William Yang Wang serves as the Mellichamp Professor of Artificial Intelligence at the University of California, Santa Barbara (2019-present). He directs the UCSB Center for Responsible Machine Learning, the Mind and Machine Intelligence Initiative, and the UCSB NLP Group. His research focuses on theoretical foundations and practical algorithms for AI, particularly in NLP, LLMs, and neuro-symbolic reasoning. PhD in Computer Science from Carnegie Mellon University Active in AI theory and applications (2016-present) Research interests span multiple AI domains, with special emphasis on NLP and responsible machine learning. He has pioneered datasets like HybridQA, TabFact, and VaTeX, enabling advancements in multi-hop QA, fact verification, and video-language tasks. His work combines statistical relational learning with modern deep learning paradigms. Recent publications center around multimodal reasoning, knowledge graph integration, and responsible AI development. He has received numerous accolades including the IEEE SPS Pierre-Simon Laplace Award (2024) and NSF CAREER Award (2021). Karen Sparck Jones Award (2022) DARPA Young Faculty Award (2018) IBM Faculty Award Mentoring 15+ PhD and postdoc researchers who now hold positions at Microsoft Research, Amazon, Meta GenAI, and academic institutions like Arizona and Rutgers. His lab maintains active collaborations with industry partners through initiatives like ChipAgents.ai, which he founded as CEO.
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Bruno Felisberto Martins Ribeiro is an Associate Professor of Computer Science at Purdue University, joining the department in Fall 2015. His research focuses on endowing machine learning algorithms with robust invariant representations for relational and temporal data, emphasizing causal and associational tasks. Key research areas include Networking and Operating Systems, Artificial Intelligence, Machine Learning, and Natural Language Processing. He holds a Ph.D. in Computer Science from the University of Massachusetts Amherst (2010). Education: Ph.D., Computer Science, University of Massachusetts Amherst, 2010 Research Interests: Explores invariances in mathematics and machine learning to improve model robustness. Key topics include graph and tensor invariances, causal relationships, adversarial robustness, and applications in recommendation systems, robotics, and drug discovery. His lab’s work has advanced counterfactual task frameworks and causal reasoning in machine learning. Recent Contributions: Recent publications address zero-shot generalization in graph neural networks, causal discovery methods, and defenses against adversarial attacks. His work spans conferences like ICML, NeurIPS, and SIGCOMM. Awards: Best Paper Award at ACM CODASPY 2021 Best Paper Award at SIGMETRICS 2016 Best Paper Award at IEEE NetSciCom 2014 Advising & Students: Supervises current PhD students Beatrice Bevilacqua, Jincheng Zhou, and Yucheng Zhang, along with MSc student Ipsit Mantri. Notable former students include S Chandra Mouli (Meta), Yangze Zhou (Spotify), and Jianfei Gao (Vector Institute). Labs & Teams: Leads research in invariant representations and causal ML, collaborating with institutions like Stanford during his sabbatical. His work bridges theory and practice, impacting areas like network analysis and AI-driven healthcare.
Raymond J. Mooney is a Professor in the Department of Computer Science at the University of Texas at Austin, where he has been a faculty member since 1987. He is the Director of the UT Artificial Intelligence Laboratory and affiliated with multiple research groups including the Machine Learning Research Group, UT Computational Linguistics Lab, and the UT Center for Computational Biology and Bioinformatics. He holds a B.S., M.S., and Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign, where his thesis was supervised by Gerald DeJong. His research spans diverse areas in artificial intelligence, machine learning, and natural language processing: Natural Language Learning Connecting Language and Perception Statistical Relational Learning Information Extraction Transfer and Active Learning Abductive Reasoning Text Mining and Clustering Recommender Systems Knowledge-Base Refinement Recent publications highlight trends in grounded language processing, human-robot interaction, and multimodal reasoning. He has been recognized with prestigious fellowships including ACL (2014), ACM (2010), and AAAI (2005). Scientific awards: Fellow of the Association for Computational Linguistics (2014) Fellow of the Association for Computing Machinery (2010) Fellow of the American Association for Artificial Intelligence (2005) Classic Paper Award (2019) Best Paper Awards (2007, 2004, 1996) He teaches graduate courses like CS 371R: Information Retrieval and Web Search (Fall 2025) and CS 395T: Grounded Natural Language Processing (Spring 2025). His research labs include: UT Artificial Intelligence Laboratory Machine Learning Research Group UT Computational Linguistics Lab UT Center for Computational Biology and Bioinformatics
Sebastijan Dumancic is an Assistant Professor at Delft University of Technology, focusing on neuro-symbolic AI through program synthesis and probabilistic programming. He leads the RAIL lab and collaborates with institutions like Harvard, MIT, and CNRS. His research bridges symbolic AI and machine learning, applying program synthesis to scientific discovery, transportation, and robotics. He holds an FWO-funded PhD from KU Leuven and has participated in initiatives like ELLIS and the Symbolic Computation and Machine Learning Initiative. Program synthesis Probabilistic programming Neuro-symbolic AI Constraint-based learning His recent articles highlight advancements in program synthesis, neuro-symbolic integration, and constraint satisfaction. Projects like Find2Fix and Intelligent Greenhouse Horticulture (funded by NWO) demonstrate practical applications. ELLIS Membership University Teaching Qualification He supervises numerous MSc and PhD students in projects involving logic programming, program synthesis, and probabilistic modeling. Active in workshops and symposia, he contributes to neuro-symbolic AI and scientific discovery.
- Reasoning
- Concept Learning
- Memory
- +۸ مورد دیگر
Professor Brett Hayes is a distinguished cognitive psychologist at the University of New South Wales, serving in the School of Psychology. He is the founding Director of the Sydney Thinking and Reasoning (STAR) Laboratory, which he has led for over 15 years, securing more than $4 million in competitive research funding. Professor Hayes has previously held the position of Head of the School of Psychology and served as a member of the Australian Research Council (ARC) College of Experts. His research expertise spans reasoning, concept learning, memory, and developmental changes in these cognitive processes. Professor Hayes employs both experimental investigation and computational modeling in his work, with a particular focus on applying fundamental cognitive research to practical problems in forensic and clinical decision-making, early childhood education, and climate change science communication. His research has significant interdisciplinary applications across psychology, education, and environmental science. Professor Hayes has published extensively in top cognitive science journals, with his most recent work focusing on inductive reasoning, sampling assumptions, learning traps, and consensus perception. His research demonstrates consistent innovation in understanding how people process information, make decisions under uncertainty, and develop reasoning abilities across the lifespan. His scientific contributions include numerous journal articles, book chapters, and co-authored textbooks on developmental psychology. Professor Hayes has also contributed to teaching through courses such as PSYC3341 Developmental Psychology (which he chairs), PSYC3221 Cognitive Science, and PSYC2061 Developmental and Social Psychology. Professor Hayes maintains an active research program with ongoing collaborations across multiple institutions, as evidenced by his numerous co-authored publications. His laboratory continues to advance our understanding of human cognition through rigorous experimental work and theoretical development.
Stuart Kurtz is a Professor in Computer Science and the College at the University of Chicago, and serves as Master of the Physical Sciences Collegiate Division. He holds the endowed position of George and Elizabeth Yovovich Professor. His research focuses on theoretical computer science, including computational complexity theory, randomness in computation, type theory, and formal logic. He has contributed to foundational areas such as the Berman-Hartmanis Isomorphism Conjecture and the computational properties of random sets. Kurtz is affiliated with the Theoretical Computer Science and Programming Languages Groups at the University of Chicago. He has been recognized with the 2009 Quantrell Award for teaching excellence. His service roles include Director of Undergraduate Studies and Department Chair in Computer Science. He actively mentors Ph.D. students and has advised multiple graduates in complexity theory and related fields. His academic background includes a Mathematics Ph.D. from the University of Illinois, supervised by Carl Jockusch. He approaches type theory as an intersection of formal logic and functional programming. Research interests span measure-theoretic randomness, computational logic, and complexity class separations. His recent work explores connections between theoretical computer science and interdisciplinary fields like physics and statistics. In teaching, Kurtz has instructed courses such as Formal Language Theory, Discrete Mathematics, and Honors Intro Programming. His service contributions include roles in the Computation Institute and Toyota Technological Institute at Chicago. His lab affiliations and collaborative work reflect a strong commitment to advancing theoretical foundations in computer science.
Éric Tanter is a Full Professor in the Computer Science Department (DCC) at the University of Chile, where he also leads the PLEIAD Lab. He holds an Inria International Chair (2025–2029) and is an Associate Researcher at the Millenium Institute for Foundational Research on Data (IMFD). His academic leadership includes prior roles as Director and Deputy Director of the DCC, and Coordinator of the PhD Program in Computer Science. His research centers on programming languages and software engineering, with a strong focus on gradual typing, type systems, program verification, and secure programming. He explores both theoretical foundations and empirical practices, aiming to bridge the gap between static and dynamic language paradigms. His recent work emphasizes gradual verification, refinement types, and applications in proof assistants and security. The trends in his recent publications show a deep engagement with foundational aspects of gradual and dependent typing, often aiming to enhance the reliability and security of software systems. These works frequently involve formalization in proof assistants like Coq and explore applications in differential privacy, secure interoperability, and symbolic execution. Inria International Chair (2025–2030) Best Paper Awards at POPL 2019, OOPSLA 2018, ICFP 2018, MSR 2011, AOSD 2010, SBLP 2008, DAIS 2006 Most Influential/Most Notable Paper Awards at DLS, Programming, DLS Facebook Research Testing and Verification Award (2018) Google Faculty Research Awards (2015, 2016) Best Professor Award, University of Chile (2011) Éric Tanter has advised numerous PhD and Master’s students, many of whom have gone on to publish influential work in top venues. He has led multiple research projects funded by FONDECYT, ANID, INRIA, and other national and international agencies. His service includes editorial roles in journals such as the Journal of Functional Programming and Science of Computer Programming, and extensive participation in program committees of major conferences like POPL, ICFP, and OOPSLA. He leads the PLEIAD Lab at the University of Chile, a research group focused on programming languages, software engineering, and formal methods. The lab fosters collaboration with international institutions and emphasizes both theoretical rigor and practical impact.
Ayhan Günمشاهده پروفایل
دانشیار
- Control Systems
- Mathematical Modeling in Electrical Engineering
- Artificial Neural Networks
- +۱۲ مورد دیگر
Assoc. Prof. Dr. Ayhan Gün is an Associate Professor in the Department of Electrical and Electronics Engineering at Kütahya Dumlupınar University's Faculty of Engineering. With a career spanning over two decades, he has held various academic positions including Research Assistant, Assistant Professor, and currently Associate Professor since 2024. His extensive administrative experience includes serving as Head of the Control and Command Systems Department (2007-2021) and various leadership roles in university-industry collaboration initiatives. Dr. Gün completed his Bachelor's degree at Near East University (1991-1996), Master's at Dumlupınar University (1998-2001), and PhD at Eskişehir Osmangazi University (2001-2007). His research focuses on control systems, mathematical modeling, artificial neural networks, robotics, SCADA, PLC programming, electromechanical systems, nonlinear control, fuzzy logic, optimization techniques, automation, biomechanics, and mechatronics. His recent publications demonstrate a consistent research trajectory in control engineering, with particular emphasis on optimization algorithms applied to quadrotor control, inverted pendulum systems, and electrical motor design. His work bridges theoretical control concepts with practical implementations in robotics and power systems. A significant portion of his research involves applying swarm intelligence and evolutionary algorithms to solve complex control problems. Bilim, Sanayi ve Teknoloji Bakanlığı Kurumsal Kapasitenin Arttırılması (2016) BİLİM SANAYİ VE TEKNOLOJİ BAKANLIĞI Çift Beslemeli İndüksiyon Generatörü Tasarımı ve İmalatı (2016) Dr. Gün has supervised multiple graduate students and managed numerous research projects, including the current 'Robotic Arm Design and Implementation for Patients with Hemiparetic Arms' project. His external roles include serving as an expert witness for judicial institutions, project referee for TÜBİTAK, and publication reviewer for IEEE Transactions. He has also contributed to regional development through his work with Kütahya Governorship's Planning and Development Board.
Tetsuya Sakai is a Professor at the School of Fundamental Science and Engineering within Waseda University's Faculty of Science and Engineering. His work focuses on information access, retrieval, and natural language processing, with a particular emphasis on evaluation frameworks for search systems. Affiliations: Waseda University (Faculty of Science and Engineering, School of Fundamental Science and Engineering) Academic Rank: Professor Research Interests : Dr. Sakai's research spans four key areas: (1) Information Access —designing systems for direct and immediate information delivery, (2) Search Evaluation —developing metrics like Height-Biased Gain and hierarchical intent-based diversity measures, (3) Fairness in IR —pioneering frameworks for group fairness in conversational search, and (4) Statistical Reform —advocating Bayesian methods and robust experimental design. His work also addresses privacy inconsistencies in mobile apps and cognitive biases in LLMs. Scientific Awards : Notable recognitions include induction into the SIGIR Academy (2023) , ACM Distinguished Member (2018) , ACM Senior Member (2016) , and multiple DEIM/FIT/CSS Best Paper Awards . He has received teaching honors like the Waseda Presidential Teaching Award (2016) and WASEDA e-Teaching Award (2018) . Article Trends : Recent publications highlight: Advancements in LLM-assisted relevance assessments and hallucination diagnostics for tool-augmented models Conversational search fairness through multi-level evaluation frameworks and group diversity metrics Innovations in 3D medical reconstruction from clinical data and multimodal uncertainty modeling Statistical rigor via randomization tests , credible intervals , and topic set design Privacy analysis in mobile app descriptions and cognitive bias studies in search interaction
Salvatore Ruggieri is a Full Professor in the Department of Computer Science at the University of Pisa, where he teaches in the Master Programme in Data Science and Business Informatics. He is affiliated with the KDD LAB, a joint research group of ISTI-CNR and the University of Pisa, and actively contributes to national and European AI initiatives such as XAI, NoBIAS, TAILOR, and SoBigData.eu. His research focuses on data mining and knowledge discovery, with a strong emphasis on ethical AI. Key areas include discrimination discovery and prevention, fairness, privacy, explainable AI (XAI), causal inference, and classification algorithms. He has led significant projects such as ENFORCE, a national FIRB project on legal and computational enforcement of non-discrimination and privacy rights in ICT systems (2010–2014), and has served as program chair for the XIII Italian Symposium on Artificial Intelligence (2014). The recent publications (2018–2023) highlight a consistent trend in interpretable and fair machine learning, including selective classification, stability of interpretable models, and causal reasoning for fairness. His work often involves collaboration with leading researchers like Dino Pedreschi and Riccardo Guidotti, and appears in top venues such as AAAI, IEEE TKDE, and WIREs. His scientific honors include the award for the best Ph.D. thesis in Theoretical Computer Science from the Italian Chapter of EATCS. Best Ph.D. Thesis in Theoretical Computer Science, Italian Chapter of EATCS He advises and collaborates with numerous researchers in the KDD LAB and has contributed to major grants and research initiatives in AI and data science. He is involved in educational programs, including the National Ph.D. in Artificial Intelligence - Society, and promotes interdisciplinary research at the intersection of computer science, law, and ethics. Ruggieri is a member of the KDD LAB, where he leads research in ethical and transparent AI. He is also part of large collaborative networks such as SoBigData.eu and HumanE-AI-Net, which aim to build socially responsible and human-centered AI systems.
John D. Norton is a Distinguished Professor in the Department of History and Philosophy of Science (HPS) at the University of Pittsburgh, where he has been a faculty member since 1983. He served as Chair of the department from 2000 to 2005 and as Director of the Center for Philosophy of Science from 2005 to 2016. His work bridges the history and philosophy of physics, with deep engagement in Einstein’s relativity, quantum theory, statistical mechanics, and foundational issues in scientific reasoning. His educational background includes a PhD in the School of History and Philosophy of Science from the University of New South Wales (1982) and a Bachelor of Engineering in Chemical Engineering from the same institution (1974). Before transitioning to philosophy, he worked as a technologist at the Shell Oil Refinery in Sydney. Norton is renowned for his development of the material theory of induction , which challenges formalist approaches by asserting that inductive inferences are warranted by domain-specific facts rather than universal logical rules. He has also made significant contributions to the philosophy of spacetime, particularly through his analysis of the hole argument , and has published extensively on thought experiments, causation, and the thermodynamics of computation. His recent publications (2021–2025) reflect a sustained focus on induction, spacetime ontology, and the limits of thermodynamic reversibility and information processing. Key themes include the critique of Bayesianism, the historical development of thermodynamics, and the philosophical implications of time travel models in general relativity. His two major books— The Material Theory of Induction (2021) and its sequel The Large-Scale Structure of Inductive Inference (2024)—form a comprehensive philosophical framework for understanding scientific reasoning beyond formal logic. Co-Founder and Executive Committee Member, philsci-archive.pitt.edu Editor for Philosophy of Physics (Space and Time, General Physics), Stanford Encyclopedia of Philosophy Contributing Editor, Archive for History of Exact Science (1996–present) Associate/Co-Editor, Studies in History and Philosophy of Modern Physics Contributing Editor, Collected Papers of Albert Einstein , Volumes 3 and 4 Norton has advised numerous graduate students and has been instrumental in shaping the academic landscape of philosophy of science through editorial leadership and archival initiatives. His teaching includes graduate seminars on confirmation theory and the popular undergraduate course Einstein for Everyone , for which he maintains a freely available online textbook.
Wayne Myrvold is a Professor in the Department of Philosophy at Western University, Faculty of Arts and Humanities. His research lies at the intersection of philosophy of physics and philosophy of science, with a focus on quantum mechanics, statistical mechanics, and the foundations of probability. His primary research interests include: Philosophy of Physics, especially quantum mechanics and relativity Foundations of probability and its role in science Statistical mechanics and thermodynamics Quantum state realism and ontology Unification and scientific explanation Epistemology of scientific inference Myrvold's recent publications, including his 2021 book Beyond Chance and Credence , explore hybrid conceptions of probability that bridge epistemic and physical interpretations. His articles span topics such as quantum collapse theories, Bell’s Theorem, and the nature of the wavefunction, reflecting a deep engagement with both technical and philosophical dimensions of modern physics. His work frequently appears in leading journals like Studies in History and Philosophy of Modern Physics , Physical Review A , and Synthese . He is the Subject Editor for Quantum Mechanics in the Stanford Encyclopedia of Philosophy and has co-authored key entries, including the comprehensive overview of Bell’s Theorem. His scholarly contributions emphasize rigorous analysis of foundational concepts in physics and their philosophical implications. Myrvold holds a BSc from McGill University and a PhD from Boston University. He maintains an active research profile with a continually updated list of publications available via Google Scholar. He advises graduate students in philosophy of science and supervises research in foundational physics. While specific grant details are not listed, his sustained publication output suggests active research support. He is affiliated with the Rotman Institute of Philosophy, which supports interdisciplinary research in philosophy of science.
Simon Henryمشاهده پروفایل
دانشیار
Simon Henry is an Associate Professor in the Department of Mathematics and Statistics at the University of Ottawa, Faculty of Science. His research focuses on category theory, higher category theory, topos theory, and their applications in non-commutative geometry and constructive mathematics. He earned his PhD under Alain Connes (2010–2014), studying the interplay between topos theory and operator algebras. Since 2015, his work has expanded into higher category theory, including strictification theorems and the Simpson conjecture. Active on MathOverflow, he contributes to categorical logic, homotopy type theory, and foundational mathematics. Research interests include categorical homotopy theory, model categories, and the Grothendieck homotopy hypothesis. His publications explore inductive model structures, locale theory, and constructive approaches to metric spaces. He supervises students in these areas and maintains an arXiv profile with over 20 papers. Key contributions include work on ∞-categories, topos-based non-commutative geometry, and categorical foundations for univalent foundations. He also explores connections between Feynman diagrams and category theory, reflecting his interdisciplinary approach to mathematical structures.
- Logic Programming
- Statistical Relational Artificial Intelligence
- Probabilistic Logic Programming
- +۳ مورد دیگر
Fabrizio Riguzzi is a Full Professor at the Department of Mathematics and Computer Science of the University of Ferrara, Italy. His academic career spans over two decades at the same institution, having served as Associate Professor (2014-2020) and Assistant Professor/Ricercatore (1999-2014). He is an active researcher in the fields of Logic Programming and Statistical Relational Artificial Intelligence with numerous publications and leadership roles in international conferences. His educational background includes: PhD in Electronic and Computer Engineering from the University of Bologna (1999) Laurea in Computer Engineering from the University of Bologna (1995) Riguzzi's research focuses on probabilistic approaches to artificial intelligence, particularly probabilistic logic programming and statistical relational AI. His work bridges symbolic reasoning with probabilistic methods, developing frameworks for uncertain knowledge representation and reasoning. He has made significant contributions to probabilistic answer set programming, neuro-symbolic integration, and applications in areas like network intrusion detection and knowledge graph completion. His research demonstrates how logical formalisms can be enhanced with probabilistic reasoning to tackle real-world problems with uncertainty. An analysis of his recent publications reveals a strong trend toward integrating neural and symbolic approaches in AI, with significant work on probabilistic answer set programming frameworks. His research spans theoretical foundations of probabilistic logic programming, practical implementations, and applications in cybersecurity, knowledge graphs, and decision-making under uncertainty. The interdisciplinary nature of his work connects computer science theory with practical AI applications. His notable awards include: Alain Colmerauer 10-Year Test-of-Time Award at ICLP 2021 Best Paper Award for "BUNDLE: A Reasoner for Probabilistic Ontologies" at RR-2013 Highly Commended Paper Award for "Probabilistic declarative process mining" at KSEM 2010 Riguzzi has supervised several PhD students to completion, including Elena Bellodi, Riccardo Zese, and Giuseppe Cota, who have gone on to win prestigious awards for their theses. He has served in numerous editorial roles, including Associate Editor of the Journal of Artificial Intelligence Research and Editor in Chief of Intelligenza Artificiale. His leadership extends to organizing major conferences like ILP 2018 and serving on program committees for top AI venues including IJCAI, AAAI, and ECAI. He is a member of the ML@unife research group and has developed several online systems including cplint, TRILL, and an Online AUC calculator. His work has fostered collaborations across the AI research community, particularly in the areas of probabilistic logic programming and neuro-symbolic AI.
- Ockham's Razor
- Epistemology
- Formal Learning Theory
- +۷ مورد دیگر
Kevin Kelly is a Professor of Philosophy at Carnegie Mellon University and the Director of the Center for Formal Epistemology. His work bridges formal epistemology, computational learning theory, and philosophy of science, with a focus on Ockham's razor, belief revision, and the topology of inquiry. Key Research Areas: Ockham's Razor, Epistemology, Formal Learning Theory, Modal Epistemic Logic, and Interdisciplinary Applications of Topology. Grants: John Templeton Foundation grant for research on truth-finding efficiency and scientific simplicity. Scientific Awards: John Templeton Foundation grant (2018–2021) Kelly's publications emphasize connections between probabilistic reasoning and qualitative belief, solutions to the lottery paradox, and computational models of knowledge acquisition. His recent work explores lighting design, human-centric ergonomics, and machine learning epistemology, reflecting a deep interdisciplinary engagement with technology and science.