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Manuel Lama PenínView profile

Professor

• Machine Learning
• Process Mining
• Predictive Monitoring
• +6 more

Manuel Lama Penín is a Full Professor at the Research Center on Intelligent Technologies (CiTIUS) with the University of Santiago de Compostela. His research bridges machine learning, process mining, and semantic modeling to address challenges in healthcare, industry, and e-learning. He has led 29 of 51 R&D projects and authored over 150 scientific works, including 52 journal articles. Research Areas: Process Mining & Conformance Checking Predictive Monitoring & Drift Detection Semantic Modeling & Service Computing Social Workflow Analysis & Data-to-Text AI in Healthcare & Educational Technologies Notable Projects: TRANSFIRESAUDE (2023, EU Health R&I Ecosystems) AQUATECHInn 4.0 (2023, Aquaculture Education) INCEPTION (2021, Concept Drift in Process Mining) Scientific Recognition: Recipient of the Transfer Technology award from the Galician Royal Academy of Sciences for co-founding InVerbis Analytics, a spin-off commercializing process mining research. Article Trends: His recent publications focus on explainable AI integration in EU regulations, drift detection methodologies, and natural language generation for process documentation. Collaborative efforts emphasize cloud-based solutions, healthcare process formalization, and predictive modeling across sectors.

Stephen M. FioreView profile

Professor

• Cognitive Science
• Team Cognition
• Human-AI Interaction
• +6 more

Stephen M. Fiore is a full-time Professor at the University of Central Florida , affiliated with the Department of Philosophy and the Institute for Simulation and Training . As Director of the Cognitive Sciences Laboratory , he leads multidisciplinary research integrating cognitive, social, organizational, and computational sciences. Research Focus: Complex collaborative cognition, human-AI teaming, simulation-based training, team science, and 21st-century skills assessment. Federal Engagement: DARPA ISAT Study Group, National Academies of Sciences consensus studies. International Roles: Visiting scholar at École Normale Supérieure de Lyon (2010), OECD PISA expert panel (2015). Awards: Pegasus Professor (2023), Scroll & Quill Society (2020), Luminary Award (2019), Reach for the Stars Award (2014), multiple Research Incentive Awards. Funding: $35M+ secured as PI/Co-PI, including NSF and Minerva Research Initiative grants. Scientific Contributions: Co-author of 250+ publications across human-human and human-AI team cognition, with key works on accelerating expertise, shared cognition, and adaptive training systems. His lab explores immersive VR, robotic perception scales, and ICE environment team dynamics. Leadership Roles: Co-founder and former President of the International Network for the Science of Team Science Co-founder and former President of the Interdisciplinary Network for Group Research Co-founder of the Global Alliance for Inter- and Transdisciplinary Research Key Collaborations: Santa Fe Institute (2013), Army's Robotics CTA, NSF-funded team science initiatives, and European research networks.

Fabio PatriziView profile

Associate Professor

• Formal Methods
• Knowledge Representation
• Machine Learning
• +7 more

Fabio Patrizi is an Associate Professor in Computer Science at the Department of Computer, Control and Management Engineering (DIAG) at Sapienza University of Rome. He serves as Coordinator of the Bachelor in Information Engineering program at the Latina site. His academic career includes significant service roles such as Editorial Board Member for the prestigious Artificial Intelligence Journal (AIJ) since 2023, Area Chair for ICSOC 2023, and co-chair for the KR 2025 track on Knowledge Representation & Reasoning and Planning & Scheduling. Patrizi's educational background led to National Scientific Habilitation for Full Professorship for sector SC 09/H1 (now GSD 09/IINF-05). His research spans Formal Methods, Knowledge Representation, Machine Learning, Reasoning about Action, Planning in AI, Service-oriented Computing, and Business Processes. His work focuses on theoretical, methodological, and practical aspects including Behavior and Service composition, Planning programs, MAS Verification, Reasoning About Actions, Infinite-plan synthesis, Data abstraction techniques, and Reinforcement Learning with non-Markovian Rewards. His publication record includes over 70 scientific papers in top-level international journals and conferences. Recent research trends show a strong focus on temporal reasoning in reinforcement learning, planning frameworks, process mining, and verification techniques. His work bridges theoretical foundations with practical applications in AI planning and verification. ICDT Test of Time Award on Automatic verification of data-centric business processes (2009) RMIT Visiting Researcher's Award (2011) ICAPS 2024 Outstanding SPC Award Patrizi has supervised 2 PhD students and actively serves on program committees for major AI conferences including AAAI, IJCAI, ICAPS, AAMAS, KR, and ECAI. His research projects include MARLeN (Principal Investigator, 2023-2025), AIPlan4EU (Task Leader, 2021-2023), DRAPE (Principal Investigator, 2019-2022), WhiteMech (Participant, 2019-2024), TAILOR (Participant, 2020-2023), VerySynCopated (Principal Investigator, 2014-2016), ACSI (Participant, 2010-2013), and SM4All (Participant, 2008-2011). He is a member of the Data Management & Service-Oriented Computing and Artificial Intelligence & Knowledge Representation research groups at DIAG, contributing to collaborative efforts in advanced AI research and applications.

• Answer Set Programming
• Computational Logic
• Algorithm Optimization
• +3 more

Daniel Bresnahan is a faculty member at the University of Nebraska at Omaha within the College of Information Science and Technology, Department of Computer Science. His research focuses on computational logic and algorithm optimization, particularly in the domain of Answer Set Programming (ASP). His work centers on improving the efficiency of logic programming systems through grounding size estimation and program rewriting techniques. Key publications include: System Predictor: Grounding Size Estimator for Logic Programs under Answer Set Semantics (2023) Grounding Size Predictions for Answer Set Programs (2019) Automatic Program Rewriting for Non-Ground Answer Set Programs (2019) These contributions explore performance prediction, combinatorial problem solving, and automated transformation of logic programs, with a focus on enhancing system efficiency through declarative programming methods.

• Artificial Intelligence
• Knowledge Representation and Reasoning
• Logic Programming
• +5 more

Thomas Eiter is a Full Professor at the Vienna University of Technology (TU Wien) in the Department of Knowledge-Based Systems, Faculty of Informatics. His research focuses on artificial intelligence, knowledge representation and reasoning, logic programming, computational logic, and neurosymbolic AI integration. He leads projects in declarative problem-solving, intelligent agent systems, and stream reasoning frameworks like LARS. Eiter has contributed to foundational work in answer set programming (ASP), algebraic reasoning, and their applications in scheduling, robotics, and real-time data processing. He has extensive international collaborations, including EU-funded projects like HumanE-AI-Net and the Austrian Science Fund (FWF) initiatives. His work emphasizes bridging symbolic AI with modern machine learning techniques, particularly in visual question answering and neural-symbolic systems. Eiter has supervised numerous PhD students and maintains active roles in academic leadership, including editorial boards of journals like Theory and Practice of Logic Programming . Key contributions include development of the DLVHEX system for hybrid knowledge representation, optimization frameworks for ASP, and methodologies for stream reasoning in dynamic environments. His research also addresses ethical AI through projects like the TAIGER initiative, focusing on training AI agents with ethical rules.

• Declarative Programming
• Answer Set Programming
• Knowledge Representation
• +2 more

Jia-Huai You is Professor in the Department of Computing Science at the University of Alberta, where he previously served as Department Associate Chair (Graduate). His research focuses on declarative problem-solving paradigms in artificial intelligence. Research examines computational aspects of knowledge representation systems including answer set programming, constraint programming, and SMT solving. Current investigations analyze semantic foundations, computational properties, and implementation strategies for declarative systems.

• Formal Methods
• Interactive Theorem Proving
• Proof Assistants
• +7 more

Chantal Keller is a Lecturer in the Computer Science Department at IUT d'Orsay, University of Paris-Saclay, and a member of the Formal Methods Laboratory (LMF), a joint unit involving CNRS and ENS Paris-Saclay. Her work bridges theoretical computer science and practical formal verification, with a strong focus on interactive theorem proving and proof assistant technologies. University: University of Paris-Saclay School: IUT d'Orsay Department: Computer Science Department Research Lab: Laboratoire Méthodes Formelles (LMF) Her research centers on enhancing the power and usability of proof assistants like Coq. She has developed tools such as SMTCoq , which integrates SMT solvers into Coq for automated reasoning, and HOLLIGHTCOQ , enabling interoperability between HOL-Light and Coq. Her PhD focused on formal proofs with SMT, and she continues to contribute to foundational aspects of type theory, lambda calculus, and logical frameworks. The 15 most recent articles reflect deep engagement with formal logic, programming language semantics, and automated reasoning. Key trends include proof interoperability, termination analysis, constraint programming, and the formalization of mathematical and computational systems in proof assistants. Her work spans theoretical developments and practical implementations in Coq, Agda, and OCaml. Chantal Keller has served on numerous program committees, including ITP, CPP, POPL, TAP, and JFLA, highlighting her active role in the formal methods community. She has held teaching and research positions since 2007, including roles at École Polytechnique and the University of Nottingham. 2025: Co-PC Chair, ITP 2022: Chair, JFLA 2021: Vice-Chair, JFLA; Co-Chair, PxTP 2017–2022: Coordinator, Digicosme UPSCaLe working group She has advised on workshops and school programs, including the EasyCrypt-F*-CryptoVerif school in 2014. She has not received any explicitly mentioned scientific awards in the provided texts. She is actively involved in teaching programming, algorithms, and mobile development at IUT d'Orsay.

• Educational Technology
• Computer Science Education
• Artificial Intelligence in Education
• +3 more

Joana Martinho Costa is an Associate Professor at Iscte-University Institute of Lisbon, where she serves as Director of the School of Applied Digital Technologies and coordinates degree programs in digital education. She leads the Distance Learning Implementation Project and is Principal Investigator of the EU-funded BRIDGE project addressing educational inequalities. Her research group affiliations include the ISTAR-Iscte Research Center where she serves as sub-coordinator of Information Systems research. Education: PhD in Education (Information and Communication Technologies), University of Lisbon (2019) Master's in Computer Science Education, University of Lisbon (2012) Bachelor's in Computer Engineering, New University of Lisbon (2010) Additional qualifications: Innovation in Education (Harvard, 2024), Distance Learning (ISCTE, 2022) Her research focuses on educational technology integration , particularly in computer science education, artificial intelligence applications in learning, and digital transformation of educational systems. She investigates technology-enhanced learning environments, programming pedagogy, computer-assisted assessment, and meta-analytical approaches to educational research. Current work explores AI-mediated learning processes and digital equity in education. Her publications demonstrate strong emphasis on meta-analytical studies of educational technologies, empirical investigations of digital learning environments, and interdisciplinary applications of data science in education. Recent work shows increased focus on pandemic-related educational adaptations and AI integration in pedagogical contexts. Awards & Honors: Santander Scholarship for Innovation in Teaching (2023) Iscte Research Awards (2020, 2021, 2022) She currently advises 3 Master's students and has supervised 12 completed Master's theses on topics including educational technology implementation, cybersecurity training, and AI in education. Leads multiple EU-funded projects including SYNCLUSIVE (labor market inclusion), ManagiDiTH (digital health transformation), and ATHENA (university digitalization). Directs research at the ISTAR-Iscte Center's Information Systems group and coordinates the Digital Education project. Leads international collaborations with universities in Finland, Greece, France and other EU countries.

• Logic and its applications in computer science
• Solving combinatorial problems
• Scheduling
• +4 more

Mateu Villaret Auselle is a Professor at the Universitat de Girona in the Department of Computer Science, Applied Mathematics and Statistics. He is affiliated with the Logic and Artificial Intelligence Research Group (LAI). Degree in Computer Science from UPC (1996) Doctor in Computer Science from UPC (2004) His research focuses on logic and its applications in computer science, particularly in solving combinatorial problems, scheduling and planning. He has been actively involved in the Logic and Artificial Intelligence Research Group (LAI). He has held teaching and management roles at Universitat de Girona since 1999, following a brief period at UPC (1996-1997) and UdG scholarship holder status (1997-1999).

Gil RegevView profile

Lecturer

• Requirements Engineering
• Business Modeling
• Enterprise Architecture
• +7 more

Gil Regev is a Lecturer at the Laboratory for Applied Software Engineering ( SSC-ENS ), part of the School of Computer and Communication Sciences ( IC ) at École Polytechnique Fédérale de Lausanne ( EPFL ). He serves as Mobility Delegate for IC-GE and has been affiliated with EPFL since 1997 after 9 years at Logitech. His academic work spans Requirements Engineering Business Process Modeling Enterprise Architecture Systems Thinking His research explores the intersection of organizational self-understanding and information systems design. Key trends in his work include Systemic approaches to service design Integration of appreciation and homeostasis in business processes Declarative specification methods Value network modeling Primitive obsession in software engineering Contextual requirements elicitation He has contributed to methodological frameworks like SEAM and practical tools for Service lifecycle management Business-IT alignment Flexible process modeling Regulation-based requirements Genomic data structure optimization Collaborative concept mapping

Yuanlin ZhangView profile

Associate Professor

• Artificial Intelligence
• Health Care
• STEM Education
• +3 more

Dr. Yuanlin Zhang is an Associate Professor in the Department of Computer Science at Texas Tech University, where he has been employed since 2004. He holds a PhD in Computer Science from the National University of Singapore. His research bridges artificial intelligence, healthcare applications, and STEM education reform. Research Focus: Dr. Zhang develops computational foundations for intelligent systems with dual applications: (1) Healthcare problem-solving (COVID-19 modeling, knowledge graphs for medical data) and (2) K-12 STEM education integration using logic programming. His NSF-funded projects include tools for intelligent systems and REU programs connecting logic programming to energy, security, and healthcare. Publication Trends (2019-2024): Recent works cluster in two domains: Computational Education (VR programming environments, curriculum integration, data science pedagogy) and Advanced Logic Systems (answer set programming, epistemic specifications, knowledge representation). Healthcare publications focus on pandemic modeling and biomedical knowledge graphs. Educational Technology: Dr. Zhang creates accessible tools like OnlineSPARC and VRASP to teach computational concepts, demonstrating consistent commitment to STEM education innovation.

Omar El KhatibView profile

Assistant Professor

• Artificial Intelligence
• Computer Science
• Answer Set Programming
• +2 more

Omar El Khatib is an Assistant Professor in the Department of Mathematics and Computer Science at Loyola University New Orleans, where he joined in Fall 2017. Previously, he served as an Assistant Professor at Taif University for seven years and as a Software Developer at Epic Systems Corporation in Madison, WI for two years. His educational background includes: Ph.D. in Computer Science from New Mexico State University (2007) Dr. El Khatib's research focuses on Artificial Intelligence with specialized expertise in Answer Set Programming, Machine Learning, and Deep Learning. His work applies declarative programming techniques to solve complex combinatorial optimization challenges across industrial domains. His publication history reveals a sustained emphasis on Answer Set Programming applications, addressing problems like assembly line balancing, pixel puzzle resolution, and job shop scheduling. These works demonstrate the versatility of logic-based approaches for modeling constrained real-world systems in manufacturing and computational problem-solving. Scientific awards: No scientific awards mentioned in the provided text Student advising and grant details are not specified in the source material. The text also contains no references to research laboratories, collaborative teams, or ongoing funded projects.

James KoleView profile

Professor

• Skill Acquisition
• Cognitive Psychology
• Educational Technologies
• +4 more

James Kole is a Professor in the School of Psychological Sciences at the University of Northern Colorado (UNC), within the College of Education and Behavioral Sciences. He joined UNC in 2013 after post-doctoral research at the University of Denver and University of Colorado, Boulder, focusing on marital communication and skill acquisition. His research examines memory mechanisms, skill transfer, educational technologies, and astronaut training efficacy under zero-gravity conditions (funded by NASA). He teaches core psychology courses including Psychological Statistics, Research Methods, and Cognition. Key research areas include: Skill acquisition and transfer in educational contexts Cognitive mechanisms underlying stereotype formation Evaluating educational technologies' psychological principles Astronaut training retention during space missions His 28+ publications span training optimization, memory load effects, and space cognition. Recent work explores second-language learning difficulty's impact on retention and astronaut-candidate performance comparisons. Dr. Kole collaborates with NASA and institutions like the University of Colorado. He advises the undergraduate honor society Psi Chi and serves on UNC's Curriculum Committee. His work bridges basic cognitive science with practical educational and space-exploration applications.

• Bioinformatics
• Theoretical Computer Science
• Computational Biology
• +3 more

Atze van der Ploeg is an Assistant Professor at the Vrije Universiteit Amsterdam's Faculty of Science, within the Theoretical Computer Science department. He is also affiliated with the Network Institute. His research focuses on interdisciplinary topics at the intersection of computer science and computational biology, with expertise in algorithm design, bioinformatics tools, and programming languages. He holds an MSc and a PhD, though specific academic details about his degrees are not provided in the text. His teaching responsibilities include courses such as Introduction to Programming (PYTHON), Object-Oriented and Functional Programming, and Systems Programming Project. These courses reflect his commitment to foundational programming skills and advanced software development practices. Key research interests include developing tools for bioinformatics (e.g., the PRALINE alignment toolkit), functional reactive programming, and visualization systems. His work emphasizes practical, principled approaches to solving complex computational problems. Though no specific grants or awards are listed, his publications span topics like type-safe dynamic typing, modular language implementation, and algorithm optimization, showcasing contributions to both theoretical and applied computer science.

Gerald Jay SussmanView profile

Professor

• Artificial Intelligence
• Computer Languages
• Computer Architecture
• +6 more

Gerald Jay Sussman is the Panasonic Professor of Electrical Engineering at the Massachusetts Institute of Technology (MIT). He received his S.B. (1968) and Ph.D. (1973) in mathematics from MIT and has been conducting artificial intelligence research there since 1964. His primary research focuses on understanding problem-solving strategies used by scientists and engineers, with dual goals of automating these processes and formalizing educational methodologies. He co-directs the Sussman Lab at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). His research spans artificial intelligence, computer languages, VLSI design, computational classical mechanics, synthetic biology, and telescope engineering. Notable contributions include co-creating the Scheme programming language, developing AI-based CAD tools for VLSI, designing the Digital Orrery for orbital mechanics simulations, and pioneering computational approaches to teaching classical mechanics. His current work includes developing explainable AI systems for autonomous vehicles. Professor Sussman's publications demonstrate broad interdisciplinary impact, with recent works spanning computer science education, software design, computational physics, and synthetic biology. His research consistently bridges theoretical computer science with practical engineering applications and educational innovation. Scientific Awards & Honors: Karl Karlstrom Outstanding Educator Award (ACM, 1990) Amar G. Bose Award for Teaching (MIT, 1992) IEEE EAB Major Education Innovation Award (2023) Taylor L. Booth Education Award (IEEE, 2024) National Academy of Engineering Member Fellow: IEEE, AAAI, ACM, AAAS, American Academy of Arts and Sciences He has supervised 46 PhD students spanning five decades, with dissertations covering AI, computer architecture, computational biology, and physical system modeling. His Sussman Lab develops computational tools for science education and engineering design, including contributions to the Magellan telescopes in Chile. Current projects involve explainable AI systems and computational mechanics frameworks.