI on mathematical work, development of intelligent tutoring systems (especially for geometry proofs), and theoretical frameworks for technology-enhanced mathematics education. His research increasingly focuses on hybrid human-AI collaboration models and their didactic implications. Scientific Awards: Finalist for the Minister of Education, Leisure and Sports Prize (2004-2005) Ramón y Cajal Fellowship (co-funded by Spanish Ministry of Science and European Social Fund) Richard leads significant research initiatives, including a 5-year international project (2025-2030) examining AI-supported proof learning in secondary geometry. He directs the development of tutoring systems like QED-Tutrix and collaborates with interdisciplinary teams across Europe and North America. Current work explores instrumental proofs and discursive AI generation in mathematics education.
Vincent Englebert is a Professor at the University of Namur's Faculty of Computer Science, where he is affiliated with the Research Center on Information Systems Engineering (PReCISE) and the Namur Digital Institute (NADI). His office is located in the IT Building at Rue Grandgagnage, 21 in Namur, Belgium. He teaches courses including Organisational and Business Modelling, Software Architecture Engineering, and Software Engineering Laboratory for the 2023-2026 academic period. Bachelor and Master in Computer Science (University of Namur) Doctor of Science in Computer Science (2000), thesis: 'A smart Meta-CASE: towards an integrated solution' Englebert's research focuses on Domain-Specific Modeling Language (DSML) Engineering, Modeling & Meta-modeling, and Software Architecture Engineering, with additional interest in Computers and Aging applications. His work spans theoretical foundations of modeling languages to practical applications in business processes and aging populations. The fingerprint of his research shows strong emphasis on design (100%), engineering (78%), computer codes (70%), Reverse Engineering (63%), architecture (60%), tools (56%), modeling (55%), and transformations (50%). His recent publications (2020-2024) demonstrate continued productivity in model-driven engineering, with particular focus on software product lines, feature modeling, and applications for aging populations. These works show a clear trajectory from theoretical modeling foundations to practical implementations and societal applications. Best paper at International Conference Modelsward'2014 Englebert has supervised numerous research projects, including the current e-Anacarde: Digitalisation project (2024-2028) and previously led major initiatives like UsiXML and METADONE. He has been active in media and public engagement, particularly regarding computer science education in schools, with multiple expert commentaries in 2016. His external responsibilities have included significant leadership positions, notably as Dean of the Faculty of Computer Science (2015-2019) and Director of the PRECISE/IS research center (until 2011). He is active in research networks and collaborations, with recent external collaborations spanning multiple countries. His work with the NAM-IP Museum demonstrates ongoing engagement with cultural institutions, while his presentations on Alzheimer's disease and computer technology show commitment to applying informatics to societal challenges.
Roberto Bruni is a Professor in the Department of Computer Science at the University of Pisa, Italy. He has been actively contributing to theoretical computer science with a focus on formal methods, operational semantics, and reaction systems. His academic service includes serving as PC member for SAS 2025 and CMSB 2025 conferences. Professor Bruni's research interests span Operational Semantic, Petri Nets, Reaction Systems, Graph Transformation, and Monoidal Category. His work bridges theoretical computer science with applications in computational biology and program analysis. He has developed significant tools like BioReSolve, a Prolog interpreter for Reaction Systems analysis, and has contributed to projects such as RemConf (Confusion removal in Petri nets) and The Link Calculus. His recent publications demonstrate a strong focus on reaction systems, program analysis, and formal verification methods. The research trend shows increasing application of theoretical computer science concepts to biological modeling and healthcare applications, particularly through enhanced reaction systems frameworks. Professor Bruni holds several institutional responsibilities including membership in the Commission Didattica del GRIN since 2020, delegation to the University Information System (SIA) for the Department of Computer Science since 2020, and membership in the Doctoral College for Smart Industry since 2018. He has been Vice-president of the Master's Degree in Data Science and Business Informatics at the University of Pisa since 2017. His teaching portfolio is extensive, covering courses in programming, algorithms, quantum computing, business process modeling, and program analysis for both undergraduate and graduate students in multiple languages (Italian and English). He has taught courses such as Programmazione e Algoritimica, Models for Programming Paradigms, Business Processes Modeling, Program Analysis, and Introduction to Quantum Computing. Professor Bruni has been involved in numerous research projects including PRA (FM4HD, DECLWARE, FOG), PRIN (ASPRA, CINA, IPODS), and several EU-funded projects (IST-FP7 ASCENS, IST-FP6 SENSORIA, CoMeta, AGILE, NAPI, GETGRATS). His previous academic positions include PhD Student at University of Pisa, International Fellow at SRI Computer Science Laboratory, and Visiting Scholar at UIUC.
Manuel V. Hermenegildo is a Distinguished Professor at the IMDEA Software Institute and a Full Professor at the Department of Computer Science, Universidad Politécnica de Madrid. He holds a PhD in Electrical and Computer Engineering from the University of Texas at Austin (1986). His academic career spans over four decades, contributing to global program analysis, verification, and parallel computing. Education: PhD, Electrical and Computer Engineering, University of Texas at Austin (1986) M.S., Electrical and Computer Engineering, University of Texas at Austin (1984) M.S., Electrical Engineering, Technical University of Madrid (1981) Research Interests: His work focuses on global program analysis , verification , and optimization for functional/non-functional properties. He pioneered techniques in abstract interpretation , parallelism , and constraint/logic programming . His contributions include the Ciao programming system and the CiaoPP preprocessor, emphasizing static analysis and program debugging . Key Awards: ACM Fellow Julio Rey Pastor Prize (2006) Elected member of Academia Europaea (2010) Test of Time Award at ICLP 2017 His work has been cited over 10,000 times, with an h-index of 60. Leadership & Contributions: Founded and directed the IMDEA Software Institute Directed Spain's National Research Directorate (1999–2002) Chair of major conferences (POPL 2010, ICLP 2006) Labs & Teams: Leader of the CLIP Lab (Computational Logic, Implementation, and Parallelism) Principal investigator in EU-funded projects on resource-aware computing
Daniel Weidner is a researcher at the Chair of Computer Science VI (Artificial Intelligence and Knowledge Systems) within the Faculty of Mathematics and Computer Science at the University of Würzburg. His work spans declarative programming, database systems, and artificial intelligence applications. Primary affiliation: Chair of Computer Science VI, University of Würzburg Research focus: AI-driven database systems and logic programming His research interests include declarative technologies, logic programming, and their applications in data mining, tennis analytics, and NoSQL databases. He has extensively contributed to the development of tools that bridge Prolog and Python environments. Weidner has supervised multiple theses and seminar papers, including works on tennis trajectory recognition, heating system data analysis, and declarative program evaluation. His projects often involve Python-based implementations of deductive database systems. Supervised: 4 bachelor's, 2 master's theses Teaching roles: Logic programming, database exercises, and advanced database seminars Scientific awards: No explicit honors mentioned in available records.
