Dr. Gregor Schuhknecht is a Researcher and incoming Max-Planck Research Group Leader at the Max Planck Institute for Brain Research in Frankfurt, Germany. Starting December 2025, he will lead the Brain Algorithms and Circuits Group, focusing on computational neuroscience and neuronal circuitry in larval zebrafish. His work bridges experimental and computational approaches to understand brain algorithms enabling flexible behaviors. Education PhD in Neuroscience (2014–2019), Institute for Neuroinformatics, University of Zurich and ETH Zurich MSc in Biology (Neuroscience) (2012–2019), ETH Zurich BSc in Biosciences (2009–2012), Heidelberg University His research investigates how synaptic circuitry implements computational algorithms for sensorimotor processing, evidence accumulation, and behavior. Experimental approaches include functional imaging, optogenetics, connectomics, and computational modeling. The group will explore neuronal circuits across scales, from synapse to behavior, with a focus on biophysical properties and dynamic environmental adaptation. Recent publications highlight correlative light/electron microscopy, developmental activity-free circuit formation, and neocortical synapse analysis. Scientific Awards Meselson Prize (2024) Aspirational Neuroscience Award (2023) Best Publication Award (2021) Swiss National Science Foundation Fellowships (2021, 2019, 2018) Gregor Schuhknecht's lab will welcome graduate and postdoctoral researchers starting December 2025, focusing on experimental and computational systems neuroscience. He previously collaborated with Harvard University's Department of Molecular and Cellular Biology and the Department of Moritz Helmstaedter for connectomic analyses.
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.
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.
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.
Yize Zhao is an Associate Professor in the Department of Biostatistics at Yale School of Public Health and an Associate Professor in the Department of Biomedical Informatics & Data Science at Yale University. She holds affiliations with multiple Yale research centers including the Yale Center for Analytical Sciences, Yale Alzheimer's Disease Research Center, Yale Wu Tsai Institute, Yale Center for Brain and Mind Health, and Yale Computational Biology and Bioinformatics. Dr. Zhao's research focuses on developing statistical and AI methods to analyze large-scale complex biomedical data including medical imaging, genomics, and electronic health records. Her methodological expertise spans Bayesian statistics, feature selection, predictive modeling, data integration, missing data analysis, and network analysis. Her research interests span multiple biomedical domains with a strong focus on mental health, psychiatry, neurodegenerative diseases, and aging. Her recent work includes brain-to-behavior modeling, multi-layer biomedical networks, imaging genetics and genomics, and the integration of multi-modal biomedical data with real-world data. Dr. Zhao's work has resulted in numerous high-impact publications, with recent research focusing on Alzheimer's disease, brain network analysis, and advanced statistical methods for neuroimaging. Her publications show a strong trend toward integrating multi-modal data sources and developing sophisticated statistical approaches to address complex biomedical questions. Thelma and Marvin Zelen Emerging Women Leaders in Data Science Award from the Institute of Mathematical Statistics (IMS) COPSS Emerging Leader Award from the Committee of Presidents of Statistical Societies (COPSS) YSPH Investigator Research Award Yale Alzheimer's Disease Research Center Research Scholar Award Elected member of the International Statistical Institute Dr. Zhao serves as an Associate Editor for Biometrics and is a standing member of the NIH Biodata Management and Analysis (BDMA) study section. Her research is supported by multiple NIH grants, highlighting the significance and impact of her work in biostatistics and biomedical data science.
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.
Christian Bick is an Associate Professor at the Department of Mathematics, Vrije Universiteit Amsterdam (VU Amsterdam). He holds visiting roles as a Visiting Research Fellow at the University of Oxford's Mathematical Institute, Honorary Associate Professor at the University of Exeter, and Visiting Fellow at the Institute for Advanced Study (TUM-IAS), Technische Universität München. His research focuses on dynamical systems and applications, particularly in network dynamics, coupled oscillator networks, and higher-order interactions. He has received prestigious awards such as the Marie Curie Intra-European Fellowship (2015) and the Hans Fischer Fellowship (2019). Education and Career: Bick obtained his PhD from Georg-August-Universität Göttingen (2012) and held postdoctoral positions at Rice University and the University of Exeter. His work bridges theoretical and applied mathematics, with interdisciplinary collaborations in neuroscience, physics, and engineering. Research Interests: Bick explores dynamics of coupled oscillator networks, asynchronous networks, and higher-order interactions. His recent work includes studies on heteroclinic networks, chimera states, and synchronization phenomena in complex systems. He leads projects like BeyondTheEdge (Marie Skłodowska–Curie Doctoral Network) and has contributed to grants such as the EPSRC New Investigator Award (2020–2023). Teaching: He teaches Dynamical Systems at VU Amsterdam and has lectured on Stochastic Processes, Mathematical Methods, and Dynamical Systems and Chaos at the University of Exeter and Oxford. Awards and Grants: His honors include the DAAD Doktorandenstipendien (2010, 2012) and Fulbright support (2008). Active grants include projects on higher-order networks and neurodegenerative disease modeling.
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.
Steven Meikle is a Professor of Medical Imaging Physics and Head of the Imaging Physics Laboratory at the Brain and Mind Centre, University of Sydney. He also serves as Deputy Director (Preclinical) of Sydney Imaging and Deputy Director of the National Imaging Facility's Sydney node. His expertise spans advanced imaging technologies, with a focus on PET/SPECT instrumentation and molecular imaging. He holds a B.App.Sc.(Hons) from the University of Technology Sydney and a PhD from the University of New South Wales. Research focuses include developing novel PET systems like Open-field PET (for freely moving rodents) and Total Body PET, which enhance imaging sensitivity and enable real-time behavioral studies alongside brain function analysis. Collaborations include Tsinghua University (China) and UC Davis (USA). He leads projects on motion correction, quantitative imaging, and AI-driven analysis. Key achievements include over 180 peer-reviewed publications, editorial roles in Physics in Medicine and Biology , and leadership in professional societies. Awards include IEEE Senior Membership and Australian Institute of Physics Fellowship. Current student projects explore Total Body PET applications, motion correction, and radiopharmaceutical evaluation. Teaching roles include medical physics courses in diagnostic radiography and medical physics programs. He advises on imaging ethics, facility implementation, and translational research bridging basic science and clinical applications.
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.
Professor Fernando Calamante is a Professor of Biomedical Engineering at The University of Sydney and Director of Sydney Imaging Core Research Facility. He leads the National Imaging Facility node and focuses on advanced MRI methodologies, particularly Diffusion and Perfusion MRI, to study brain connectivity and neurological disorders. His work includes developing the MRtrix software, widely used in diffusion MRI analysis. He holds extensive funding (~$50M) and has been recognized with awards like ISMRM Fellowship and NHMRC grants. His research spans super-resolution imaging, brain connectomics, and clinical applications in stroke and tumors. Education: BSc (Physics, Argentina), PhD (Magnetic Resonance Imaging, University College London). Career highlights include leadership roles at The Florey Institute and ISMRM presidency (2021-2022). Research interests include: Novel MRI methods for brain connectivity and super-resolution imaging Applications of Diffusion and Perfusion MRI in neurology Integration of structural and functional connectomics Key achievements: Over 200 publications, software innovations, and leadership in global MRI societies.
Fred R. Volkmar, M.D., is the Irving B. Harris Professor of Child Psychiatry, Pediatrics, and Psychology at Yale University, and Director of the Yale Child Study Center. He also serves as Chief of Child Psychiatry at Yale-New Haven Hospital. His academic affiliations include the Yale School of Medicine and leadership roles in multiple clinical and research programs. Dr. Volkmar holds an M.D. from Stanford University (1976) and a psychology master’s degree from the same institution, alongside an undergraduate psychology degree from the University of Illinois (1972). He is renowned for authoring the DSM-IV autism and pervasive developmental disorders section and has authored hundreds of publications and several influential books, including Asperger’s Syndrome and Handbook of Autism . His research focuses on autism spectrum disorders (ASD), including diagnosis, treatment, comorbidities, and transition to adulthood. His publications emphasize evidence-based practices, neurodevelopmental mechanisms, and clinical guidelines. He leads initiatives in medical care for ASD populations and advocates for improved community support systems. Notable contributions include co-developing the M-CHAT screening tool and advancing interdisciplinary approaches to ASD management. His work intersects clinical practice, policy, and education, prioritizing patient-centered care and reducing stigma.
Essa Yacoub is a Professor in the Department of Radiology at the University of Minnesota, affiliated with the PhD Program in Medical Physics and the Center for Magnetic Resonance Research. His work focuses on advancing MRI and fMRI technologies, particularly at ultrahigh magnetic fields (e.g., 10.5 T), to achieve unprecedented spatial and temporal resolution in brain imaging. He leads projects in RF coil design, noise reduction algorithms, and developmental neuroimaging. Roles: Professor, Medical Physics Program Faculty Affiliations: Center for Magnetic Resonance Research, Department of Radiology Research emphasizes high-resolution fMRI applications, including layer-specific brain mapping, pediatric neurodevelopment studies (e.g., Baby Connectome Project), and translational tools like BIBSNet for infant brain segmentation. His innovations bridge hardware engineering (RF coils) and software (denoising pipelines) to tackle challenges in mesoscopic-scale imaging. Key contributions include optimizing imaging protocols at 7T/10.5T, developing NORDIC denoising for submillimeter data, and advancing understanding of brain networks in aging and neurological disorders. His work is foundational for large-scale initiatives like the Human Connectome Project and non-human primate neuroimaging collaborations. Grants and collaborations focus on translational imaging technologies, while educational contributions include training through the Medical Physics PhD Program. Ongoing efforts aim to refine ultra-high field MRI applications for clinical and basic neuroscience research.
