Inductive Logic & Statistics Professors and Supervisors

Sean Holdenمشاهده پروفایل

استاد

University of CambridgeUnited Kingdom

Sean Holden is a Professor in the Department of Computer Science and Technology at the University of Cambridge, affiliated with The Computer Laboratory. He holds positions at both the University and Trinity College. His research focuses on automated theorem proving, machine learning, and their intersections with formal methods and AI. He leads the development of the Connect++ theorem prover, which won the Best Newcomer award at CASC 2024. His work spans areas including connection calculus, graph neural networks, and reinforcement learning in games like Mahjong. Holden's academic contributions include over 50 publications in top venues such as IJCNN, ICPRAM, and Journal of Automated Reasoning. Notable works include foundational research on Bayesian methods in theorem proving and machine learning applications in bioinformatics and medical imaging. He serves as an Associate Editor for IEEE Transactions on Artificial Intelligence since 2023. His research group explores topics like protein graph embeddings, medical AI (e.g., breast cancer classification via MUGI-MRI), and the integration of machine learning with automated reasoning systems. Collaborations span institutions including Trinity College and international partners in computational biology and AI.

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Christopher Hitchcockمشاهده پروفایل

استاد

California Institute of Technology (Caltech)United States

Christopher Hitchcock is the J. O. and Juliette Koepfli Professor of Philosophy at the California Institute of Technology (Caltech). He holds a B.A. from Princeton University (1986) and M.A. (Philosophy & Mathematics) and Ph.D. (1993) from the University of Pittsburgh. He has served as Associate Professor (1998–2002), Professor (2002–2014), and Koepfli Professor (2014–present). From 2018–2022, he was Executive Officer of Caltech's Division of the Humanities and Social Sciences (HSS). Hitchcock's research focuses on causation, explanation, formal epistemology, and philosophy of science with interdisciplinary connections to computer science, law, linguistics, and biology. His work addresses causal concepts in scientific methodologies, legal reasoning, and everyday contexts. Key themes include probabilistic causation, causal modeling, counterfactual reasoning, and causal pluralism. He has been recognized with multiple awards: Philosophers' Annual 'Top 10 Papers' (1995, 2001, 2003), and the Philosophy of Science Association's Best Paper Prize (1998). Hitchcock currently serves as Associate Editor of the Journal of Causal Inference , Nominating Editor for Philosophers' Annual , and on editorial boards of the Stanford Encyclopedia of Philosophy and Philosophical Gourmet Report . His teaching spans courses on free will, philosophy of space/time, and probability/epistemology. Notable contributions include developing frameworks for understanding causal processes, interventions, and the interplay between causal and epistemic concepts.

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Manuel Blumمشاهده پروفایل

استاد

University of California, BerkeleyUnited States

Manuel Blum is a renowned computer scientist and winner of the 1995 ACM Turing Award. He holds the title of Bruce Nelson University Professor of Computer Science at Carnegie Mellon University's School of Computer Science. Previously, he was a faculty member at the University of California, Berkeley. His work spans theoretical computer science, computational complexity theory, cryptography, and program checking. He has supervised 35 doctoral students, many of whom are leading figures in computer science. Education: Ph.D. in Mathematics (MIT, 1964), M.S. in Electrical Engineering (MIT, 1961), B.S. in Electrical Engineering (MIT, 1959). Research focuses on foundational aspects of computing, including cryptographic protocols (e.g., CAPTCHA), program correctness mechanisms, and computational complexity. His contributions to secure human-computer interaction and zero-knowledge proofs are particularly influential. Teaching highlights include courses on theoretical cryptography and complexity theory. Awards include the ACM Turing Award, ACM Fellow, and UC Berkeley's Distinguished Teaching Award.

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Tom McCoyمشاهده پروفایل

استادیار

Johns Hopkins UniversityUnited States

Tom McCoy is an Assistant Professor in the Department of Linguistics at Yale University, with additional affiliations in the Department of Computer Science and the Wu Tsai Institute. His interdisciplinary research bridges computational linguistics, cognitive science, and artificial intelligence to understand the computational principles underlying human language. Dr. McCoy completed his PhD in Cognitive Science at Johns Hopkins University in 2021, where his graduate training emphasized interdisciplinary approaches to studying language acquisition from multiple theoretical and methodological perspectives. McCoy's research centers on two primary strands: language learning mechanisms investigating how humans acquire language from minimal data and how these capabilities can be replicated in machines, and linguistic representations exploring what computational architectures can effectively model language structure. His work frequently employs neural network language models while maintaining strong connections to linguistic theory and cognitive science principles. He has published influential work on hierarchical inductive biases in sequence-to-sequence networks and generalization capabilities of neural networks with respect to center embeddings. His recent publications reveal a research trajectory increasingly focused on the intersection of neural networks and cognitive science, examining inductive biases, Bayesian approaches to language learning, and the symbolic-subsymbolic debate in AI. His work appears in top venues including Nature Communications, Proceedings of the National Academy of Sciences, and leading AI conferences. McCoy actively mentors the next generation of researchers, currently considering PhD applicants for Fall 2026 and postdoctoral researchers for Fall 2026. He maintains a selective research group focused on computational linguistics and cognitive AI, prioritizing quality mentorship and methodological rigor. Though specific lab names aren't publicized, McCoy's research operates at the intersection of multiple departments at Yale, suggesting a collaborative environment where linguists, computer scientists, and cognitive scientists work together to tackle fundamental questions about language processing in both humans and machines.

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Martin Možinaمشاهده پروفایل

استادیار

University of LjubljanaSlovenia

Dr. Martin Možina is an Assistant Professor at the University of Ljubljana , affiliated with the Artificial Intelligence Laboratory since 2004. His work bridges classical machine learning with argumentation theory and focuses on interpretable AI methods. Research includes argument-based machine learning for knowledge-driven AI Developed nomogram visualization techniques for linear models Created automated chess tutors for move explanation Research projects span from 2004 to 2025, including: Current DRIFT project (2022-2025) on deep learning for power grid optimization Deep reinforcement learning applications in energy systems Argumentation for medical prognosis (lung cancer) and knowledge acquisition Publications show interdisciplinary work between: AI explainability (4/6 articles) Education technology (2/6) Healthcare AI applications (2/6) Graphical model interpretation (1/6) Multi-agent research (1/6) Teaching includes Decision Systems courses, with a focus on applied AI methods.

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Pablo Mosteiro Romeroمشاهده پروفایل

استادیار

Utrecht UniversityNetherlands

Dr. Pablo Mosteiro Romero is an Assistant Professor at Utrecht University's Faculty of Social and Behavioural Sciences within the Department of Methodology and Statistics . His work bridges Natural Language Processing (NLP) , Applied Data Science , and Human-centered Artificial Intelligence , focusing on language change, morphology-syntax trade-offs, and applications in healthcare and open societies. Education: B.Sc. in Physics, Rutgers University (Summa Cum Laude, 2007) Ph.D. in Computer Science, Princeton University (2014, Centennial Fellowship) Research Interests revolve around computational linguistics, information-theoretic approaches to linguistics, and ethical AI. He investigates synonym evolution, morphosyntactic interactions, and fairness in mental health-focused AI systems. Recent work includes de-identification in medical texts and taxonomy induction via reinforcement learning. Publications span top venues like ACL, IEEE, and Springer, covering topics such as topic modeling interpretability, bias discovery in ML, and multimodal negation processing. Collaborations with colleagues like Scheepers, Spruit, and Kaymak highlight interdisciplinary applications in electronic health records. Awards include the Centennial Fellowship at Princeton and Summa Cum Laude at Rutgers. As a University Teaching Qualification holder, he teaches courses in data science, Python programming, and research methods, supervising theses in AI and Business Informatics. His work aligns with Utrecht's Institutions for Open Societies (IOS) theme, addressing gender diversity and global justice in data-driven systems.

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Mahir Emrah Aktuncمشاهده پروفایل

مدرس

Rensselaer Polytechnic InstituteUnited States

Mahir Emrah Aktunc serves as a Lecturer in Cognitive Science at Rensselaer Polytechnic Institute (RPI), based in Sage 3203. Holding a Ph.D. in Science and Technology Studies from Virginia Polytechnic Institute and State University, an M.A. in Psychology from the University of Iowa, and a B.A. in Psychology from Koc University, he brings extensive interdisciplinary expertise to his role. His teaching portfolio spans undergraduate and graduate courses in cognitive science, research methods, statistics, logic, and philosophy of science across institutions in the U.S. and Turkiye. Education: Ph.D., Science and Technology Studies, Virginia Polytechnic Institute and State University M.A., Psychology, University of Iowa B.A., Psychology, Koc University Aktunc's research integrates empirical cognitive psychology with philosophical analysis, focusing on human working memory mechanisms, evidence evaluation in neuroimaging, inductive reasoning processes, and symbolic cognition frameworks. His methodology bridges laboratory neuroscience techniques with epistemological inquiry, particularly examining how theoretical assumptions influence fMRI data interpretation and memory model validation. This dual perspective enables novel critiques of methodological practices in cognitive science. His publication trajectory from 2004-2021 reveals consistent thematic evolution: early work established foundational memory paradigms (phonological similarity effects, word-learning stimuli), while mature research confronts epistemological challenges in neuroimaging (theory-ladenness, Duhemian problems, severe testing). Cross-cutting themes include error management in rule discovery and the interplay between cognitive architecture and scientific inference, reflecting his commitment to rigorous interdisciplinary synthesis. No scientific awards are documented in the provided materials. Dr. Aktunc currently teaches PSYC 4330: Introduction to Cognitive Neuroscience and PSYC 4450: Learning at RPI, mentoring students through empirical research design and philosophical analysis. While specific grant funding isn't detailed, his publication record indicates sustained scholarly productivity across cognitive psychology and philosophy of science venues.

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Matthieu EXBRAYATمشاهده پروفایل

مدرس

University of OrléansFrance

Matthieu EXBRAYAT is a Lecturer in Computer Science at the University of Orleans, affiliated with the Fundamental Computer Science Laboratory of Orléans (LIFO). He holds significant administrative responsibilities including Vice President for Digital and Educational Innovation at the University of Orleans and previously served as Co-head of the IT department (2019-2021) and Head of the IMIS Computer Science Masters (2015-2021). His research spans multiple domains with a primary focus on Machine Learning applications in data analysis and visualization, time series analysis, and computer vision applications in archaeology. His work demonstrates strong interdisciplinary connections between computer science and archaeological documentation, particularly in ceramic sherd classification using deep learning techniques. Earlier in his career, he contributed significantly to high-performance databases and probabilistic relational learning, especially Markov logic networks. Analysis of his publication history reveals an evolving research trajectory: beginning with database systems and parallel join algorithms in the early 2000s, progressing through Markov logic networks and clustering algorithms in the 2010s, and more recently focusing on machine learning applications in archaeological imaging and programming language analysis. His recent publications show a clear shift toward practical applications of machine learning in diverse domains while maintaining theoretical rigor. Throughout his career, EXBRAYAT has maintained extensive collaborative research networks, with Lionel Martin appearing as a co-author on 20 publications, followed by Guillaume Cleuziou (14), Jacques-Henri Sublemontier (9), and Quang-Thang Dinh (7). These collaborations span multiple research domains and demonstrate his ability to work across disciplinary boundaries. As an educator, he teaches distributed information systems, programming languages, databases, AI/data mining, and geographic information systems. He has also been actively involved in scientific mediation, delivering numerous public lectures on artificial intelligence topics since 2017, including conferences such as "Living in harmony with AIs" (2023) and "Artificial Intelligence: My new toaster is an AI!" (2019). His administrative contributions are substantial, including roles as Communications Correspondent for the Computer Science Disciplinary Center, Facilitator of the DataCenters ComUE Centre Val de Loire reflection group, and Vice President of Digital Resources at the Leonardo da Vinci Confederal University. He also served as LIFO website webmaster for over a decade (2002-2014) and was responsible for the STIC degree program (now IT degree) from 2004-2008.

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Bernd Fischerمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Bernd Fischer is a Professor and the current Head of Division in the Division of Computer Science at Stellenbosch University, South Africa. Previously, he held positions at TU Braunschweig, NASA Ames Research Center, and University of Southampton, establishing a strong international academic background in software engineering and formal methods. Professor Fischer's research focuses on automated software engineering, particularly logic-based techniques. His work spans specification-based component reuse, program synthesis, and program verification, with current emphasis on annotation inference, software model checking, and human-oriented presentation of verification results. His research bridges theoretical foundations with practical applications, particularly in concurrent program verification, grammar-based testing, and fault localization techniques. His work has significant implications for improving software reliability and developer productivity. Analysis of his recent publications reveals a strong focus on concurrent program verification through lazy sequentialization techniques, with substantial contributions to tools like CSeq and ESBMC. His research demonstrates consistent innovation in software verification, particularly in addressing the challenges of concurrency, bounded model checking, and fault localization. The interdisciplinary nature of his work connects theoretical computer science with practical software engineering challenges. ASE 2012 Most Influential Paper Award ACM Distinguished Paper Award Best Presentation Award Professor Fischer has successfully advised PhD students including Gillian Greene, who defended her thesis on "Concept-Based Exploration of Rich Semi-Structured Data Collections," and Geoff Birch, who completed work on "Fast, Fully-Automated, Model-Based Fault Localisation and Repair with Test Suites as Specification." His mentoring approach integrates theoretical rigor with practical tool development. His research has been supported through various academic grants enabling the development of multiple software verification tools. Professor Fischer leads development of several important software engineering tools including AutoBayes for statistical program synthesis, ConceptCloud for interactive visualization of software repositories, CSeq for concurrent program verification, and ESBMC for software model checking. These tools represent significant contributions to the software engineering research community and have been recognized in international verification competitions.

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Mathias Sablé-Meyerمشاهده پروفایل

پژوهشگر ارشد

Max Planck Institute for Human DevelopmentGermany

Mathias Sablé-Meyer is a Postdoctoral Researcher at University College London's Sainsbury Wellcome Centre, working in Tim Behrens's lab. He completed his PhD under the supervision of Stanislas Dehaene at PSL/Collège de France and NeuroSpin, CEA, where he investigated humans' ability to manipulate highly abstract structures, particularly focusing on the perception of geometry. His research spans cognitive neuroscience, computational modeling, and experimental psychology, with a focus on understanding the mechanistic implementation of compositional mental representations. He employs methodologies including MEG, fMRI, behavioral experiments, and computational models to investigate how humans mentally represent abstract structures like geometric shapes, language, and mathematical concepts. Sablé-Meyer's work reveals fascinating insights about human cognitive uniqueness, particularly in geometric perception compared to non-human primates. His recent research examines sequence processing mechanisms and aims to establish foundations for mechanistic implementations of Language of Thought models. He has published in top journals including Cognitive Psychology , Trends in Cognitive Sciences , PNAS , and Journal of Neuroscience . Glushko Dissertation Prize from the Cognitive Science Society (2023) As a supervisor, he co-advises Master's students including Maxime Cauté, who is exploring cross-modal representation of sequences, and is involved with several other students including Svenja Kuchenhoff, Amy Wong, and others. His lab follows open science principles, with code systematically published on the Open Science Framework alongside articles. His research program bridges cognitive science, neuroscience, and artificial intelligence, with implications for understanding both human cognition and developing more human-like AI systems. He maintains active collaborations across institutions including UCL, Collège de France, MIT, and École Normale Supérieure.

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