Dr. George Cantwell is an Assistant Professor in the Department of Engineering at the University of Cambridge, affiliated with Cambridge Infectious Diseases. He specializes in computational methods for inference problems, particularly in disease spreading across networks. Education: PhD in Physics from the University of Michigan; postdoctoral fellowship at the Santa Fe Institute His research focuses on network science , complex systems , and statistical inference , with an emphasis on computational approaches. His work spans theoretical and applied domains, including: Message passing algorithms for heterogeneous networks Bias correction in social network analysis (friendship paradox) Statistical inference of network structure from noisy data Modeling judicial voting behavior through network interactions Computational cognitive neuroscience of category learning Recent publications highlight interdisciplinary applications in epidemiology, physics, and cognitive science. He actively mentors students in networks, complex systems, and statistical inference.
Tianxi Li is an Assistant Professor in the Department of Statistics at the University of Minnesota, Twin Cities, within the College of Science and Engineering. Their research integrates statistical methodology with applications in network science, data privacy, and biomedical data analysis. Their research interests lie at the intersection of statistics and network science, focusing on statistical modeling of complex networks , data privacy , network security , and biomedical applications such as neuroimaging and genomics. They develop adaptive and scalable methods for network estimation, community detection, and differential correlation analysis. The recent publications demonstrate a consistent focus on advancing statistical tools for network-structured data, with increasing applications in neuroscience and cancer genomics. The work spans theoretical development (e.g., network growth models) and practical applications (e.g., glioblastoma gene modules), reflecting a balance between methodology and real-world impact. Tianxi Li leads an active research program funded by the National Science Foundation, indicating recognition and support for their innovative work. Principal Investigator, Statistical tools for network security protection: from data privacy to threat detection , NSF (2024–2025) They advise graduate students in statistics and data science, though specific advisees are not listed. Their collaborative network includes researchers in biostatistics, computer science, and machine learning, as evidenced by co-authorships and interdisciplinary projects. Li's work contributes to the UN Sustainable Development Goals, particularly through advancements in data-driven solutions for secure and ethical data analysis.
Maurice Smith serves as the Gordon McKay Professor of Bioengineering at Harvard University's School of Engineering and Applied Sciences (SEAS), where he leads the Neuromotor Control Lab. His primary appointment resides within the Department of Bioengineering, focusing on the computational and neural mechanisms underlying human movement control. Smith's research centers on sensorimotor learning , motor adaptation , and neuromotor control systems . He investigates how the brain forms and retains motor memories, particularly examining cerebellar contributions to long-term sensorimotor memory and the dissociation between implicit and explicit learning pathways. His work frequently employs computational modeling to dissect neural tuning properties and motor variability regulation. Analysis of his recent publications reveals a strong emphasis on temporal dynamics in motor learning , cerebellar function in memory consolidation , and Bayesian frameworks for understanding sensorimotor adaptation . His research demonstrates consistent focus on how error processing, uncertainty, and neural plasticity shape motor memory formation across multiple timescales. Smith maintains active collaborations with researchers including Wilsaan M. Joiner, Yohsuke R. Miyamoto, and Nathan Sandholtz, as evidenced by frequent co-authorship patterns. His laboratory investigates fundamental questions in motor control with implications for neurorehabilitation and adaptive robotics.
Rishidev Chaudhuri is an Associate Professor at the University of California, Davis in the Department of Neurobiology, Physiology and Behavior within the College of Biological Sciences. His research focuses on computational neuroscience and neural dynamics, employing mathematical models to investigate how neural circuits generate cognitive processes such as memory, perception, and decision-making. His work explores neural dynamics through models of memory systems, attentional mechanisms, and probabilistic inference. Recent publications highlight advances in understanding hippocampal memory scaffolds, parietal-frontal interactions, and neuromorphic computing inspired by brain architecture. Education: BA in Physics (Amherst College), PhD in Applied Mathematics (Yale University) Centers: Center for Neuroscience; affiliated with Applied Mathematics and Neuroscience Graduate Groups Scientific awards and honors are not explicitly mentioned in the provided materials.
Mikkel N. Schmidt is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His research focuses on statistical modeling, Bayesian methods, and their applications in science and industry. He has held visiting roles at Columbia University (2007) and Cambridge University (2008-2009). His work integrates probabilistic modeling with computational inference to address complex problems in diverse fields such as molecular discovery, optical communication, and brain connectivity analysis. Education highlights include visiting scholar and postdoctoral experiences at top-tier institutions. Research interests span statistical methodology development, machine learning applications, and interdisciplinary problem-solving. Current projects involve Bayesian neural networks for molecular discovery and federated learning optimization. Advising efforts include supervising multiple PhD students in areas like molecular discovery and denoising diffusion models. Notable collaborations involve work on materials science, quantum communication, and medical signal processing. His contributions bridge theoretical advancements with practical industrial applications, emphasizing interdisciplinary innovation.
Luigi Acerbi is an Associate Professor in the Department of Computer Science at the University of Helsinki, where he leads the Machine and Human Intelligence research group. He is also an active member of the Finnish Center for Artificial Intelligence (FCAI) and ELLIS (European Laboratory for Learning and Intelligent Systems). His research focuses on probabilistic machine learning and computational neuroscience, particularly on developing efficient methods for statistical inference, Bayesian models of perception, and resource-constrained rationality. His work bridges machine learning and cognitive science, with applications in Bayesian optimization, simulation-based inference, and image completion. The recent publications highlight a strong trend toward unifying probabilistic conditioning across diverse tasks using transformer-based meta-learning frameworks like the Amortized Conditioning Engine (ACE). These works emphasize amortized inference, flexible latent variable modeling, and the integration of prior knowledge at runtime, enabling efficient and scalable Bayesian methods for complex problems. Scientific Affiliations: University of Helsinki, Department of Computer Science Finnish Center for Artificial Intelligence (FCAI) ELLIS (European Laboratory for Learning and Intelligent Systems) Education: PhD in Computational Neuroscience, Doctoral Training Centre, Edinburgh, UK Advisor: Sethu Vijayakumar and Daniel Wolpert Visiting work at Computational and Biological Learning Lab, Cambridge Postdoctoral Experience: Alex Pouget’s lab, University of Geneva, Switzerland Wei Ji Ma, New York University, USA Collaboration with the International Brain Laboratory Luigi Acerbi mentors PhD students including Daolang Huang and Nasrulloh Loka, and collaborates widely with researchers such as Samuel Kaski. He has contributed to open-source tools like PyVBMC and is involved in community initiatives such as the EurIPS conference. His work is supported by grants from the Research Council of Finland, Business Finland, and the UKRI Turing AI World-Leading Researcher Fellowship. He leads a research lab focused on amortized probabilistic inference, with ongoing projects including PriorGuide and Stacked VBMC, aiming to make Bayesian methods more practical and accessible for real-world scientific and engineering applications.
Robin A. Murphy is a Professor of Experimental Psychology at the University of Oxford and a Fellow and Tutor for Admissions at Corpus Christi College. He holds a PhD from McGill University (Canada) and has contributed over 50 scientific publications on associative learning mechanisms, their role in mental disorders like depression and psychopathy, and translational neuroscientific applications. His research bridges animal and human learning, with a focus on computational psychopathology and neurochemical modulation (e.g., serotonin). Education: Undergraduate degree, Queen's University MA and PhD in Psychology, McGill University Research Interests: Dr. Murphy investigates how associative learning processes underpin mental disorders, particularly through neurochemical systems like serotonin. His work explores causal reasoning biases in depression, psychopathy's learning deficits, and translational models linking animal studies to human cognition. Key themes include agency perception, contingency judgment, and the neural substrates of prejudice. Recent projects involve peptide research in aging and computational modeling of multi-agent systems in depression. Grants and Funding: Supported by MRC, BBSRC, Wellcome Trust, and Japan Society for the Promotion of Science. He has advised UK Research Councils and NSERC (Canada). Labs and Teams: Leads a lab focusing on associative learning mechanisms, collaborating with institutions globally on computational psychopathology and translational neuroscience.
Prof Raphaël Phan is a Professor and Deputy Head of the School of IT at Monash University Malaysia. His expertise spans security, cryptography, malicious AI, emotion recognition, motion analysis, and generative AI. He has published over 220 papers and led significant projects including privacy-preserving data mining funded by UK MoD and Malaysian government grants exceeding RM4 million. He co-designed the BLAKE hash function (SHA-3 finalist) and has an h-index of 50. Education: PhD in Cryptography (Multimedia University, 2005), MEngSci (2001), BEng (Hons) Computer Engineering (1999). Research focuses on adversarial AI, brain networks, and secure systems. Current projects include Æmbience: emotion-aware virtual assistants using motion magnification. Supervised 15 PhD graduates and 19 current students. Professional affiliations: Chartered Engineer (IET, UK), HEA Fellow, Board of Engineers Malaysia. Recent work emphasizes causal bias detection in micro-expressions, brain tumor detection via advanced YOLOv8, and generative adversarial networks for medical imaging. His work bridges cybersecurity with neuroscience applications.
Jerome Busemeyer is a Distinguished Professor and Provost Professor in the Department of Psychological and Brain Sciences at Indiana University Bloomington, with a joint affiliation in the Cognitive Science Program. He is a Fellow of the American Academy of Arts and Sciences, reflecting his significant contributions to the science of decision-making and cognitive modeling. His educational background includes a Ph.D. in Psychology from the University of South Carolina (1979), an M.A. from the same institution (1976), and a B.A. cum laude from the University of Cincinnati (1973). He completed postdoctoral training in Quantitative Methods at the University of Illinois in 1980. Busemeyer's research focuses on mathematical and computational models of human judgment and decision-making , with pioneering work in quantum cognition , dynamic models , and neural network modeling . He developed Decision Field Theory , a dynamic cognitive model of decision processes, and has led the application of quantum probability to explain cognitive 'errors' such as order effects and violations of rational decision rules. His recent publications reveal a consistent trend toward integrating quantum-theoretic frameworks with empirical decision research, exploring topics such as pricing behavior, confidence judgments, multi-attribute decisions, and medical decision-making. These works appear in top journals including Psychological Review , Trends in Cognitive Sciences , and Proceedings of the National Academy of Sciences , indicating sustained high-impact contributions. Fellow, American Academy of Arts and Sciences Distinguished Professor, Indiana University Provost Professor, Indiana University Busemeyer has advised numerous graduate students and postdoctoral researchers, many of whom have become active collaborators and co-authors. His lab, the Judgment and Decision-Making Laboratory (Decision Research Laboratory), supports interdisciplinary research in cognitive science and receives ongoing funding for theoretical and experimental work. He has authored influential books, including Quantum Models of Cognition and Decision and Cognitive Modeling . The lab, located in Room A300G of the Psychology Building, fosters collaborative research on decision-making, quantum cognition, and computational modeling, with active projects involving fMRI, behavioral experiments, and theoretical development.
Dr. David Sewell is a Senior Lecturer and Deputy Head of School (Teaching & Learning) at the School of Psychology, The University of Queensland. His research focuses on attention, learning, memory, and decision-making, with a strong emphasis on formal mathematical models of human cognition. He is affiliated with the Centre for Perception and Cognitive Neuroscience within the Faculty of Health, Medicine and Behavioural Sciences. Education: Bachelor (Honours) of Arts and Doctor of Philosophy, both from the University of Western Australia. David's research explores the intersection of cognitive psychology and computational modeling. Key areas include perceptual decision-making, attentional mechanisms, and the application of diffusion models to understand cognitive processes. His work also extends to sustainability and collective self-regulation through cognitive frameworks. The 15 most recent articles highlight his contributions to modeling decision thresholds in memory prioritization, analyzing gaze cueing effects, and investigating neural correlates of confidence in multisensory decisions. Collaborative projects frequently involve interdisciplinary approaches, combining neuroscience, psychology, and computational methods. He has supervised multiple PhD candidates, serving as Principal or Associate Advisor, with research topics ranging from visual categorization to metacognition in children. Current and past funding includes ARC Discovery Projects on collective self-regulation and category learning constraints.
Professor Nir Oren is a faculty member at the School of Natural and Computing Sciences , University of Aberdeen. His research focuses on multi-agent systems , formal argumentation , computational trust theory , and norm-based reasoning . He currently supervises PhD students in Computing Science and serves as Dean for Research Performance. Research Specialisms: Artificial Intelligence, Operational Research Contact: n.oren@abdn.ac.uk Research Trends (2022–2025): Nir Oren's publications span argumentation theory , BDI agent modeling , resilience in autonomous systems , and human-machine collaboration . His recent work addresses responsibility-aware AI , medical explainability , and environmental sensor networks . Key methods include probabilistic reasoning , game theory , and logical formalisms .
Xiaofeng Liu is an Assistant Professor at Yale University School of Medicine in the Departments of Radiology & Biomedical Imaging and Biomedical Informatics & Data Science. He is also an Associate Member at the Broad Institute of MIT and Harvard. Previously, he held faculty positions at Harvard Medical School and research roles at Massachusetts General Hospital and Beth Israel Deaconess Medical Center. PhD in Mechatronics from University of Chinese Academy of Sciences Dual Bachelor's degrees in Automation (Wang-Daheng Elite Class) and Communication from University of Science and Technology of China His research integrates trustworthy AI, medical imaging, and data science to improve diagnosis, prognosis, and treatment monitoring for neurological disorders, cancer, and cardiovascular diseases. Key focus areas include domain adaptation techniques, diffusion models, and interpretable AI systems. Led special issues in IEEE Transactions on Pattern Analysis and Medical Image Analysis Developed novel frameworks like Ordinal UDA and Memory-Consistent Adaptation Scientific accolades include the Trailblazer R21 Award (NIBIB), OpenAI Research Award, and National Artificial Intelligence Research Resource Pilot Award. He serves as Associate Editor for IEEE Transactions on Neural Networks and Learning Systems and actively contributes to MICCAI and NIH review panels. His lab at Yale (XLiu Lab) investigates neural basis of intelligence to inspire AI development, with applications in brain tumor segmentation, cardiac imaging, and cross-modal medical diagnostics.
Irina Rish is a Full Professor at the Université de Montréal and a core academic member of Mila – Quebec Artificial Intelligence Institute, where she leads the Autonomous AI Lab. She holds a Canada Excellence Research Chair (CERC) and a CIFAR AI Chair, reflecting her leadership in foundational AI research. Her work is supported by major initiatives, including the U.S. Department of Energy’s INCITE project on Summit and Frontier supercomputers. PhD in AI, University of California, Irvine MSc in AI, University of California, Irvine MSc in Applied Mathematics, Moscow Gubkin Institute Her research focuses on machine learning, neural scaling laws, emergent behaviors in foundation models, continual learning, robustness, and neuroscience-inspired AI . She explores how AI systems can become more general, flexible, and aligned with human cognition. Her recent work investigates training dynamics in large language models, efficient pruning techniques, and the development of time-series foundation models. The analysis of her recent publications reveals a strong focus on scaling behaviors, continual adaptation, and robustness in AI systems . Her work spans theoretical understanding of training dynamics (e.g., zero-sum learning), practical optimization methods, and applications in climate modeling and mental health. She emphasizes open science, leading open-source projects and co-founding Nolano.ai to build efficient, compressed foundation models. Canada Excellence Research Chair (CERC) CIFAR AI Chair IBM Eminence & Excellence Award (2018) IBM Outstanding Innovation Award (2018) IBM Outstanding Technical Achievement Award (2017) IBM Research Accomplishment Award (2009) Irina Rish advises a large group of PhD and Master’s students across Université de Montréal, McGill, and Concordia. She leads major research grants and collaborates internationally on HPC-based AI research. She is also the co-founder and CSO of Nolano.ai, driving innovation in efficient AI systems. She leads the Autonomous AI Lab, which focuses on building large-scale foundation models, understanding neural scaling laws, and developing bio-inspired learning systems. She actively organizes reading groups on scaling, continual learning, and out-of-distribution generalization, fostering a collaborative research environment.
Zoe Drayson is an Associate Professor in the Department of Philosophy at the University of California, Davis, and serves as the Director of the Cognitive Science Program. She holds a Ph.D. in Philosophy from the University of Bristol (2011), an M.Sc. in Philosophy of Mental Disorder from King’s College London (2005), and an M.A. in Philosophy from the University of Edinburgh (2000) with First Class Honours. Research Focus: Drayson's work bridges philosophy and the mind-brain sciences, emphasizing the nature of psychological explanation and the relationship between scientific and philosophical approaches to the mind. Her research spans: Representation in cognitive science Perception-action interplay Metaphysics and epistemology of mental states Explanatory virtues in cognitive theories Challenging radical conclusions in cognitive science Recent Publications: Focus on predictive processing, extended cognition, implicit knowledge, and Bayesian modeling. Themes include theoretical integration, philosophical naturalism, and the limits of empirical reductionism. Scientific Awards: Center for Philosophy of Science Visiting Research Fellowship (2015) with $6,000 stipend University of Stirling Impact Fellowship (2013) with £10,000 research grant Arts and Humanities Research Council Doctoral Award (£45,000) (2007) AHRC Masters for Research Preparation Award (£13,000) (2004) Teaching: Drayson teaches courses in philosophy of mind, cognitive science, and evolution of mind at UC Davis, including graduate-level philosophy of psychology and the interdisciplinary Cognitive Science major curriculum.
David Klindt is Assistant Professor at Cold Spring Harbor Laboratory, leading research at the intersection of biological systems and artificial intelligence. His lab investigates how brains process sensory information and generalize knowledge across contexts, studying neural representations to inspire robust AI models. Research combines computational neuroscience and machine learning to develop algorithms mimicking biological learning efficiency. Current projects examine latent computing in biological neural networks through dynamical systems frameworks, sparse coding principles in neural representations, and geometric organization in visual processing. His group develops methods for mechanistic interpretability, self-supervised learning identifiability, and compute-efficient inference. Recent publications analyze toroidal representations in grid cells, retinal feature detection, and Cryo-EM structure disentanglement. Dr. Klindt's work has been recognized through publications in Nature Communications, eLife, and NeurIPS. Before joining CSHL, he was a Machine Learning Research Scientist at Meta Reality Labs and postdoctoral researcher at Stanford University and NTNU. He holds a Ph.D. in Computational Neuroscience and Machine Learning from the University of Tübingen.
