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.
Gerd Bruder is an Associate Professor at the University of Central Florida , affiliated with the Department of Computer Science . He previously held positions as a Research Associate Professor at UCF's Institute for Simulation and Training (2016-2022), Post-Doc at University of Hamburg (2014-2016), and Post-Doc at University of Würzburg (2011-2014). Habilitation : Computer Science, University of Hamburg (2017) Ph.D. : Computer Science, University of Münster (2011) M.Sc. : Computer Science (minor in Mathematics), University of Münster (2009) Research Interests : Gerd Bruder's work focuses on Extended Reality (XR) systems, spanning Virtual Reality (VR) , Augmented Reality (AR) , and Mixed Reality (MR) . Key areas include human factors , digital twins , 3D user interfaces , and perceptual modeling , with applications in healthcare, military operations, architecture, and smart environments. Recent Publications highlight innovations in XR locomotion , trust calibration with autonomous agents, spatial cognition , and haptic-visual integration . His 2025 articles address task-switching efficiency in XR and collaborative mixed reality design. Scientific Awards : Best Paper at ACM VRST 2023 2021 TechConnect Innovation Award Best Demo at ACM SUI 2021 Multiple Best Paper Awards (2016-2023) Best Poster & Demo Awards (2015-2020) Patents include systems for medical simulation , smart environment interruption management , and adaptive AR interfaces . His work bridges academic research and practical applications across VR, AR, and MR domains.
Suresh Krishna is an Associate Professor in the Department of Physiology at McGill University's Faculty of Medicine. His research focuses on the neurophysiological and computational basis of sensory processing, attention, and eye movements, with applications to brain-machine interfaces and human health. He works with human subjects, non-human primates, and open datasets using in-vivo electrophysiology, eye-tracking, and computational modeling. Research interests include visual attention mechanisms, saccadic eye movement control, neural coding of motion perception, and the interplay between attention and decision-making. His work bridges basic neuroscience with translational applications such as improving neural prosthetics and understanding perceptual disorders. Recent work highlights how neural remapping processes during saccades underlie spatial perception, and how attention modulates neural activity patterns in visual cortex. The lab's publications reveal critical insights into the temporal dynamics of attentional shifts and their neural substrates, particularly in areas MT and MST. Dr. Krishna's team also investigates auditory temporal processing in the inferior colliculus, exploring correlations between neuronal responses to sound modulation. Their findings contribute to understanding how sensory systems encode temporal information across modalities. Research is conducted in the M2B3 Lab (http://m2b3.lab.mcgill.ca), which integrates experimental and computational approaches to study brain mechanisms underlying perception and action. No specific awards are listed, but ongoing work involves major contributions to primate neurophysiology and translational neuroscience.
Milos Popovic is a Professor and Director at the Institute of Biomedical Engineering, University of Toronto, and serves as Senior Scientist and Director of Research at the Toronto Rehabilitation Institute (KITE), University Health Network. He holds the Toronto Rehab Chair in Spinal Cord Injury Research and Neural Engineering and co-leads the Centre for Research in Advanced Neural Implant Applications (CRANIA). His leadership spans academic, clinical, and translational research domains in neuroengineering and rehabilitation. His research focuses on neurorehabilitation, neuroprosthetics, functional electrical stimulation (FES), brain-machine interfaces, and assistive technologies . He develops physiological control systems and therapeutic robotics to restore motor function after spinal cord injury and stroke. His work integrates biomechanics, signal processing, and human-machine interfaces to innovate rehabilitative solutions. Dr. Popovic's recent publications emphasize electrical neuromodulation, FES therapy for motor recovery, deep brain stimulation for cognitive deficits, and wearable/implantable technologies . The articles reflect a strong trend toward closed-loop systems, translational research, and interdisciplinary innovation in neuroengineering and rehabilitation. Swiss National Science Foundation Technology Transfer Award - 1st place (1997) Engineering Medal for Research and Development, Professional Engineers of Ontario (2008) Elected to the College of Fellows, American Institute of Medical and Biological Engineering (2011) 1st Prize and Best Intellectual Property Award, TiEQuest Business Venture Competition (2012) Morris (Mickey) Milner Award for Assistive Technologies (2013) University of Toronto Inventor of the Year Award (2013) University Health Network’s Inventor of the Year Award (2015) Dr. Popovic has secured significant research funding and leads the Neural Engineering and Therapeutics team at KITE. He co-founded MyndTec Inc. and the Canadian National Spinal Cord Injury Conference, demonstrating strong mentorship, innovation, and national impact. He advises graduate students and postdoctoral fellows in biomedical engineering and rehabilitation sciences. He leads the Rehabilitation Engineering Laboratory and the Neural Engineering and Therapeutics Team at KITE, fostering collaboration across engineering, neuroscience, and clinical rehabilitation. His lab develops advanced neurotechnologies with a focus on clinical translation and commercialization.
Surya Ganguli is an Associate Professor in the Department of Applied Physics at Stanford University, with courtesy appointments in Neurobiology and Electrical Engineering. He serves as Senior Fellow at the Stanford Institute for Human-Centered AI and is affiliated with the Stanford Neuroscience Institute , Bio-X , and Wu Tsai Neurosciences Institute . His research spans theoretical neuroscience, machine learning, and statistical mechanics. Ph.D. , UC Berkeley, Theoretical Physics (2004) M.A. , UC Berkeley, Physics (2000) M.A. , UC Berkeley, Mathematics (2004) M.Eng. , MIT, Electrical Engineering and Computer Science (1998) B.S. , MIT, Physics (1998) B.S. , MIT, Mathematics (1998) B.S. , MIT, Electrical Engineering and Computer Science (1998) His lab explores how higher-level cognitive phenomena emerge from neural network dynamics, focusing on perception, memory, attention, and decision-making . Research themes include statistical mechanics of learning , neural representational geometry , and biologically plausible learning rules . Current work examines nonlinear interactions in neural networks through the Schmidt Science Polymath Award (2023). Key article trends reveal expertise in neural coding limits (2022 Nature), synaptic plasticity models (2022 Neural Computation), and deep learning theory (2022 NeurIPS publications). His 2019 Annual Review chapter on statistical mechanics of deep learning established foundational insights into network criticality. Scientific Awards : NSF Career Award (2019) Simons Foundation Investigator (2016) McKnight Scholar Award (2015) James S. McDonnell Foundation Scholar (2014) Sloan Research Fellow (2013) As advisor, he mentors 10+ doctoral students across Applied Physics, Neurosciences, and Computer Science, including Vamshi Balanaga and Mason Kamb. His lab collaborates with experimental teams at Stanford and beyond, supported by grants from NSF , Simons Foundation , and Swartz Foundation . The Neural Dynamics & Computation Lab unites physicists, mathematicians, and neuroscientists to decode cognition through interdisciplinary methods.
Andrew Rice is a Professor of Computer Science at the University of Cambridge's Department of Computer Science and Technology, and holds the Hassabis Fellowship in Computer Science. He is also the Director of Studies in Computer Science at Queens' College. His research focuses on programming languages, software engineering, and machine learning applications in software development. He leads projects like Isaac Computer Science and ALTA (Automated Language Teaching and Assessment), advancing adaptive learning technologies. His work includes static analysis tools such as Error Prone at Google, energy efficiency studies in computing infrastructure, and contributions to the Computing for the Future of the Planet initiative. His teaching emphasizes practical skill development through flipped classrooms and video lectures, earning him the 2014 Pilkington Prize for teaching excellence. He has held visiting roles at Google and collaborated on energy consumption research for mobile devices and data centers. His research spans systems, networking, and natural language processing, with a strong focus on applying computational methods to real-world challenges. Key Projects: Isaac Physics/Computer Science, ALTA, Error Prone Static Analysis Research Themes: Programming Languages, Machine Learning, Energy Efficiency Awards: Pilkington Prize (2014)
Michael J. Frank is the Edgar L. Marston Professor of Psychology and Professor of Brain Science at Brown University's School of Cognitive, Linguistic, and Psychological Sciences. He holds academic affiliations with the Carney Institute for Brain Science and specializes in cognitive neuroscience, computational neuroscience, and decision-making processes. Frank earned his Ph.D. in Neuroscience & Psychology from the University of Colorado at Boulder in 2004, and joined Brown University in 2011 after serving as a Professor at the University of Arizona. His research integrates computational modeling and experimental methods to explore neural mechanisms underlying reinforcement learning, decision-making, and cognitive control, with a focus on prefrontal cortex-basal ganglia interactions and dopamine modulation. Frank's honors include the Troland Research Award (2021), Kavli Fellowship (2016), and the Cognitive Neuroscience Society Young Investigator Award (2011). He is an editor for eLife, Behavioral Neuroscience, and the Journal of Neuroscience. His lab, based at http://ski.clps.brown.edu, investigates topics such as neural circuit models of cognitive control, neuropsychological testing, and translational applications of computational models in psychiatry. Frank's research emphasizes interdisciplinary approaches, combining behavioral experiments, neuroimaging (fMRI, EEG), and pharmacological studies to dissect brain-behavior relationships. Key findings include insights into dopamine's role in motivation, decision-making deficits in schizophrenia, and computational phenotyping of mental disorders. His work bridges basic science and clinical applications, aiming to inform therapeutic strategies for neurological and psychiatric conditions.
Gordon Geoffrey J. is a Professor at Carnegie Mellon University, specializing in Machine Learning , Artificial Intelligence , and Computer Science . His research spans Reinforcement Learning , Domain Adaptation , Graph-based Planning , and Relational Learning , with significant contributions to Adversarial Learning , Deep Learning Dynamics , and Mobile Sensor Networks . He has pioneered algorithms like ARA* and Anytime Dynamic A* for efficient planning under constraints. Key research areas: Transfer Learning , Bayesian Knowledge Tracing , Multi-agent Systems , Database Optimization His work has been cited thousands of times across foundational topics in AI and robotics, demonstrating sustained impact in algorithm design and applications to complex systems.
Rick Hoyle is a Professor of Psychology and Neuroscience at Duke University, where he serves as Associate Chair of the Department of Psychology and Neuroscience. He is also a Faculty Network Member of the Duke Institute for Brain Sciences. His academic career spans decades, with significant contributions to understanding self-regulation and adolescent development. Dr. Hoyle earned his B.A. from Appalachian State University (1983), followed by an M.A. (1986) and Ph.D. (1988) from the University of North Carolina, Chapel Hill. He progressed from Assistant to Associate to Full Professor at the University of Kentucky from 1989 to 2003 before joining Duke University. His research focuses on how adolescents and emerging adults manage goal pursuit through self-regulation, taking a broad view that accounts for personality, environment, cognition, emotion, and social influences. He employs longitudinal methods with repeated assessments, sometimes spanning years with data collection multiple times per year, or intensive studies with assessments several times daily. His lab develops innovative measurement tools including self-report measures of self-control and grit, as well as unobtrusive approaches using mobile phones and wearable devices to track goal pursuit in natural settings. His recent publications reveal a strong focus on self-regulation in digital contexts, adolescent substance use, socioeconomic influences on development, and innovative measurement approaches. His work increasingly integrates technology (social media analysis, wearable devices) with traditional psychological research methods to understand real-world behavior. Fellow, Association for Psychological Science (2013) Dr. Hoyle has secured numerous research grants totaling millions of dollars, including the current Real-Time and Randomized Tests of Social Media and Mental Health Links in Early Adolescence (2024-2029), NCCU Duke - Substance Use Research & Education (2024-2029), and Mid-Life Health Inequalities in the Rural South: Risk and Resilience (2023-2028). His grant portfolio demonstrates sustained funding for research on adolescent development, substance use, self-regulation, and health disparities. He teaches advanced courses including Applied Structural Equation Modeling and Psychology and Neuroscience Grant Writing, mentoring the next generation of researchers in quantitative methods and research design. His work through the Center for the Study of Adolescent Risk and Resilience (2008-2025) has established a significant research infrastructure for longitudinal studies of adolescent development. Current projects examine social media effects on mental health, substance use patterns, and the impact of environmental factors on adolescent well-being using innovative digital tracking methods.
David Clewett, Ph.D. , is an Assistant Professor of Psychology at University of California, Los Angeles (UCLA) , where he leads the Dynamic Arousal and Memory Lab. His research explores how emotional arousal, stress, and neuromodulatory systems like norepinephrine and dopamine shape the way we encode, organize, and retrieve memories. He joined UCLA in July 2020 after completing his Ph.D. in Neuroscience at the University of Southern California and a postdoctoral fellowship at NYU and Columbia University. Education: Ph.D. in Neuroscience, University of Southern California (2016) B.S. in Biopsychology with minor in English, University of California, Santa Barbara Research Focus: Dr. Clewett’s lab investigates how physiological arousal influences attention and memory, using a multimodal approach that includes fMRI, pupillometry, eye-tracking, and pharmacological methods. His work spans four major themes: The role of emotion and arousal in selective memory enhancement or suppression. How contextual shifts and emotional states structure episodic memory into meaningful events. Techniques to weaken or update traumatic or unwanted memories. The dynamic effects of attention and arousal states on neural and memory representations. Publications and Impact: His work has been published in top-tier journals such as Nature Communications , Journal of Neuroscience , Hippocampus , and Trends in Cognitive Sciences . His research has contributed to understanding how neuromodulators like norepinephrine and dopamine interact with memory systems to prioritize salient information, and how these processes can be leveraged for therapeutic intervention in PTSD, depression, and aging-related memory decline. Students and Lab Team: Dr. Clewett mentors a diverse team of graduate students and research assistants. Current graduate students include Jacinda Taggett, Ringo Huang, Erin Morrow, Bailey Harris, and Brandon Katerman. Former lab members have gone on to Ph.D. programs at Harvard, UC Berkeley, and other top institutions. Lab and Facilities: The lab is located in Pritzker Hall at UCLA, and is equipped with tools for fMRI, pupillometry, eye-tracking, and behavioral testing. The lab also collaborates with researchers across UCLA and other institutions, including NYU, Columbia, and USC.
Risto Miikkulainen is a Professor of Computer Science and Neuroscience at the University of Texas at Austin and VP of AI Research at Cognizant AI Lab. He directs the UTCS Neural Networks Research Group and is currently on leave from UT, working on Evolutionary Computation and Deep Learning at Sentient Technologies, Inc. Education: Ph.D. in Computer Science, UCLA, 1990 M.S. in Applied Mathematics, Helsinki University of Technology (now Aalto University), 1986 Risto Miikkulainen's research focuses on biologically-inspired computation such as neural networks and evolutionary computation. His work spans three main areas: (1) Neuroevolution, evolving complex deep learning architectures and recurrent neural networks for sequential decision tasks in robotics, games, and artificial life; (2) Cognitive Science, developing models of natural language processing, memory, and learning that shed light on disorders such as schizophrenia and aphasia; and (3) Computational Neuroscience, studying the development, structure, and function of the visual cortex, episodic memory, and language processing. His research combines theoretical understanding of biological information processing with practical applications for developing intelligent artificial systems. His recent publications (2025) show a strong focus on evolutionary approaches to AI development, particularly in neural architecture search, loss function optimization, and explainable AI. Many papers explore the intersection of evolutionary computation with deep learning, creating more efficient and transparent AI systems. His work spans theoretical foundations and practical applications in areas ranging from environmental control systems to cognitive modeling. Scientific Awards: College of Fellows, International Neural Network Society, 2024 Best Pathway to Impact Award, NeurIPS Climate Change workshop, 2024 AAAI Fellow, 2023 IEEE CIS Evolutionary Computation Pioneer Award, 2020 Gabor Award, International Neural Network Society, 2017 Outstanding Paper of the Decade Award, International Society for Artificial Life, 2017 IEEE Fellow, 2016 Multiple Best Paper Awards at GECCO, CIG, and CEC conferences Deployed Application Award, AAAI/IAAI-2013, AAAI/IAAI-2018 Miikkulainen has extensive experience mentoring students through undergraduate research courses like CS378 Computational Intelligence in Game Design I and II, where students develop independent research projects on the OpenNERO research platform. He has received multiple awards for deployed applications, demonstrating the practical impact of his research. His work has led to the development of the NERO game platform, which serves as both an educational tool and research platform for AI. He directs the UTCS Neural Networks Research Group, which focuses on neuroevolution, cognitive science models, and computational neuroscience. The group has developed the NERO (Neuro-Evolving Robotic Operatives) platform, a machine learning game that allows users to train intelligent agents through evolutionary computation. The group's work spans theoretical research and practical applications in AI, with connections to both academic and industry partners.
Peter Gries is a Professor of Chinese Politics and the Lee Kai Hung Chair at the University of Manchester, serving as the founding Director of the Manchester China Institute. He previously held roles at the University of Oklahoma and the University of Colorado, Boulder. Born in Singapore, he holds degrees from Middlebury, the University of Michigan, and a PhD from UC Berkeley. His research focuses on political psychology, particularly U.S.-China relations and Chinese nationalism, with notable works including China’s New Nationalism and The Politics of American Foreign Policy . He leads initiatives like the UK-China International Photography Award and the UK-China Diplomatic Dialogue. His grants include funding from the Ford Foundation and Smith Richardson Foundation for projects on security dialogues and course development. He sits on editorial boards of The China Quarterly , Political Psychology , and Nations & Nationalism . Recent work explores racial stereotypes, intersectionality, and threat perception in international relations. Education : Bachelor's and Master's in Asian Studies from Middlebury and the University of Michigan PhD in Politics from UC Berkeley Research Interests : Political psychology of international affairs U.S.-China relations Chinese nationalism and foreign policy Public opinion dynamics Grants : $60,000 from Smith Richardson Foundation (2005–2006) Multiple grants from Ford Foundation and Mershon Center for Sino-American Security Dialogues $4,000 from Freeman Foundation for Chinese Foreign Policy course development Media Contributions : Expert commentary on BBC World News, NYT, and CGTN Discussions on U.S.-China trade wars, China’s Japan policy, and global perceptions of China Labs/Institutes : Manchester China Institute Former director of the Institute for US-China Issues at University of Oklahoma
James T. Enns is a Professor and Distinguished University Scholar in the Department of Psychology within the Faculty of Arts at the University of British Columbia. His research primarily focuses on the role of attention in human vision, with secondary interests in developmental psychology and human-machine interaction. Dr. Enns' research interests span perception, attention, vision, cognition, development, and human-machine interaction. His work explores how the human mind selects information, with particular emphasis on visual attention mechanisms. His laboratory research investigates the fundamental processes of visual perception and how attention modulates these processes across different contexts and developmental stages. Analysis of Dr. Enns' publication record reveals consistent engagement with visual perception, attentional mechanisms, and cognitive processing. His research demonstrates expertise in both theoretical frameworks and experimental methodologies related to visual attention, with applications spanning basic cognitive science to potential implementations in human-computer interaction systems. His work often bridges theoretical cognitive psychology with practical applications. Canadian Society for Brain, Behaviour and Cognitive Science Donald O. Hebb Distinguished Contribution Award (2013) Distinguished University Scholar, UBC (2004) Robert E. Knox Master Teaching Award (2004) Royal Society of Canada Fellow (2002) Killam Faculty Research Prize (1994) Killam Faculty Research Fellowship (1993) Society of Experimental Psychology Fellow Dr. Enns has served as Editor for the Journal of Experimental Psychology: Human Perception and Performance, and as Associate Editor for Psychological Science, Consciousness and Cognition, and Visual Cognition. His research has been supported by grants from NSERC, the Canadian Foundation for Innovation, the Australian Research Council, BC Health, and Nissan. He has authored textbooks on perception, edited research volumes on the Development of Attention, and published numerous scientific articles on vision, attention, and cognitive science. Dr. Enns is currently accepting graduate students and continues to mentor the next generation of cognitive scientists. Dr. Enns leads the UBC Vision Lab, which focuses on how the human mind selects information. The lab conducts research on visual attention, perception, and cognitive processes using a variety of experimental methodologies.
Nathaniel D. Daw serves as the Huo Professor in Computational and Theoretical Neuroscience and Professor of Neuroscience and Psychology at Princeton University, based at the Princeton Neuroscience Institute. His research integrates computational modeling with experimental neuroscience to investigate fundamental mechanisms of learning and decision-making. Daw's research focuses on computational and theoretical neuroscience, specializing in reinforcement learning, memory systems, and decision-making processes. He examines how neural circuits represent value, update beliefs through experience, and balance model-based versus model-free control strategies. His work frequently bridges theoretical frameworks with behavioral and neural data to explain phenomena ranging from habitual behavior to flexible cognitive control. Analysis of his 2025 publications reveals dominant themes in neural replay mechanisms, individual differences in learning trajectories, and clinical applications to eating disorders. His work increasingly incorporates large language models for psychological assessment while maintaining core focus on interpretable cognitive architectures and hierarchical planning. Daw maintains active research operations through the Princeton Neuroscience Institute, an interdisciplinary hub fostering collaboration between computational modelers, neuroscientists, and psychologists to advance understanding of neural mechanisms underlying cognition.
Andrew Yonelinas is a Professor in the Department of Psychology at the University of California, Davis, where he directs the Human Memory Lab. He holds additional leadership roles as Associate Director of the Center for Mind and Brain and is an affiliated faculty member with the UC Davis Center for Neuroscience. His research bridges cognitive psychology and neuroscience to investigate fundamental memory mechanisms and their neural substrates. His educational background includes a Ph.D. in Experimental Psychology from McMaster University (1995) and a B.S. in Cognitive Science from the University of Toronto (1990). These foundational studies established his expertise in experimental methodologies and cognitive theory. Yonelinas specializes in dual-process models of memory, distinguishing between recollection (detailed contextual retrieval) and familiarity (vague recognition). His lab employs process dissociation, remember/know procedures, and ROC modeling alongside neuroimaging (fMRI, ERP) and clinical studies with amnesic and Alzheimer's patients. Recent work expands into auditory working memory, multisensory integration, and the impact of mental illness on cognitive processes, revealing hippocampal roles across memory systems. His research consistently addresses how memory fails in clinical conditions while developing unified theoretical frameworks. Analysis of his 2024-2025 publications shows a strong focus on memory mechanisms across sensory modalities, with increasing emphasis on clinical applications. Key trends include hippocampal contributions to visual/auditory working memory, EEG-based biomarkers for mental illness, and the interplay between schema knowledge and memory distortion in aging populations. His work demonstrates methodological innovation through model-based EEG phenotyping and multisite clinical collaborations. His scientific recognition includes: American Psychological Society’s Shahin Hashtroudi Memorial Award University of California Chancellor’s Fellow Award European Brain and Behavior Society International Lecture Award Yonelinas actively shapes his field through editorial roles at top journals including Proceedings of the National Academy of Sciences and Journal of Experimental Psychology, while serving as a grant reviewer for NIH, NSF, and international funding bodies. His Human Memory Lab trains next-generation researchers in memory theory and methodology, with recent projects examining stress effects on memory precision and neural mechanisms of action slips. The Human Memory Lab operates within UC Davis's neuroscience ecosystem, collaborating closely with the Center for Mind and Brain on projects involving clinical populations and neuroimaging. Current initiatives include the CNTRACS Consortium for EEG standardization in mental illness and investigations into how stress modulates memory binding through hippocampal mechanisms.
