جست‌وجوی استاد و پژوهشگر

Gregor Betzمشاهده پروفایل

استاد

• Artificial Intelligence
• Philosophy of Science
• Computational Philosophy
• +۳ مورد دیگر

Gregor Betz is Professor of Philosophy of Science at the Karlsruhe Institute of Technology (KIT), where he is affiliated with the Department of Philosophy. His office is located in Building 9.20, Room 506, and he holds consultation hours on Wednesdays during lecture periods. His research spans the intersection of philosophy, artificial intelligence, and argumentation theory. Initially focusing on the limits of scientific prediction in economics and climate science, the role of values in science, and the ethics of climate engineering, Betz shifted toward computational philosophy after the release of GPT-2 in 2019. He has pioneered work in applying large language models to argument analysis and critical thinking. In terms of scholarly output, Betz's recent work demonstrates a strong focus on AI applications to philosophical problems, particularly in developing methods for logical argument analysis using language models. His research shows consistent progression from traditional philosophy of science topics toward computational approaches, with notable contributions including early work on what later became known as Chain of Thought reasoning, critical thinking applications for language models, and multi-agent simulations using LLMs. Betz has received recognition for his work through various research projects including Making Reflective Equilibrium Precise (SNF-DFG) and Argumentation at School (DFG Network Fund). His startup Logikon AI, founded in 2023, applies critical thinking methods to improve generative AI. His educational background includes an M.A. in Philosophy, Political Sciences, and Mathematics (2002), a Ph.D. in Philosophy (2004), and a Habilitation in Philosophy (2008), all from Freie Universität Berlin. He has authored numerous publications including the book 'Argumentationsanalyse: Eine Einführung' (2020) and the monograph 'Debate Dynamics: How Controversy Improves Our Beliefs' (2012).

• Artificial Intelligence
• Machine Learning
• Automated Algorithm Configuration
• +۳ مورد دیگر

Holger Hoos is a Professor in the Department of Methodology of Artificial Intelligence at RWTH Aachen University. His research spans artificial intelligence, automated algorithm configuration, and machine learning robustness, with applications in optimization, earth observation, and quantum computing challenges. Research Focus: His work emphasizes: Robustness verification and efficiency improvements in neural networks Automated Machine Learning (AutoML) frameworks and benchmarking Multi-objective optimization and algorithm configuration AI applications in remote sensing, time-series analysis, and recommender systems Recent publications (2024-2025) show a dominant trend toward enhancing AI reliability through rigorous verification methods, scalability solutions for large-scale problems, and adaptable frameworks for dynamic data environments. Quantum computing applications and energy-efficient AI also feature prominently. He leads research initiatives at RWTH Aachen focusing on methodological advances in AI, though specific labs/teams are not detailed.

Vikranth Rao Bejjankiمشاهده پروفایل

دانشیار

• Neuroscience
• Psychology
• Computational Modeling
• +۳ مورد دیگر

Vikranth Rao Bejjanki is an Associate Professor of Psychology at Hamilton College. His research focuses on the neural and computational mechanisms underlying human learning, employing methods such as psychophysics, computational modeling, and functional neuroimaging. He is affiliated with the Princeton Neuroscience Institute as a former postdoctoral researcher and maintains memberships in the Society for Neuroscience, Vision Sciences Society, Sigma Xi Scientific Honor Society, and the American Association for the Advancement of Science. Ph.D. in Brain and Cognitive Sciences, University of Rochester M.A. in Brain and Cognitive Sciences, University of Rochester B.A. in Cognitive Science, State University of New York at Buffalo B.S. in Computer Engineering, State University of New York at Buffalo Bejjanki’s research explores how humans learn from experiences through probabilistic inference, cue integration, and neural plasticity. His work bridges experimental psychology with computational models to understand visual perception, spatial localization, and the impact of video game training on cognitive adaptability. He has contributed to journals like Nature Neuroscience , Proceedings of the National Academy of Sciences , and PLoS Computational Biology , often integrating neuroimaging and behavioral analysis to decode learning mechanisms. His publications highlight interdisciplinary themes, merging neuroscience with artificial intelligence, cognitive modeling, and sensory processing. Key trends include the role of noise correlations in brain pattern classification, the development of perceptual templates through video games, and the application of Bayesian inference to spatial tasks. Scientific Awards & Affiliations: Sigma Xi Scientific Honor Society Society for Neuroscience Vision Sciences Society American Association for the Advancement of Science Bejjanki has taught courses such as Fundamentals of Human Neuroscience and Psychology and Neuroscience of Learning , reflecting his expertise in cognitive systems and neural mechanisms of learning. While his student advising record is not explicitly detailed, his collaborative research with institutions like Princeton underscores his commitment to advancing neuroscience through both theoretical and empirical approaches.

• Bayesian Statistics
• Category Theory
• Compiler Construction
• +۴ مورد دیگر

Matthijs Vákár is an Assistant Professor at the Department of Information and Computing Sciences , Utrecht University , focusing on the software foundations of machine learning . He actively develops the Stan language , a state-of-the-art probabilistic programming language. Areas of Expertise: Bayesian Statistics, Category Theory, Compiler Construction, Machine Learning, Programming Languages. Research Themes: Probabilistic programming languages, differential programming languages, separation of modeling from algorithmic tasks, automatic differentiation in ML-family languages. His research bridges theoretical computer science and practical machine learning, emphasizing logical relations, monoidal structures, and efficient compiler designs for differentiable programming. His recent publications explore correctness proofs, category-theoretic models, and optimization techniques in automatic differentiation. Scientific Awards: ERC Starting Grant NWO Veni Fellowship Marie Sklodowska-Curie Fellowship Academic Journey: PhD from the University of Oxford (dependent types and computational effects), MSc in Pure Mathematics (University of Cambridge), and BScs in Mathematics and Physics (Utrecht University). Prior roles include postdoctoral research at Columbia University and the University of Oxford. Industrial Experience: Build systems at Microsoft Research, NLP at Linguamatics, and compiler design for Stan.

Lorenzo Zuccatoمشاهده پروفایل

پژوهشگر ارشد

• Statistics
• Logic
• Preference Learning
• +۶ مورد دیگر

Lorenzo Zuccato is a Doctoral Research Fellow at the Department of Statistics and Data Science, University of Oslo. His work integrates statistical modeling with logical reasoning to address inconsistencies in data, particularly in preference learning frameworks. Education: Master's in Mathematics (University of Trento, 2024), focusing on Bayesian Nonparametric Clustering for Preference Learning. Industry Experience: Former Data Scientist specializing in anti-money laundering solutions. Teaching Role: Teaching Assistant for STK2130 - Modelling by Stochastic Processes. Research interests include Bayesian inference , preference modeling , and applications to finance and energy . His PhD project, "Modeling inconsistencies: a joint statistical learning and logical reasoning framework," aims to develop methods for handling inconsistent data across domains.

• Artificial Intelligence
• Machine Learning
• Game Theory
• +۷ مورد دیگر

Frans A. Oliehoek is a Full Professor in Interactive Learning and Decision Making at the Department of Intelligent Systems, Delft University of Technology. He co-leads the Sequential Decision Making group and serves as director and co-founder of the ELLIS Delft Unit. His academic affiliations include senior membership at AAAI, board membership at IFAAMAS, and associate editor roles for JAIR and AIJ. His research focuses on interactive learning and decision making , integrating AI, machine learning, and game theory to develop algorithms for agents interacting in complex, dynamic environments. Key themes include Bayesian reinforcement learning , influence-based abstraction , multi-agent coordination , and uncertainty modeling in real-world applications like traffic control, human-AI collaboration, and e-commerce agents. Recent publications highlight advancements in Bayesian RL with factored POMDPs , multi-agent safety guarantees , and state abstraction . His work spans theoretical foundations (e.g., Nash equilibria, MDP homomorphic networks) and practical frameworks (e.g., SHARPIE for human-AI experiments). Scientific contributions include: ELLIS Fellowship Senior AAAI Membership Best Paper Award at ALA 2021 Outstanding Paper Award at RLC 2024 He has advised students like Miguel Suau and Robert Loftin, with applications in multi-agent reinforcement learning , Bayesian planning , and human-AI interaction . Collaborations span institutions like UC Berkeley, University of Amsterdam, and MIT.

• Political Opinions
• Machine Learning
• Automated Text Analysis
• +۷ مورد دیگر

Nick Beauchamp is an Associate Professor in the Department of Political Science at Northeastern University. His research focuses on how political opinions form and evolve through discussion and deliberation, leveraging techniques from machine learning, automated text analysis, and social network analysis. His affiliations include The Roux Institute in Portland, ME, and Northeastern’s campuses in Boston, MA, and London, UK. University: Northeastern University Department: Political Science Rank: Associate Professor Beauchamp’s research integrates computational methods with political science to analyze political discourse, ideology prediction, and social dynamics. His recent work explores large language model personalization, stance detection, and temporal reasoning in political contexts. Selected trends from his publications include advancements in Natural Language Processing for political analysis, applications of Machine Learning to social networks, and interdisciplinary collaborations bridging Data Science and Public Health .

Nisheeth Srivastavaمشاهده پروفایل

دانشیار

• Cognitive Science
• Artificial Intelligence
• Computational Modeling
• +۷ مورد دیگر

Nisheeth Srivastava is an Associate Professor in the Department of Computer Science & Engineering and Cognitive Science at the Indian Institute of Technology, Kanpur. His research bridges computational models of cognition with real-world applications in AI and local problem-solving. Cognitive Science : Mathematically modeling human behavior inattention, memory, and decision-making. Artificial Intelligence : Integrating cognitive insights into AI agent design for human-like behavior. Solving Local Problems : Applying technical knowledge to address challenges in resource-constrained environments. His recent work explores predictive modeling limitations , probability inference from sparse data , and decentralized multi-agent systems , with trends spanning decision theory, machine learning, and user behavior analysis. Lab affiliates include Visiting Research Scientist Muhammad Aurangzeb Ahmed, focusing on agent-based simulations of religious affiliation.

• Trustworthy AI
• Medical Imaging
• Data Science
• +۶ مورد دیگر

Xiaofeng Liu is an Assistant Professor at Yale School of Medicine with joint appointments in Radiology & Biomedical Imaging and Biomedical Informatics & Data Science . He is an Associate Member at the Broad Institute of MIT and Harvard , and actively engaged in editorial roles as Associate Editor for IEEE Transactions on Neural Networks and Learning Systems and Journal of Medical Internet Research . His research spans trustworthy AI , medical imaging , and data science with applications in disease diagnosis and treatment monitoring. His research focuses on generalizable AI models for medical imaging, particularly in domain adaptation , diffusion models , and multimodal analysis . Recent work trends include AI-driven oncology (e.g., glioblastoma survival prediction), medical image enhancement (super-resolution, PET denoising), and clinical decision systems for Crohn's disease and depression detection. Collaborations with Georges El Fakhri and Jinsong Ouyang demonstrate cross-disciplinary approaches. 2025 Robert F. Wagner Best Student Paper Finalist Award 2024 Trailblazer R21 NIH Award 2024 NSF NAIRR Pilot Award 2024 Google Cloud Research Credits The Xiaofeng Liu Lab (Whisk Cup) develops AI methods for biomedical imaging, affiliated with the Yale Biomedical Imaging Institute and Center for Brain & Mind Health . The lab trains students in deep learning , medical image analysis , and health informatics .

Mario Románمشاهده پروفایل

پژوهشگر

• Category Theory
• Functional Programming
• Probabilistic Programming
• +۵ مورد دیگر

Mario Román is a Research Associate at the Department of Computer Science, University of Oxford , affiliated with the Compositional Systems and Methods Group at Tallinn University of Technology. His work bridges category theory , functional programming , and probabilistic programming , focusing on formal semantics and mathematical notation. His research interests include monoidal and premonoidal categories , Markov categories , and coinductive methods for dataflow programming. He explores the algebraic structures underlying quantum programming and stochastic processes , often applying graphical calculi and string diagrams. Recent work trends, as seen in his publications, emphasize distributive monoidal categories for program logics, effectful Mealy machines for bisimulation, and partial Markov categories for probabilistic reasoning. His collaborations span institutions in Europe and Asia, including Tallinn, Oxford, and Tokyo. Scientific Awards : Kleene Award, 'Monoidal Streams for Dataflow Programming', LiCS’22 Distinguished Paper, 'Effectful Mealy Machines: Bisimulation and Trace', LiCS’25 Best BSc Thesis, Spanish Royal Mathematical Society, 2018 International Mathematical Olympiad Honorary Mention, 2012 Mario has contributed to community initiatives as a member of the Compositionality Journal Executive Board and served on program committees for Applied Category Theory and Mathematical Foundations of Programming Semantics .

• Cognitive Science
• Artificial Intelligence
• Emotion Understanding
• +۴ مورد دیگر

Sean Dae Houlihan is a Neukom Computational Science Postdoctoral Fellow and Lecturer in the Cognitive Science Program at Dartmouth. His research focuses on computational models of social cognition, particularly how humans reason about emotions through Bayesian inference and generative models. Education : PhD in Brain and Cognitive Sciences from MIT, advised by Rebecca Saxe, Josh Tenenbaum, and John Gabrieli. Collaborations : Works with Luke Chang, Jonathan Phillips, and Soroush Vosoughi at Dartmouth; affiliated with the Center for Brains, Minds and Machines (CBMM) and the Dalai Lama Center for Ethics. His work bridges cognitive science, artificial intelligence, and neuroscience by reverse-engineering social intelligence through probabilistic programs. This approach aims to formalize psychological theories and develop machines with human-like emotional intelligence. The 15 most recent publications highlight his focus on Bayesian modeling of theory-of-mind, cross-cultural emotion representation, and probabilistic programming frameworks like memo. Key themes include causal inference, social coordination, and multimodal integration in emotion understanding. Awards : Glushko Dissertation Prize (2024), Best Dissertation in Affective Science Award (2024).

Jeffrey A. Livingstonمشاهده پروفایل

دانشیار

• Behavioral Economics
• Experimental Economics
• Economics of Education
• +۷ مورد دیگر

Jeffrey A. Livingston serves as Associate Professor of Economics at Bentley University, where he has maintained active research and teaching programs since joining the faculty. His academic appointments include regular scholarly contributions through 2024 and current grant funding extending to 2025. Education Background: Ph.D. from University of Maryland-College Park (2003) M.A. from University of Maryland-College Park (2000) B.A. from University of Kansas (1997) B.A. from University of Kansas (1996) Professor Livingston's research program centers on behavioral and experimental economics, with particular focus on how irrational individual behaviors impact market outcomes. His work spans education policy analysis, discrimination studies, and charitable giving behaviors. Recent research has expanded into the economics of artificial intelligence, examining trust dynamics in human-AI interactions. His publication record shows consistent scholarly output since 2003, with recent work examining contemporary issues like generative AI trustworthiness and media effects on charitable giving. The research employs experimental methodologies across diverse contexts including education, charity, and technology adoption. Current Research Funding: Bentley Research Council Grant (2024-2025) Faculty Affairs Committee Grant (2024) Net Institute Summer Grant (2008) Professor Livingston teaches courses in applied microeconometrics, behavioral economics, game theory, and principles of intermediate microeconomics. His work contributes to UN Sustainable Development Goals related to quality education, with research connecting economic behavior to educational outcomes and policy implications.

Karl Fristonمشاهده پروفایل

استاد

• Theoretical neurobiology
• Brain imaging
• Statistical parametric mapping
• +۱۱ مورد دیگر

Karl Friston FMedSci FRSB FRS is a Wellcome Principal Research Fellow and Scientific Director at the Wellcome Trust Centre for Neuroimaging, and Professor at the Institute of Neurology, University College London. He also serves as an Honorary Consultant at The National Hospital for Neurology and Neurosurgery, UK. Friston is a theoretical neuroscientist and authority on brain imaging who has made seminal contributions to neuroscience methodology and theory. Friston's research interests span theoretical neurobiology, computational neuroscience, and computational psychiatry. He invented statistical parametric mapping (SPM), voxel-based morphometry (VBM), and dynamic causal modelling (DCM). His most significant theoretical contribution is the free-energy principle for action and perception (active inference), which provides a unified framework for understanding brain function. His work integrates predictive coding, Bayesian inference, and information theory to explain perception, action, and learning. His research has profound implications for understanding schizophrenia through the dysconnection hypothesis, which frames psychosis as a failure of hierarchical predictive coding. Friston's publication record demonstrates consistent thematic development across decades, with recent work focusing on active inference, free-energy minimization, and computational psychiatry. His articles reveal a progression from methodological innovations in neuroimaging to comprehensive theoretical frameworks that unify perception, action, and learning under Bayesian principles. The recurring themes include hierarchical generative models, precision weighting, and the role of prediction errors in neural processing. Young Investigators Award in Human Brain Mapping (1996) Fellow of the Academy of Medical Sciences (1999) President of the international Organization of Human Brain Mapping (2000) Minerva Golden Brain Award (2003) Fellow of the Royal Society (2006) Medal, College de France (2008) Weldon Memorial prize and Medal (2013) Charles Branch Award for unparalleled breakthroughs in Brain Research (2016) Glass Brain Award - lifetime achievement award in human brain mapping (2016) As Scientific Director of the Wellcome Trust Centre for Neuroimaging, Friston leads one of the world's premier neuroimaging research centers. His theoretical work has generated numerous research programs across multiple institutions investigating active inference in perception, action, psychiatry, and even developmental biology. His frameworks have been applied to diverse areas including robotics, machine learning, and philosophical questions about consciousness and agency. Friston's work continues to shape the theoretical foundations of cognitive neuroscience through both his mathematical innovations and conceptual frameworks that unify previously disparate phenomena.

• Machine Learning
• Big Data Analytics
• Pattern Recognition
• +۷ مورد دیگر

Professor Junbin Gao is Professor of Big Data Analytics at the University of Sydney Business School. He previously served as Professor in Computing (2010-2016) and Associate Professor (2005-2010) at Charles Sturt University, and held academic positions at University of New England and University of Southampton. He is also a Member of the Sydney Southeast Asia Centre, demonstrating his regional academic engagement. Professor Gao earned his BSc and MSc from Huazhong University of Science and Technology (HUST) and PhD from Dalian University of Technology (DUT). His academic journey includes Professor at HUST (1997), Postdoctoral Research Fellow at Wuhan University (1991-1993), and multiple guest professorships at leading Chinese institutions including Wuhan University, HUST, and Beijing University of Technology. Professor Gao's research spans from approximation theory and multivariate spline functions to wavelet applications, and now focuses on machine learning and big data analytics. His recent work explores matrix neural networks, tensorial recurrent neural networks, and innovative approaches to data subspace clustering on manifolds. He integrates Riemannian geometry with machine learning to address challenges with manifold-valued data, finding applications in international relations, financial panel data, and computer vision. His publication record shows a clear progression toward increasingly sophisticated applications of machine learning in business analytics. Professor Gao has secured significant research funding including two Discovery Project grants from the Australian Research Council (ARC). His 260 academic papers and two books demonstrate substantial scholarly impact across multiple domains of data science and machine learning. Professor Gao supervises numerous research students working on cutting-edge topics including multivariate volatility forecasting, interpretable uncertainty systems, graph machine learning for consumer behavior, and deep learning for stock market prediction. His research group bridges theoretical advances with practical applications in business analytics, with particular interest in international relations research and financial panel data analysis.

Cameron Freerمشاهده پروفایل

پژوهشگر

• Probabilistic Computing
• Random Structures
• Mathematical Logic
• +۴ مورد دیگر

Cameron Freer is a Research Scientist at the Massachusetts Institute of Technology's Probabilistic Computing Project. His academic career spans multiple institutions including Keio University, Harvard University, and University of Hawaii at Manoa, with roles ranging from Postdoctoral Fellow to Project Associate Professor. Current affiliation: MIT Probabilistic Computing Project Past academic roles: Keio University SFC (2021–2024), MIT Brain and Cognitive Sciences (2013–2015), MIT Mathematics (2008–2010) Research interests focus on probabilistic computing , random structures , and their intersections with logic, mathematics, and artificial intelligence. Key contributions include foundational work on Markov categories , graphons , and feedback computability . Recent publications explore probabilistic programming systems , random graph modeling , and computable aspects of measure theory . Collaborators include prominent researchers from Harvard, Oxford, and MIT. Professional engagements include committee memberships in major conferences like POPL (2024), LAFI (2024–2025), and PLDI (2024). Holds PhD in Mathematics from Harvard University (2008).