جست‌وجوی استاد و پژوهشگر

• Neurosciences
• Artificial intelligence
• Machine learning
• +۴ مورد دیگر

Professor Janaina Mourao-Miranda is a Professorial Research Associate in the Department of Computer Science at University College London's Faculty of Engineering Sciences. She is affiliated with the Centre for Medical Image Computing (CMIC) and leads the Machine Learning and Neuroimaging Lab (MLNL), accessible at http://www.mlnl.cs.ucl.ac.uk. Her research program focuses on developing and applying machine-learning models to investigate complex relationships between neuroimaging data and multidimensional descriptions of mental health disorders. Specifically, she explores whether we can learn about underlying brain mechanisms of mental disorders from these relationships, better stratify patient groups based on these relationships, and combine information from clinical assessments with different neuroimaging modalities to build better diagnostic and prognostic models of mental health disorders. Professor Mourao-Miranda's work spans multiple disciplines including Neurosciences, Artificial Intelligence, Machine Learning, Computational Neuroscience, Biological Psychology, Clinical and Health Psychology, and Pattern Recognition. Her recent publications demonstrate a strong emphasis on multivariate analysis of brain imaging data, advanced techniques for handling incomplete datasets, and developing interpretable machine learning models specifically designed for clinical applications in mental healthcare. Her key scientific contributions include: Developing novel machine learning approaches specifically tailored for neuroimaging analysis Investigating fundamental brain-behavior relationships in mental health disorders Creating innovative methods for patient stratification based on multimodal data integration Advancing statistical techniques for handling incomplete clinical datasets Pioneering interpretable AI models that bridge the gap between computational methods and clinical practice Professor Mourao-Miranda teaches a specialized module on Applied Artificial Intelligence for the MSc programs in AI for Biomedicine and Healthcare and AI for Sustainable Development at UCL, where she shares her expertise in applying artificial intelligence to solve real-world healthcare challenges and contribute to sustainable development goals.

Mathias Sablé-Meyerمشاهده پروفایل

پژوهشگر ارشد

• Mechanistic implementation of compositional mental representation
• Geometry perception in humans and non-human primates
• Human reasoning and cognitive fallacies
• +۵ مورد دیگر

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.

Nuttida Rungratsameetaweemanaمشاهده پروفایل

استادیار

• Neural computations
• Cognitive neuroscience
• Computational neuroscience
• +۶ مورد دیگر

Nuttida Rungratsameetaweemana is an Assistant Professor in the Department of Biomedical Engineering at Columbia University's Fu Foundation School of Engineering and Applied Science. She also serves as a Provost Research Fellow at Columbia University, demonstrating her significant research contributions early in her academic career. NuttidaLab, her research group, focuses on integrating computational and experimental approaches to investigate the neural computations underlying complex cognitive functions in both health and disease. The lab's research spans several key areas including learning mechanisms, decision-making processes, and social behavior, with particular emphasis on understanding how these cognitive functions become disrupted in neurological and neuropsychiatric disorders. The lab has developed several important computational neuroscience tools including NeuralDecoder for representational similarity analysis, state_space_analysis for modeling neural dynamics, MARLAX for multi-agent reinforcement learning, and dynamax for state space modeling. These tools reflect the lab's commitment to developing robust analytical frameworks that can be widely adopted by the neuroscience community. NuttidaLab maintains an active GitHub presence with multiple repositories that demonstrate the lab's focus on open science and reproducible research. The lab's work combines cutting-edge computational approaches with experimental neuroscience to develop a deeper understanding of brain function. The lab actively seeks talented students and researchers interested in interdisciplinary work at the intersection of engineering, neuroscience, and computational science. Professor Rungratsameetaweemana's research represents an important bridge between theoretical computational models and experimental neuroscience, with significant potential for advancing our understanding of both healthy brain function and neurological disorders.

Stuart Jonathan Russellمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Probabilistic Reasoning
• +۷ مورد دیگر

Stuart Jonathan Russell is a Distinguished Professor of Computer Science, Cognitive Science, and Computational Precision Health at the University of California, Berkeley. He holds the Smith-Zadeh Chair in Engineering and is also a Professor of Computational Precision Health at the University of California, San Francisco. Russell is the founder and leader of the Center for Human-Compatible Artificial Intelligence (CHAI) at UC Berkeley and serves as an Honorary Fellow of Wadham College, Oxford. His academic journey began with a B.A. in Physics from the University of Oxford, followed by a Ph.D. in Computer Science from Stanford University. Throughout his distinguished career, Russell has received numerous prestigious honors including the IJCAI Computers and Thought Award (1995), IJCAI Award for Research Excellence (2022), Fellow of the Royal Society (2025), and Member of the National Academy of Engineering (2025). Russell's research spans multiple domains within artificial intelligence, with a recent focus on ensuring AI systems remain beneficial to humanity. His work includes significant contributions to machine learning, probabilistic reasoning, knowledge representation, planning, real-time decision making, and inverse reinforcement learning. In recent years, his research has increasingly focused on AI safety, value alignment, and developing frameworks for human-compatible AI systems that maintain human control as AI capabilities advance. His publication record shows a clear trend toward addressing the long-term challenges of AI development, particularly the control problem and value alignment. The research spans theoretical foundations of AI, practical applications in robotics and decision making, and critical examinations of the societal implications of increasingly capable AI systems. Russell's work has evolved from foundational AI research to increasingly focus on the alignment problem and mechanisms for ensuring AI systems remain beneficial. Russell has received numerous scientific awards recognizing his contributions to the field: IJCAI Computers and Thought Award (1995) AAAI Fellow (1997) ACM Fellow (2003) AAAS Fellow (2011) Blaise Pascal Chair (2012) Reith Lectures (2021) Officer of the Order of the British Empire (OBE) (2021) Fellow of the Royal Society (2025) Member of the National Academy of Engineering (2025) Russell has advised numerous doctoral students including Marie desJardins, Eric Xing, and Shlomo Zilberstein, and has mentored many postdoctoral researchers who have become leaders in the field. His research has been supported by various grants from organizations including the National Science Foundation, Defense Advanced Research Projects Agency, and other funding bodies focused on advancing AI research with careful consideration of safety and societal impact. He has been particularly active in securing funding for research on human-compatible AI and value alignment. He founded and leads the Center for Human-Compatible Artificial Intelligence (CHAI), which brings together researchers from multiple disciplines to address the challenge of creating AI systems that reliably do what humans want them to do. The center collaborates with other research groups including the Berkeley Artificial Intelligence Research (BAIR) lab, the Kavli Center for Ethics, Science, and the Public (KCESP), and the Institute for Cognitive and Brain Sciences (ICBS), creating a rich interdisciplinary environment for addressing the challenges of AI safety and human compatibility.

• Cognitive Science
• Behavioral Economics
• Decision Making
• +۴ مورد دیگر

Professor Nick Chater serves as Professor of Behavioural Science within the Behavioural Sciences Group at Warwick Business School (WBS), University of Warwick. His academic profile spans cognitive science, behavioral economics, and decision-making psychology, with significant contributions to understanding the fundamental mechanisms of human thought and behavior. Chater's research interests encompass cognitive science, behavioral economics, and the psychology of decision-making. He has pioneered work challenging traditional views of deep, unconscious mental processes, most notably through his influential 2018 book "The Mind is Flat: The Remarkable Shallowness of the Improvising Brain." His research program investigates how people process information, make decisions under uncertainty, and develop language and social coordination mechanisms. Key themes include the role of noise in cognition, Bayesian approaches to modeling human thought, and the application of behavioral insights to public policy design. Chater's extensive publication record reveals a strong trend toward interdisciplinary integration of computational modeling with empirical research. His recent work demonstrates consistent engagement with cutting-edge developments across cognitive science, particularly in Bayesian cognitive modeling and behavioral public policy. The S-Frame agenda for behavioral public policy research represents a systematic approach to translating behavioral insights into policy design. His collaborative network spans psychology, economics, linguistics, and artificial intelligence, reflecting the interdisciplinary nature of modern cognitive science. As an educator, Chater teaches "Behavioural Sciences for the Manager" for Executive MBA programs and "Judgement and Decision Making" for various MSc programs at Warwick Business School. His teaching focuses on applying behavioral science principles to business contexts, helping students understand how cognitive biases and heuristics influence managerial decision-making. His work on coordination in dynamic interactions and moral cognition provides valuable insights for understanding organizational behavior and team dynamics.