جستجوی استاد راهنمای دانشگاهی

Joe Kableمشاهده پروفایل

دانشیار

• Decision Making
• Neuroeconomics
• Cognitive Neuroscience
• +۲ مورد دیگر

Joe Kable, PhD, serves as the Baird Term Associate Professor of Psychology at the University of Pennsylvania, where his research investigates the neurophysiological basis of human decision-making through integrative approaches from experimental economics, cognitive neuroscience, and judgment psychology. His laboratory specializes in fMRI studies examining how subjective value representations guide choices involving immediate versus delayed rewards. Education: B.S. in Chemistry, Emory University PhD in Neuroscience, University of Pennsylvania Dr. Kable's research program centers on neural mechanisms of temporal discounting, risk assessment, and individual differences in choice behavior. His work demonstrates how socioeconomic status, aging, and clinical conditions modulate decision processes, with particular emphasis on hippocampal-prefrontal interactions during value computation. Recent studies reveal how time perception alterations affect neural activity in reward circuits and how social factors influence trust decisions across the lifespan. Analysis of his 15 most recent publications shows a strong methodological focus on fMRI and lesion studies, with growing clinical translation in depression, addiction, and dementia. Key thematic trends include the neural encoding of effort costs in social contexts, structural brain markers for impulsivity, and the dissociable roles of frontal subregions in persistence behaviors. His work consistently bridges basic decision neuroscience with real-world applications in mental health. Scientific Awards: No scientific awards mentioned in source material Dr. Kable leads an active research laboratory at Penn but the source text provides no details about graduate student advising or specific grant funding. His publications indicate collaboration with clinical researchers at the Penn Memory Center, particularly in aging-related decision studies. The laboratory employs multimodal neuroimaging techniques including resting-state fMRI, TMS, and lesion mapping to investigate decision circuits, with recent work extending to computational modeling of value representation and social cognition mechanisms.

Mayank R. Mehtaمشاهده پروفایل

استاد

• Neurophysics
• Synaptic Plasticity
• Learning and Memory
• +۳ مورد دیگر

Mayank R. Mehta is a Professor at the University of California, Los Angeles (UCLA), holding joint appointments in the Departments of Physics & Astronomy, Neurology, and Neurobiology. He is a member of the Brain Research Institute and the W. M. Keck Center for Neurophysics at UCLA. His research bridges experimental and theoretical neuroscience, focusing on how neuronal networks encode space-time, the role of brain rhythms in learning and memory, and the impact of sleep and virtual reality on neural dynamics. His recent publications highlight breakthroughs in understanding hippocampal spatiotemporal selectivity, dendritic activity during behavior, and the causal influence of visual cues on memory neurons. Notable findings include the discovery that dendrites generate ten times more spikes than neuronal cell bodies and the modulation of hippocampal theta rhythms in virtual reality. Research Themes: Neurophysics of spatial-temporal coding Dendritic contributions to learning Virtual reality and brain plasticity Neural oscillations in memory consolidation Key Collaborators: Bert Sakmann (Max Planck Florida Institute) Thomas Hahn (Bernstein Center Heidelberg/Mannheim) Maryam Ghorbani (UCLA) Mehta's lab at UCLA trains graduate and postdoctoral researchers in cutting-edge techniques combining hardware development, electrophysiological recordings, and biophysical modeling. His work has significant implications for treating learning and memory disorders like Alzheimer's disease.

• Neurosciences
• Biomedical Engineering
• Cognitive Sciences
• +۴ مورد دیگر

Dr. Juan Alvaro Gallego is a Senior Lecturer (equivalent to Associate Professor) in the Department of Bioengineering at Imperial College London's Faculty of Engineering. He leads the Behaviour and Neural Dynamics Lab (Be.Neural), a multidisciplinary team focused on understanding neural mechanisms underlying motor control and spinal cord learning, with applications in developing neural interfaces to restore movement in conditions like Parkinson’s disease and paralysis. His research integrates behavioral experiments, neural recordings, data analysis, and computational models, funded by the ERC, EPSRC, ARIA, and industry partners like InBrain Neuroelectronics and Meta Reality Labs. Research interests include motor control, neural dynamics, and clinical applications of neural engineering. The lab collaborates across systems neuroscience and biomedical engineering, aiming to translate fundamental discoveries into therapeutic technologies. Key areas of focus include neural manifolds, synaptic plasticity in motor learning, and closed-loop neuroprosthetics for tremor management. Funding sources include the European Research Council, Engineering and Physical Sciences Research Council, and industry collaborations. The Be.Neural Lab’s work is showcased on their dedicated website (https://beneural.ic.ac.uk).

John P. O'Dohertyمشاهده پروفایل

استاد

• Decision Neuroscience
• Reward-related Learning
• Computational Modeling
• +۳ مورد دیگر

John P. O'Doherty serves as the Fletcher Jones Professor of Decision Neuroscience within Caltech's Division of Humanities and Social Sciences, holding continuous faculty appointments since 2004 (Assistant Professor 2004-07, Associate Professor 2007-09, Professor 2009-present, Fletcher Jones Professor 2021-present). He previously directed the Caltech Brain Imaging Center (2013-17) and maintains affiliations with the T&C Chen Center for Social and Decision Neuroscience. His educational background includes a B.A. from University of Dublin, Trinity College (1996) and D.Phil. from University of Oxford (2000). His research focuses on computational and neural mechanisms of reward-based learning and decision-making , employing fMRI, intracranial recordings, and mathematical modeling to investigate how the brain solves complex decision problems through evolutionarily conserved algorithms. Key areas include Reinforcement learning systems (model-based/model-free arbitration) Observational and social learning mechanisms Neural representation of value, risk, and uncertainty Computational phenotyping of mental disorders Temporal dynamics of goal persistence Analysis of his 2023-2025 publications reveals dominant trends in computational psychiatry (problem gambling, autism traits), hierarchical decision-making, and neuroeconomic modeling of social behavior. His work consistently integrates cross-species computational frameworks with human neuroimaging to identify transdiagnostic mechanisms. While specific awards beyond his endowed professorship aren't detailed, his leadership as Brain Imaging Center Director and prolific high-impact publications demonstrate significant recognition. Current advising includes graduate researcher Sneha Aenugu on goal-persistence projects, with administrative support from Mary A. Martin (mmartin@caltech.edu). His active research program continues to pioneer computational approaches to understanding decision pathologies.

Susanne Weisمشاهده پروفایل

استاد پژوهش

• Neuroimaging
• Cognitive Neuroscience
• Machine Learning in Neuroscience
• +۳ مورد دیگر

Susanne Weis is a Research Professor and Group Leader of the 'Variability of the Brain' group at the Department of Brain and Behavior (INM-7), part of the Institute of Neuroscience and Medicine (INM) at Research Center Jülich GmbH. Her work focuses on understanding brain variability through advanced neuroimaging techniques and machine learning, with particular emphasis on sex differences, hormonal influences, and clinical applications in mental health. Her research interests include neuroimaging methodologies, machine learning applications in cognitive neuroscience, and the structural-functional relationships underlying brain variability. She investigates how factors like sex hormones and naturalistic stimuli (e.g., movies) affect brain connectivity and cognitive performance, aiming to improve diagnostic and predictive tools for disorders such as schizophrenia and Alzheimer’s disease. Publications highlight her contributions to developing datasets (e.g., SpEx), analyzing confound leakage in ML models, and exploring meta-analytic networks during naturalistic viewing. Her work bridges basic science and clinical impact, addressing challenges in interpreting neuroimaging data and advancing personalized medicine approaches. In her role as a group leader, Weis oversees research projects and collaborates with interdisciplinary teams. She is affiliated with the Helmholtz Association and contributes to the broader scientific community through her research in neuroimaging and computational neuroscience.

• Perinatal Brain Injury
• Neonatal Neuroscience
• Maternal Nutrition
• +۵ مورد دیگر

Carina Mallard is Professor of Experimental Perinatal Brain Injury Research and Pro-Vice-Chancellor at the University of Gothenburg since July 2023, where she oversees research infrastructure and chairs the Research Board. She previously served as Deputy Vice-Chancellor (2021–2023) and Head of Core Facilities (2018–2021). Her academic career began with a Physiology degree from Lund University and a PhD in Pediatrics at the University of Auckland (1995), followed by a professorship appointment in 2006. Her research focuses on perinatal brain injury , particularly in premature infants , investigating neuroinflammatory mechanisms maternal dietary interventions neurovascular unit dynamics microglia activation long-term consequences of maternal obesity . Recent publications highlight her work on neonatal rodent models , transcriptomic profiling , and anti-inflammatory therapies . Key themes include RNA degradation , mitochondrial protection , and immune-neurovascular interactions . She has supervised 20 doctoral students since 2004 and collaborates internationally with institutions like King's College London. Contact details include two institutional emails and physical addresses across Gothenburg's biomedical campuses. Her leadership roles span research strategy, doctoral education, and EU-level scientific initiatives.

Aidan J Hornerمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Memory Research
• Neuroimaging
• +۳ مورد دیگر

Aidan J Horner is a Professor in the Department of Psychology at the University of York. He holds a BSc in Psychology (2005) and MSc in Cognitive Neuroscience (2006) from the University of York, followed by a PhD in Cognitive Neuroscience from the University of Cambridge (2010). His career includes postdoctoral research at Otto-von-Guericke University (2010–2011) and University College London (2011–2016), and a visiting scholar position at Stanford University (2008). He returned to York as a Lecturer in 2016, advancing to Senior Lecturer and his current Professorship. Research Focus: Horner’s work examines how the brain encodes and retrieves long-term memories, particularly spatial and event-based information. He employs experimental psychology, virtual reality, neuroimaging (e.g., fMRI, MEG), and computational modeling to study hippocampal and cortical mechanisms underlying memory formation, consolidation, and forgetting. His recent studies explore the role of theta oscillations in memory binding, the impact of emotion on memory coherence, and forgetting dynamics. Publications & Awards: Over 50 peer-reviewed articles, including high-impact work in Current Biology , Nature Communications , and Cognition . Recognized with the Annual Cognitive Paper Prize Award (2022) for groundbreaking contributions to memory research. Grants & Projects: Lead investigator on ESRC-funded projects (e.g., "Promoting rapid and sustained learning of novel information" , 2018–2022). Collaborates with institutions like the York Neuroimaging Centre (YNiC) to advance neuroimaging techniques in cognitive studies. Labs & Teams: Affiliated with the York Neuroimaging Centre (YNiC), integrating neuroimaging with behavioral and computational approaches to memory systems. Active in interdisciplinary teams studying memory plasticity and cognitive neuroscience.

Matthew B. Blaschkoمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +۵ مورد دیگر

Matthew B. Blaschko is a Professor in the Department of Electrical Engineering at KU Leuven, Belgium. He serves as director of the KU Leuven ELLIS unit and is a fellow in the ELLIS Health program. He is a Core PI in the Flanders AI Research Program, working as a workpackage lead for Decision Support Systems and Medical Imaging. Blaschko is also a member of the KU Leuven Institute for Artificial Intelligence and one of the leaders of the working group on Machine Learning and Data Science. Professor Blaschko received his B.S. from Columbia University, M.S. from the University of Massachusetts Amherst, and Dr. rer. nat. from Technische Universität Berlin (awarded for work at Max Planck Institutes Tübingen). He was a Newton International Fellow at the University of Oxford and received his Habilitation (HDR) from École Normale Supérieure de Cachan. Prior to joining KU Leuven, he was a Permanent Research Scientist in the INRIA Saclay Research Center and a Faculty Member at Ecole Centrale Paris. His research focuses on machine learning techniques applied to visual data, with particular emphasis on calibration in deep learning, medical image analysis, and federated learning. Blaschko's work bridges theoretical foundations with practical applications, as evidenced by technology developed in his research being incorporated into MONA, software for ophthalmic image analysis. His research group has made significant contributions to the fields of model calibration, uncertainty estimation, and medical imaging analysis, with recent publications showing strong trends toward improving reliability of AI systems in medical contexts and advancing theoretical understanding of calibration metrics. Professor Blaschko has been recognized with several awards including the Université Paris-Saclay STIC Doctoral School Best Scientific Contribution Award, Best Paper Award at CVPR 2008, Main Award at DAGM 2008, and Best Student Paper Award at ECCV 2008. Professor Blaschko has supervised numerous PhD and Master's students, with current and former students including Deniz Soysal, Claire Marchal, Dongli Xu, Sebastian Gruber, Jiameng Li, Marco Mezzina, and many others working on diverse topics from Alzheimer's disease analysis to surgical phase recognition. His research has been supported by various funding sources including the Flanders AI Research Program. He has co-organized several influential workshops including the "Another Brick in the AI Wall: Building Practical Solutions from Theoretical Foundations" at CVPR 2025, Commands 4 Autonomous Vehicles workshop at ECCV 2020, and the Learning from Limited Labeled Data workshop series at NIPS 2017 and ICLR 2019. His laboratory focuses on machine learning for medical image analysis, with applications in ophthalmology, neurology, and surgical robotics. The group maintains active collaborations with medical institutions and participates in international challenges such as the KNee OsteoArthritis Prediction (KNOAP2020) challenge.

Stefano Fusiمشاهده پروفایل

دانشیار

• Computational Neuroscience
• Theoretical Neuroscience
• Neural Circuit Modeling
• +۲ مورد دیگر

Stefano Fusi is an Associate Professor of Neuroscience at Columbia University's Vagelos College of Physicians and Surgeons, with joint affiliations at the Mortimer B. Zuckerman Mind Brain Behavior Institute and Kavli Institute. His laboratory focuses on computational modeling of neural circuits and neuromorphic engineering. Education PhD in Physics, Hebrew University of Jerusalem (1999) BS in Physics, Sapienza University of Rome (1992) Research Focus Fusi investigates how biological complexity supports neural computation through three primary domains: theoretical analysis of neural circuit dynamics, representational geometry in learning systems, and hardware implementations of brain-inspired algorithms. His work bridges machine learning, neurophysiology, and theoretical physics, emphasizing high-dimensional representations and memory optimization. Recent publications demonstrate consistent focus on neural coding principles across hippocampus, prefrontal cortex, and sensory systems, with innovations in modeling working memory, stress responses, and cross-species computational paradigms. Collaborations & Labs Leads an interdisciplinary laboratory collaborating with Columbia experimental neuroscientists, MIT engineers, and Stanford computational researchers to validate theoretical models. Current projects include neuromorphic hardware development and neural decoding of emotional states.

Simon Schäferمشاهده پروفایل

استاد

• Neuroscience
• Developmental Biology
• Stem Cell Research
• +۴ مورد دیگر

Prof. Dr. Simon Schäfer leads the Schäfer Lab at the Technische Universität München , focusing on engineering advanced organoid systems to study human brain development, disease modeling, and repair mechanisms. His work bridges stem cell biology, gene editing, and bioengineering to develop personalized therapies for brain disorders. Stem Cell & Organoid Technology Neurodevelopmental Mechanisms Neurodegenerative Disease Models Gene Editing & Neuroimmune Interactions Translational Neuroscience Recent research emphasizes brain organoid development, microglia phenotypes, and neurodevelopmental timing anomalies in autism. His team’s work also explores zika virus interactions with glioblastoma stem cells and neuronal plasticity in psychiatric disorders. Scientific awards and funding include support from the Deutsche Forschungsgemeinschaft (DFG), Brain & Behavior Research Foundation (BBRF), and Munich Cluster for Systems Neurology (SyNergy). Collaborations span institutions like the TUM Center for Organoid Systems. Advises 6 students (2 PhD, 1 MSc, 3 associated) Labs include Schäfer Lab, COS@TranslaTUM Contact: simon.schafer@tum.de

Dirk Bernhardt-Waltherمشاهده پروفایل

استاد

• Visual Perception
• Cognitive Neuroscience
• Scene Categorization
• +۵ مورد دیگر

Professor Dirk Bernhardt-Walther is an academic at the University of Toronto, serving as Program Director of the Cognitive Science Program and Department of Psychology . He investigates neural and computational mechanisms underlying high-level sensory perception, focusing on real-world scenes, mid-level vision, and visual aesthetics. Education: PhD in Computation and Neural Systems (Caltech, 2006), M.Phil (University of Cambridge) Research Focus: His lab employs fMRI, MEG, EEG , and GAN-generated stimuli to study scene categorization, perceptual organization, and aesthetic processing. Recent work explores curvature perception, emotion representation in scenes, and neural dissociations between computational and subjective visual metrics. Laboratory Members: The Bernhardt-Walther Lab includes PhD students like Gaeun Son (scene perception), Charlotte Leferink (scene representation), and Dela Farzanfar (aesthetic processing), alongside postdocs and collaborators. Advising: Supervises graduate students in projects combining computational modeling, psychophysics, and neuroimaging, particularly those with backgrounds in computer science or cognitive neuroscience.

Surya Ganguliمشاهده پروفایل

دانشیار

• Theoretical Neuroscience
• Computational Neuroscience
• Machine Learning
• +۴ مورد دیگر

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.

David Clewettمشاهده پروفایل

استادیار

• Emotion and memory
• Neuroscience of arousal
• Episodic memory
• +۴ مورد دیگر

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.

Andrew Yonelinasمشاهده پروفایل

استاد

• Human Memory
• Cognitive Neuroscience
• Memory Processes
• +۵ مورد دیگر

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.

• Computational Neuroscience
• Theoretical Neuroscience
• Reinforcement Learning
• +۴ مورد دیگر

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.