Heng Huang is the Brendan Iribe Endowed Professor in the Department of Computer Science and the Department of Electrical and Computer Engineering at the University of Maryland, College Park . He earned his Ph.D. in Computer Science from Dartmouth College and holds prior degrees from Shanghai Jiao Tong University. His research focuses on advancing the foundations and applications of artificial intelligence, particularly in machine learning, data mining, natural language processing, computer vision, and biomedical informatics . His work integrates large-scale optimization, fairness, and robustness in deep learning systems. Heng Huang’s recent publications demonstrate a strong trend in large language models, federated learning, model watermarking, continual learning, and medical image analysis . His work appears consistently in top venues like NeurIPS, ICML, CVPR, ICLR, and MICCAI, reflecting a broad impact across theoretical and applied AI. He actively mentors students and postdocs, seeking highly motivated researchers in machine learning and related domains. His work has significant implications for healthcare, privacy, and trustworthy AI.
Tae-Ho Lee is an Associate Professor in the Department of Psychology at Virginia Tech, with affiliated appointments in the School of Neuroscience and Translational Biology, Medicine, and Health. His research focuses on affective and cognitive neural development across the lifespan, neurodegeneration, and family-based neural dynamics. PhD in Brain and Cognitive Science, University of Southern California M.A. in Clinical Psychology, Korea University B.A. in Psychology, Korea University Dr. Lee’s work explores brain connectome dynamics, dyadic neural concordance in families, and age-related attentional control. He employs neuroimaging techniques to study how familial and environmental factors shape emotional and cognitive outcomes in adolescents and older adults. Recent publications highlight trends in longitudinal studies , parent-child neural similarity , and functional connectivity in emotion regulation . Key areas include autism spectrum disorder, substance misuse risk, and the role of socioeconomic factors in brain development. Rising Star , Association for Psychological Science (2020) Dr. Lee is not currently accepting students and leads the Affective Neurodynamics and Development (AND) Lab at Virginia Tech.
Eric Shea-Brown is a Professor in the Department of Applied Mathematics at the University of Washington, part of the College of Arts & Sciences. He holds adjunct roles in the Department of Physiology and Biophysics and is an affiliate investigator at the Allen Institute for Brain Science. His research focuses on the nonlinear dynamics of neural networks, decision-making processes in neural circuits, and the interplay between neural encoding and sensory information. He collaborates extensively with experimentalists and theorists across institutions, leveraging tools from dynamical systems, stochastic processes, and information theory. Education: Bachelor's in Engineering Physics, UC Berkeley Ph.D. in Applied and Computational Mathematics, Princeton University Eric's work is supported by the Burroughs-Wellcome Fund, NSF, NIH, and the Simons Foundation. His lab emphasizes equity and inclusion and is part of the UW Computational Neuroscience Center. He has held postdoctoral positions at NYU’s Courant Institute and has affiliations with the Center for Sensorimotor Neural Engineering and the UW Institute for Neuroengineering. Research highlights include studies on neural decision-making dynamics, spike coordination mechanisms, graph-theoretic approaches to neural connectomics, and chaos in spiking circuits. His group collaborates with UW neuroscience departments and the Allen Institute to analyze emerging neural datasets.
Professor Guy Williams is a leading academic at the University of Cambridge with a focus on imaging science and clinical neurosciences, affiliated with Downing College and the Wolfson Brain Imaging Centre . Holding a PhD in Physics from his initial Natural Sciences degree, he specializes in nuclear magnetic resonance (NMR) and MRI techniques for brain imaging. Education: BA, PhD in Physics His research centers on non-invasive imaging of brain structure and function, particularly in traumatic brain injury (TBI) and dementia. His work involves developing novel MRI pulse sequences and advanced data analysis algorithms, including AI-based diagnostic tools. He leads studies on white matter integrity post-trauma, longitudinal dementia assessment, and applications of MRI in disorders of consciousness and addiction. Recent publications highlight collaborations in traumatic brain injury outcomes, AI-guided dementia prediction, and neuroimaging of post-COVID cognitive deficits. His team's work on ultra-high field laminar fMRI and distortion correction methods has advanced clinical neuroscience applications. Key techniques include diffusion tensor imaging (DTI), 7 Tesla MRI, and positron emission tomography (PET/MR). His research spans from basic NMR physics to clinical translation, with a strong emphasis on multi-site studies and real-world diagnostic implementation.
Claire Donnat is an Assistant Professor in the Department of Statistics at the University of Chicago, specializing in statistical and machine learning methods for graph-structured and high-dimensional data. Her work bridges theoretical innovation with applications in biomedical research, environmental science, and public health. Education: B.S. and M.S. in Applied Mathematics from Ecole Polytechnique; Ph.D. in Statistics from Stanford University (2020). Her research focuses on three methodological directions: (1) statistical foundations for graph neural networks (GNNs), (2) structured estimation with graph constraints, and (3) multimodal data integration with uncertainty quantification. Key applications include thermotolerance in photosynthetic microbes, family network analysis for child welfare, and spatial transcriptomics. The 15 most recent publications highlight her work in GNNs, CCA, tensor modeling, and epidemic analysis, with keywords spanning statistics, machine learning, and network science. Her methodological contributions address challenges in sparsity, graph topology, and heterogeneous data fusion. Scientific Awards: Facebook Research Award (2021), C3.AI COVID Grand Challenge winner (2020), Lumiata hackathon winner (2020), Stanford Centennial Award (2019), and others. Claire's research group actively recruits postdocs and students for projects involving graph-based modeling, data integration, and biomedical applications. She also provides research consulting in statistical methodology and graph modeling for life sciences.
Yiyu Yao is a Professor in the Department of Computer Science at the University of Regina, Faculty of Science. He holds a B.Eng. from Xi'an Jiaotong University and earned both his M.Sc. and Ph.D. from the University of Regina. His office is located in College West 308.6, and he can be reached at Yiyu.Yao@uregina.ca or by phone at (306) 585-5226. Dr. Yao's research spans multiple interconnected domains in intelligent systems. His primary focus is on three-way decisions, which serves as a unifying framework for his work in granular computing, rough sets, and decision-theoretic models. He has developed significant theoretical contributions to decision-theoretic rough sets (DTRS) and probabilistic rough sets, creating bridges between uncertainty management and practical decision-making applications. His work extends to web intelligence, information retrieval systems, and multiview data analysis, where he applies his theoretical frameworks to real-world problems in data science and artificial intelligence. Analysis of Dr. Yao's recent publications reveals a strong continuing focus on three-way decision theory, with increasing applications across diverse domains. His work demonstrates evolution from foundational theoretical contributions to sophisticated applications in multi-criteria decision making, conflict analysis, and explainable AI. The research shows integration of granular computing principles with modern machine learning techniques, particularly in handling uncertainty and developing interpretable models. Recent publications indicate growing interest in the intersection of three-way decisions with fuzzy sets, shadowed sets, and cognitive approaches to data analysis. Dr. Yao has mentored numerous graduate students and has hosted many visiting scholars, primarily from Chinese institutions including Nanjing University Posts and Telecommunication, Harbin Normal University, Shaanxi Normal University, and others. He serves as Area Editor on Rough Sets for the International Journal of Approximate Reasoning and as Associate Editor for Information Sciences. He is also Associate Editor-in-Chief for the Journal of Emerging Technologies in Web Intelligence and serves on multiple editorial boards including LNCS Transactions on Rough Sets and Web Intelligence and Agent Systems. He has organized significant conferences including the International Joint Conference on Rough Sets (IJCRS 2017) and served on the Steering Committee for the International Symposium on Fuzzy and Rough Sets.
Guillaume Lajoie is an Associate Professor in the Department of Mathematics and Statistics at Université de Montréal and a Core Academic Member of Mila – Quebec Artificial Intelligence Institute. He holds a Canada CIFAR AI Research Chair and a Canada Research Chair in Neural Computation and Interfacing. His research focuses on the intersection of AI and neuroscience, particularly in understanding neural network dynamics and developing brain-machine interfaces for clinical and scientific applications. He is affiliated with the Centre de recherches mathématiques (CRM), the Interdisciplinary Center for Research on the Brain and Learning (CIRCA), and the UNIQUE initiative. Education: PhD in Applied Mathematics from the University of Washington (Seattle), postdoctoral fellowships at the Max Planck Institute for Dynamics and the University of Washington Institute for Neuroengineering. Awards include the FRQS Scholar designation and leadership roles in strategic research initiatives like UNIQUE and CIRCA. Research interests include neural computations, recurrent neural networks, neurotechnology, and responsible AI development. Supervised students include François Paugam (PhD), Giancarlo Kerg (PhD), and others. Key grants include projects on adaptive neuroprosthetics, neural decoding, and Canada Research Chairs funding.
Evelyn Lake is an Associate Professor in the Department of Radiology and Biomedical Imaging at Yale University, with affiliations to the Wu Tsai Institute and Yale Biomedical Imaging Institute. Her research focuses on functional neuroimaging, particularly using rodent models to study brain connectivity, neurovascular coupling, and disorders like Alzheimer’s disease and autism spectrum disorder. PhD in Medical Biophysics, University of Toronto (2016) Postdoctoral Associate, Yale University (2019) BSc (Honors) in Biophysics, University of Guelph (2010) Her work spans multimodal imaging techniques (e.g., simultaneous Ca2+ and fMRI), examining how genetic mutations (e.g., PTEN, KATNAL2) affect cerebrospinal fluid dynamics and neuronal connectivity. She investigates longitudinal changes in Alzheimer’s models and methodological aspects of animal neuroimaging, such as sample size optimization and awake imaging protocols. Recent publications highlight her contributions to understanding autism-related white matter abnormalities, transdiagnostic connectome predictive modeling, and the impact of neurovascular uncoupling in awake mice. Collaborations include Todd Constable, Francesca Mandino, Xilin Shen, and Xenophon Papademetris.
Giorgio Ascoli is a University Professor in the Department of Bioengineering at George Mason University, where he has been since 1997. He is the Founding Director of the Center for Neural Informatics, Structures, & Plasticity (CN3) and Founding Editor-in-Chief of the journal Neuroinformatics . His affiliations span computational neuroanatomy, neuroinformatics, and hippocampal modeling. Education : PhD in Biochemistry and Neuroscience (1996), Scuola Normale Superiore; MS in Chemistry and Biochemistry (1993), Pisa University; BS in Chemistry and Physics (1991), Scuola Normale Superiore. Dr. Ascoli investigates the relationship between brain structure, activity, and function from cellular to circuit levels. His research focuses on anatomically plausible neural networks to model mammalian brains, particularly the hippocampus, with implications for understanding human memory and consciousness . He pioneered computational neuroanatomy, developing tools like L-Neuron for neuronal shape modeling and curating NeuroMorpho.Org , a central repository for digitally reconstructed neurons. His recent publications emphasize neuronal classification , connectome analysis , and biologically informed machine learning . Awards include the 2012 Outstanding Faculty Award (Virginia), 2022 AIMBE fellowship , and 2023 Presidential Faculty Excellence Awards . He has mentored over 20 graduate students and postdoctoral fellows, with funding from NIH, NSF, DARPA, and private foundations. Scientific Contributions : Over 137 peer-reviewed articles, 4 patents, 2 authored/edited books, and leadership in NeuroMorpho.Org and Hippocampome. Grants : $20M+ in cumulative funding, including NIH R01s, NSF BRAIN EAGERs, and Burroughs-Wellcome Trust support. Labs : Leads the Computational Neuroanatomy Group within CN3, focusing on hippocampal modeling, neuronal morphology, and consciousness theories.
Duncan Astle is the Gnodde Goldman Sachs Professor of Neuroinformatics at the Department of Psychiatry, University of Cambridge. He serves as a Programme Leader at the Medical Research Council's Cognition and Brain Sciences Unit (MRC CBU) and is a Fellow of Robinson College. Astle heads the 4D Lab (Development, Dynamics, Disorders, Data Science), which provides a research home for approximately 15 Early Career Researchers working at the intersection of developmental cognitive neuroscience and advanced data science methodologies. Astle's research focuses on understanding childhood development through innovative analytical approaches. His work employs transdiagnostic methods to study children with attention, learning, and memory difficulties, moving beyond traditional diagnostic categories. He investigates how neural systems develop in childhood, how they relate to developmental disorders, and how they respond to intervention. His research integrates network science, machine learning, and generative modeling to capture the complexity of neurodevelopmental diversity, examining how cognitive skills, literacy, numeracy, and mental health interrelate over developmental time. His publication record reveals a strong focus on brain connectivity and organization across development. Recent work explores structural and functional neurodevelopmental trajectories, brain wiring economics, and the impact of environmental factors on neural development. Astle's research frequently employs advanced data science techniques to identify sub-populations of children with different cognitive or brain profiles, regardless of diagnosis, and to map non-linear relationships between brain organization and cognitive difficulties. His work has increasingly focused on transdiagnostic approaches to understanding developmental disorders and the application of computational models to developmental neuroscience. Astle actively supervises PhD students and has built a substantial research group that contributes to major projects including the Centre for Attention Learning and Memory (CALM) and Resilience in Education and Development (RED). His work has been supported by prestigious funding bodies including the Royal Society, the British Academy, the Medical Research Council, and the Economic and Social Research Council, as well as multiple charitable foundations. The 4D Lab, under Astle's leadership, utilizes state-of-the-art facilities at the University of Cambridge, including on-site magnetic resonance imaging and magnetoencephalography scanners. The lab contributes to building specialist cohorts such as CALM (800 children with cognitive difficulties plus 200 comparison children) and RED, which study children's development, resilience, and educational outcomes. Astle's team explores how growing up in adverse environments affects children's brains, behavior, and mental health, with the aim of identifying early markers of risk and resilience.
Xenophon Papademetris is a Professor of Biomedical Informatics & Data Science and Radiology & Biomedical Imaging at Yale School of Medicine. He serves as Associate Director of Biomedical Imaging Data Sciences at Yale Biomedical Imaging Institute and directs the Medical Software and Medical Artificial Intelligence Certificate Program. PhD in Electrical and Information Sciences from Yale University (2000) BA from Cambridge University (1994) Postdoctoral Fellowship at Yale University (2002) His research focuses on medical image analysis, machine learning, and biomedical software development. He has developed tools like BioImage Suite Web and contributed to standards committees at the Association for the Advancement of Medical Instrumentation (AAMI). His work spans modalities including MRI, CT, PET, and optical imaging. Recent publications emphasize neuroimaging analysis, explainable AI in healthcare, and multimodal data integration across species. He leads NIH-funded research under the BRAIN Initiative (R24 MH114805) and has authored a textbook on Medical Software published by Cambridge University Press. IEEE Senior Member Yale Brown-Coxe Postdoctoral Fellowship Harding Bliss Prize for Excellence in Engineering He directs the BioImage Suite Project, creating web-based image analysis tools using JavaScript and WebAssembly. His teaching includes both academic courses and a Coursera program on Medical Software with over 14,000 enrollments.
Dr. Kanwaljeet S. Anand is a dual-appointed Professor of Pediatrics (Pediatric Critical Care) and Anesthesiology, Perioperative & Pain Medicine at Stanford University School of Medicine. As director of the Pain/Stress Neurobiology Lab and Jackson Vaughan Critical Care Research Fund, he serves as Editor-in-Chief of Pediatric Research and maintains active membership in Bio-X, MCHRI, and Wu Tsai Neurosciences Institute. Rhodes Scholar with D.Phil from University of Oxford Harvard postdoctoral fellowship and Boston Children's Hospital residency Founded Harmony Health Clinic - Arkansas' largest charitable medical-dental facility A translational researcher with 30+ years of impact, Dr. Anand established the first scientific framework for infant pain perception and developed novel pain assessment methodologies. Current research focuses on: Hair biomarker analysis for stress and social affiliation (cortisol/oxytocin) Machine learning systems for objective pain detection in non-verbal infants Biopsychosocial interventions for stress reduction in disadvantaged youth Neurotoxicity mechanisms of sedatives in developing brains Global NICU opioid usage patterns through the NeoOpioid Consortium His work has yielded over 260 publications and significant advances in: Pediatric pain management protocols Neonatal stress biomarker development Critical care neurobiology insights Community health initiatives AI-driven clinical decision support systems Scientific Recognition 9th Annual 'In Praise of Medicine' Public Address, Erasmus University (2014) Nightingale Excellence Award (2016) Honorary Doctorate from University of Örebro (2019) NIH SBIB-H82 Study Section Chair (2018) Multiple IASP and American Pain Society awards Swedish Academy of Medicine's Nils Rosén von Rosenstein Award (2009) St. Jude Endowed Chairholder (2010) As mentor to Med Scholar Anjali Gupta and advisor to numerous professional bodies, Dr. Anand maintains active clinical leadership in Pediatric Intensive Care while advancing computational approaches to pain detection through collaborations with Stanford's AI researchers.
Carey E. Priebe is a Professor in the Department of Applied Mathematics and Statistics at the Whiting School of Engineering, Johns Hopkins University. He maintains strong affiliations with multiple research centers including the Johns Hopkins University Center for Imaging Science, the Mathematical Institute for Data Science, and the Human Language Technology Center of Excellence. His academic career spans several decades with significant contributions to statistical methodology and theory. Dr. Priebe's research focuses on computational statistics, statistical pattern recognition, and statistical inference for high-dimensional and graph data. His work bridges theoretical statistics with practical applications in areas such as brain connectome mapping, network analysis, and image processing. He has made significant contributions to spectral graph theory, graph matching, and vertex nomination, with applications ranging from neuroscience to national security. His publication record demonstrates consistent contributions to statistical methodology, with a notable emphasis on graph-based statistical methods. His research trajectory shows increasing focus on network data analysis, particularly in the last decade, with applications to brain mapping and connectome analysis as evidenced by his NSF BRAIN Initiative grant and Nature publication. 2013 Erskine Fellow (University of Canterbury) 2011 McDonald Award for Excellence in Mentoring and Advising 2010 ASA SDNS Distinguished Achievement Award 2009 Erskine Fellow (University of Canterbury) 2008 National Security Science and Engineering Faculty Fellow 2008 Pond Award for Excellence in Teaching NSF BRAIN EAGER grant recipient (2014) Professor Priebe has supervised an extensive number of doctoral students whose work spans statistical methodology, network analysis, and machine learning. His students have secured positions at prestigious institutions including academia (University of Wisconsin, Boston University), government research labs, and major technology companies (Microsoft, Facebook, Amazon). His research has been supported by significant grants from NSF, DARPA, and other agencies focused on national security applications and fundamental statistical methodology development. He maintains active collaborations across multiple disciplines and institutions, as evidenced by his numerous conference presentations and visiting appointments including at The Alan Turing Institute and The Isaac Newton Institute. His work bridges theoretical statistics with practical applications in neuroscience, security, and data science.
Matt Nassar is an Associate Professor of Neuroscience and Assistant Professor of Cognitive and Psychological Sciences at Brown University. He leads the Learning, Memory and Decision Lab, which is part of the Department of Neuroscience and the Robert J. & Nancy D. Carney Institute for Brain Science. His research focuses on understanding how the brain flexibly processes information to achieve complex and adaptive behaviors through computational approaches that bridge cognitive psychology and neuroscience. Education: PhD, University of Pennsylvania (2012) BA, Colgate University (2004) Nassar's research examines how different cognitive systems—learning, memory, and perception—leverage common computational principles to optimize decision-making. His work particularly focuses on how the brain balances stability and flexibility in processing information, how uncertainty is represented and utilized in learning, and how neural computations underlie complex behaviors. Through computational modeling and empirical research, he investigates how modular information-processing systems impact decisions and complex behavior in dynamic environments. His research integrates methods from cognitive psychology, neuroscience, and computational modeling to address fundamental questions about human cognition. Analysis of Nassar's recent publications (2020-2024) reveals a strong focus on computational neuroscience applied to decision-making, learning, and psychiatric conditions. His work frequently employs Bayesian modeling approaches to understand belief updating, uncertainty processing, and structure learning. Key themes include the neural basis of flexibility in learning, computational mechanisms underlying psychiatric symptoms, and age-related changes in cognitive processing. His research bridges cognitive psychology, neuroscience, and computational modeling to provide insights into both healthy cognition and disorders such as depression and schizophrenia. Scientific Contributions: Developed computational models of belief updating and learning under uncertainty Investigated neural mechanisms of stability-flexibility tradeoffs in cognition Examined age-related differences in learning and memory processes Explored computational mechanisms underlying psychiatric conditions Studied the role of noise correlations in neural learning systems Investigated how prefrontal cortex representations shape decision processes Nassar actively mentors researchers in his lab, with recent announcements highlighting postdocs joining from prestigious institutions like Max Planck UCL and Freie Universität Berlin. His lab appears to receive significant research funding, supporting multiple postdoctoral positions and research projects. Collaborations span multiple departments at Brown University, particularly with researchers in Cognitive and Psychological Sciences, Neurology, and Psychiatry. The lab has produced numerous high-impact publications in top journals including Nature Human Behaviour, Brain, and eLife. The Learning, Memory and Decision Lab, led by Nassar, is an active research group that uses computational models to understand how the brain represents and stores information for effective decision making. Recent lab announcements (as of February 2025) indicate the lab is expanding with new postdoctoral researchers joining from Harvard, Max Planck UCL, and Freie Universität Berlin, suggesting strong research momentum and funding support. The lab appears to be well-integrated within Brown's neuroscience community, with collaborations spanning multiple departments and research centers.
Kep Kee Loh is a Senior Tutor in the Department of Psychology at the National University of Singapore (NUS). Currently, he also holds an NUS Overseas Postdoctoral Fellowship position at both the Montreal Neurological Institute (McGill University) and the University of Oxford. His research focuses on comparative primate neuroanatomy, examining what makes the human brain special compared to other primates through multimodal MRI techniques. Ph.D. in Neuroscience from Université Claude Bernard Lyon I (2014-2018) M.Sc. in Cognitive Neuroscience from University College London (2011-2012) B.Soc.Sci. (Hons.) in Psychology from National University of Singapore (2007-2011) Dr. Loh's research primarily investigates the anatomical organization of brains across humans and various primate species including chimpanzees, baboons, and macaques. He employs different magnetic resonance imaging techniques (anatomical, resting-state, diffusion-weighted MRI) to compare brain organization across species, with particular focus on the medial frontal cortex, sulcal anatomy, and the evolution of speech and language in the human brain. His work adopts a multimodal approach to provide an integrative view of what sets human brains apart from other primates. His recent publications demonstrate a strong focus on comparative neuroanatomy across species, with particular emphasis on primate brain evolution, frontal cortex organization, and language-related neural pathways. The research spans multiple disciplines including neuroscience, cognitive science, and evolutionary biology, with increasing attention to methodological advancements in neuroimaging techniques for cross-species comparisons. NUS Overseas Postdoctoral Fellowship (2021) Institute of Language, Communications and the Brain (ILCB) Postdoctoral Fellowship (2019) Fondation Recherche Médicale (FRM) Fin de Thèse (PhD funding) (2017) BRAIN Student Travel Award, 6th Motivation and Cognitive Control Symposium (2016) Dr. Loh has been involved in numerous collaborative research projects across international institutions, including the French Institute of Health and Medical Research (Stem Cell and Brain Research Institute), Aix-Marseille Université, and currently McGill University and the University of Oxford. His work has received significant recognition with over 1,000 citations for his 33 publications. While specific grant information isn't detailed in the provided text, his postdoctoral fellowships indicate successful competitive funding. Dr. Loh collaborates with several research groups including the Montreal Neurological Institute at McGill University and research teams at the University of Oxford. His work connects with broader initiatives like the collaborative resource platform for non-human primate neuroimaging, indicating participation in larger research networks focused on advancing primate neuroscience through shared resources and methodologies.
