جست‌وجوی استاد و پژوهشگر

Christopher Honeyمشاهده پروفایل

دانشیار

• Computational Neuroscience
• Cognitive Neuroscience
• Neural Dynamics
• +۳ مورد دیگر

Christopher Honey is an Associate Professor in the Department of Psychological & Brain Sciences at Johns Hopkins University, affiliated with the Krieger School of Arts & Sciences. His research focuses on computational cognitive neuroscience, exploring how the brain processes sequential information such as language and memory. He holds a PhD from Indiana University and has held positions at Princeton University and the University of Toronto before joining JHU in 2016. Education: PhD in Psychological and Brain Sciences, Indiana University Postdoctoral Fellowship at Princeton University with Uri Hasson Bachelor’s in Applied Mathematics and English Literature, University of Cape Town Research Interests: Neural dynamics of memory and perception Temporal processing in the brain Cognitive modeling using computational methods Neuroimaging data standards (e.g., BIDS) Publications highlight his work on brain state fluctuations, neuroimaging data structures, and memory enhancement. His lab develops tools for analyzing fMRI and EEG data, emphasizing real-world applications like smartphone-based cognitive interventions. Lab and Collaborations: Active projects on narrative processing and hippocampal replay Development of open-source neuroscience tools like iELVis Focus on translational research for aging populations

Saud Alhusainiمشاهده پروفایل

استادیار

• Neurology
• Movement Disorders
• Parkinson's Disease
• +۴ مورد دیگر

Saud Alhusaini MD PhD is an Assistant Professor of Neurology at the Warren Alpert Medical School of Brown University and serves as a Neurologist/Movement Disorders Specialist at Rhode Island Hospital. His research integrates imaging genomics and multimodal brain imaging approaches to investigate neurological disorders including Parkinson's disease, essential tremor, and epilepsy. He is affiliated with the Carney Institute for Brain Science and collaborates extensively with clinicians, geneticists, electrophysiologists, MRI specialists, neuropsychologists, and data scientists. Education: PhD from the Royal College of Surgeons in Ireland (RCSI) MSc in Neuroscience from Trinity College Dublin MD from University of Dublin, School of Medicine Adult neurology residency at McGill University/Montreal Neurological Institute Clinical research fellowship at Yale School of Medicine Clinical fellowship at Stanford University Medical Center Dr. Alhusaini's research focuses on identifying key endophenotypes and subclinical biomarkers to elucidate the underlying mechanisms of complex neurological conditions. His work spans multiple areas including movement disorders, epilepsy, and brain structure genetics. He has made significant contributions to understanding the genetic architecture of brain structures through his involvement with the ENIGMA consortium, which conducts large-scale collaborative analyses of neuroimaging and genetic data across institutions worldwide. An analysis of his publication record reveals a consistent pattern of high-impact research at the intersection of neurology, genetics, and advanced imaging techniques. His recent work demonstrates particular expertise in Parkinson's disease genetics, epilepsy network analysis, and movement disorder diagnostics. The breadth of his research, spanning from basic genetic mechanisms to clinical applications, highlights his comprehensive approach to understanding neurological disorders. Dr. Alhusaini has received funding from the Rhode Island Research Foundation, Brown Physicians, Inc., and Advance RI-CTR to support his research initiatives. His collaborative approach is evident through his numerous multi-institutional projects and extensive co-author network across Brown University departments including Neurology, Neurosurgery, and Pathology and Laboratory Medicine.

• Neuroscience
• Machine Learning
• Neuroimaging
• +۴ مورد دیگر

Dustin Scheinost is an Associate Professor at Yale School of Medicine, affiliated with the Department of Radiology & Biomedical Imaging, Yale Child Study Center, Department of Statistics, and Yale Biomedical Imaging Institute. His research focuses on connectomics , machine learning , and neuroinformatics through the Multi-modal Imaging, Neuroinformatics, & Data Science (MINDS) Lab. Radiology & Biomedical Imaging (Primary) Child Study Center (Secondary) Statistics (Secondary) Wu Tsai Institute Yale Stress Center Research Interests include developing novel statistical and machine learning methods for functional connectivity in big neuroscience data, leading the BioImage Suite Web (BISWeb) platform, and advancing early life neuroimaging through the Fetal, Infant, Toddler Neuroimaging Group (FIT’NG). His work is supported by grants from NIMH, NIAA, NIDA, and NHLBI. Selected Scientific Contributions span functional connectivity in laterality preferences, anti-racist AI governance in psychiatry, self-citation trends in neuroscience, and predictive modeling of mood disorders. He collaborates extensively with Todd Constable and others on multimodal neuroimaging studies.

Teresa Cheungمشاهده پروفایل

استاد مدعو

• Biomedical Engineering
• Neuroimaging
• Magnetoencephalography (MEG)
• +۴ مورد دیگر

Teresa Cheung is an Adjunct Professor in the Department of Engineering Science at Simon Fraser University’s Faculty of Applied Sciences. Her research focuses on neuroimaging techniques, particularly magnetoencephalography (MEG), and their applications to understanding brain networks in health and disease. She holds a Ph.D. in Physics from SFU (2012) and completed a postdoctoral fellowship at the University of Cambridge (2012–2013). Research interests include: MEG instrumentation and optically pumped magnetometers (OPM) Cortical-cerebellar networks and cerebellar activity localization Neuroimaging of neurological disorders like major depressive disorder and epilepsy Functional and structural connectome analysis across the human lifespan Multimodal integration of MEG, MRI, fMRI, and DTI data Recent work emphasizes the relationship between cardiovascular health, brain aging, and cognitive resilience. Her studies span clinical applications (e.g., depression biomarkers) and technical advancements in neuroimaging systems. Collaborations include multi-site studies on depression and aging cohorts like the Cam-CAN project. Publications highlight innovative methods in MEG system design, neural network dysfunction analysis, and lifespan brain dynamics. Her work bridges engineering, neuroscience, and clinical research to advance non-invasive brain imaging and neurophysiological understanding.

James C. Geeمشاهده پروفایل

استاد

• Biomedical Image Analysis
• Medical Imaging Computing
• Image Segmentation
• +۵ مورد دیگر

James C. Gee is a Professor of Radiologic Science in Radiology at the University of Pennsylvania's Perelman School of Medicine. He serves as Director of the Penn Image Computing and Science Laboratory and Co-Director of the Translational Biomedical Imaging Center , with affiliations in Bioengineering and Applied Mathematics graduate groups. His research focuses on biomedical image analysis, specialization in segmentation, registration, and morphometry applied to neurodegenerative diseases and multi-organ systems. Education : B.S. in Computer Science/Electrical Engineering (University of Washington, 1987), Ph.D. in Computer and Information Science (University of Pennsylvania, 1996) Research : Quantitative medical imaging methods, brain connectomics, neurodegeneration mapping, and translational imaging technologies Publications : 15+ recent works on AI-driven image analysis for Alzheimer's disease, cardiac amyloidosis, and radiomics applications Leadership : Directs MSE-DS Online Degree Program, co-chairs Radiology DCOAP Committee, and founded RISE (Radiology Initiative to Support Inclusive Excellence) His laboratory develops advanced computational tools like ITK-SNAP for biomedical imaging, with applications in both in vivo clinical imaging and ex vivo histology . The work spans cross-disciplinary collaborations in computer science, neuroscience, and clinical medicine.

• Artificial Intelligence
• Computational Biology
• Graph Neural Networks
• +۵ مورد دیگر

Pietro Liò is Full Professor in the Department of Computer Science and Technology at the University of Cambridge, where he leads research in Artificial Intelligence and Computational Biology as part of the AI group and the Cambridge Centre for AI in Medicine. He holds additional affiliations as Fellow and Council member of Clare Hall College, member of Ellis (European Lab for Learning & Intelligent Systems), and member of Academia Europaea. Professor Liò earned dual PhDs in Complex Systems and Non Linear Dynamics from the University of Florence and in Theoretical Genetics from the University of Pavia, Italy. His educational background bridges theoretical computer science with biological sciences, forming the foundation for his interdisciplinary research approach. His research focuses on developing Artificial Intelligence and Computational Biology models to understand disease complexity and advance personalized medicine. Current work emphasizes Graph Neural Network modeling for integrating multi-scale, multi-omics, and multi-physics data; combining deep learning with mechanistic approaches; explainability in medical AI; and developing AI-based medical digital twins and personal decision support systems. His work spans from fundamental algorithm development to clinical applications, with particular emphasis on translating computational advances into medical solutions. Analysis of his recent publications reveals strong activity in geometric deep learning , explainable AI for healthcare , and multi-omics integration , with increasing focus on clinically applicable tools that maintain both predictive power and interpretability. Member of Academia Europaea Listed among Top Italian Scientists by VIA-Academy Professor Liò has mentored over 40 PhD students and postdoctoral researchers, including notable names such as Petar Velickovic, David Buterez, and Chaitanya Joshi. His research is supported through collaborations with the Cambridge Centre for AI in Medicine and various international partnerships. He serves on departmental committees including Student Complaints and Postdoc Mentoring, and has completed equality and diversity training essentials. He leads research within the Artificial Intelligence group at Cambridge, focusing on creating computational frameworks that bridge biological complexity with clinical applications through advanced machine learning techniques.

Sebastian Seungمشاهده پروفایل

استاد

• Neuroscience
• Machine Learning
• Connectomics
• +۵ مورد دیگر

Sebastian Seung is a Professor at Princeton University , affiliated with both the Department of Computer Science and the Princeton Neuroscience Institute . His career spans Harvard University (Ph.D., 1990), Bell Laboratories, and Massachusetts Institute of Technology before joining Princeton in 2014. An External Member of the Max Planck Society and 2008 Ho-Am Prize recipient, Seung merges machine learning with neuroscience . Research Focus : Pioneering connectomics , Seung developed technologies for reconstructing neural circuits from high-resolution brain images, including FlyWire for collaborative brain mapping. His work explores brain function, development, and plasticity , drawing parallels between fly visual systems and convolutional networks . Awards & Affiliations : 2008 Ho-Am Prize in Engineering External Member, Max Planck Society Technical Contributions : Led breakthroughs in 3D connected component labeling and high-throughput EM imaging for mammalian brains, partnering with NIH’s BRAIN Initiative to scale connectomics to whole mouse brains. Seung’s team has shifted from EM analysis to interpreting connectomes , focusing on neural circuit function and biological mechanisms in flies and mice. His lab alumni network spans institutions, advancing AI and neuroscience globally.

Michael Economoمشاهده پروفایل

استادیار

• Systems Neuroscience
• Motor Control
• Neural Circuits
• +۳ مورد دیگر

Michael N. Economo, PhD, is an Assistant Professor in the Department of Biomedical Engineering at Boston University. His research focuses on neural circuits controlling movement, leveraging cutting-edge optical, electrophysiological, and genetic tools. He holds affiliations with Neuroscience & Neuroengineering and Photonics & Optical Systems programs. Education: PhD in Biomedical Engineering from Boston University, B.S. Biomedical Engineering and B.A. Mathematics from Duke University. Research Interests: Systems neuroscience, motor control, long-range neural circuits, computational neuroscience, neurotechnology. His lab investigates how neural circuits across brain regions coordinate movement using advanced techniques like optogenetics, in vivo imaging, and transcriptomics. Key Projects: Orofacial motor control, voltage imaging with TICO microscopy, neural circuit dissection. Technologies: Neuropixels probes, fluorescent voltage indicators, single-cell RNA sequencing. Notable Awards: NSF CAREER (2023), Scialog Fellow (2023), Whitehall Foundation Young Investigator Award (2021). Advising & Grants: Supervises graduate students (e.g., Munib Hasnain, Jackie Birnbaum) and postdocs (e.g., Vicky Moya, Yujin Han). Lab members focus on motor planning, neuromodulation, and neural dynamics. Labs/Teams: The Economo Lab collaborates on neurotechnology development and integrates interdisciplinary approaches from engineering and biology. Affiliated with BU’s Graduate Program in Neuroscience.

• Neuroimaging
• Alzheimer's Disease
• Neurodegenerative Disorders
• +۵ مورد دیگر

Dr. Elisenda Bueichekú serves as a Research Scientist at the Yale School of Medicine within the Radiology & Biomedical Imaging Department . Her work focuses on neuroimaging applications in neurodegenerative disorders, particularly Alzheimer’s disease progression and memory systems analysis. She collaborates with leading researchers in the Mental Health PET Radioligand Development (MHPRD) Program and the PET Core facility. Primary Affiliation: Yale School of Medicine Department: Radiology & Biomedical Imaging Research Programs: MHPRD Program, PET Core Her research integrates multimodal neuroimaging techniques to investigate: Tau pathology propagation patterns Connectome-based disease modeling Cortical hub analysis in memory systems Cognitive resilience mechanisms Neuroimaging of anosognosia Functional network contributions to creativity Dr. Bueichekú contributes to translational research through: Advanced PET/MRI methodologies Spatiotemporal disease progression mapping Development of imaging-based biomarkers Recent publications demonstrate expertise in: Alzheimer’s disease neuroimaging Tau and amyloid co-accumulation Functional connectivity analysis Memory system characterization Scientific contributions include: 2025 Center for Brain & Mind Health Pilot Grant Key publications in top-tier journals (Nature Aging, Alzheimer's & Dementia, etc.)

Koen Haakمشاهده پروفایل

دانشیار

• Vision Science
• Neuroimaging
• Machine Learning
• +۴ مورد دیگر

Dr. Koen Haak is an Associate Professor at Tilburg University's Department of Cognitive Science and Artificial Intelligence within the Tilburg School of Humanities and Digital Sciences. His research focuses on vision science, neuroimaging, and AI applications in healthcare. He leads projects like 'Bridging the gap between visual function and functional vision' (NWO Vidi) and 'Neuroimaging biomarkers for predicting vision training success after stroke' (NWO KIC). He collaborates with institutions such as the Donders Institute and the Lifelong Vision Consortium. His work contributes to UN SDGs related to health and innovation. Research interests include analyzing brain imaging data to predict functional vision outcomes, developing machine learning tools for clinical trials, and studying visual cortex plasticity. He has authored 51+ publications, including papers in Nature Neuroscience and Translational Psychiatry . Awards include NWO Veni (2016) and Vidi (2020) fellowships. He teaches courses on computer vision and AI at Tilburg University. Current projects explore predictive analytics for eye treatments, thalamocortical connectivity, and sleep disruption effects in maritime pilots. His lab develops methods like connectopic mapping and deep learning for MRI analysis, with applications in Alzheimer's, autism, and psychiatric disorders.

Mikail Rubinovمشاهده پروفایل

استادیار

• Network Neuroscience
• Computational Neuroscience
• Brain Connectivity
• +۴ مورد دیگر

Mikail Rubinov serves as Assistant Professor of Biomedical Engineering (primary appointment), Computer Science, Psychiatry, and Psychology at Vanderbilt University's School of Engineering. His interdisciplinary work bridges computational neuroscience, network science, and clinical applications. His research focuses on integrative statistical models of large-scale neural data , exploring brain network organization across species and scales. Key interests include evolutionary principles of brain networks, transcriptomic basis of neural individuality, information transfer in neural systems, and neuropsychiatric connectivity phenotypes. The Rubinov Lab develops computational frameworks for analyzing complex neural systems and integrates neuroscientific knowledge with multi-omics data. Recent publications reveal strong trends in network neuroscience methodology development (circular analysis frameworks, unbiased sampling techniques) and translational applications (epilepsy networks, autism spectrum connectomics, gut-brain axis interrogation). His work increasingly incorporates transcriptomic data with neuroimaging at biobank scale. NIH Grant Writing Workshop (June 2022) NIH Workshop Short Talks (April 2023) Rubinov actively mentors graduate and undergraduate students across Biomedical Engineering and Computer Science. His lab maintains collaborations with UCSF, HHMI Janelia Research Campus, Weizmann Institute, and international neuroscience consortia. Current projects include integrative models of large-scale neural data and transcriptomic basis of neural individuality. The Rubinov Lab operates within Vanderbilt's Department of Biomedical Engineering with extensive cross-school collaborations. Technical resources include GitHub repositories for constraint network models (cnm-code), volumetric segmentation (voluseg), and brain connectivity toolboxes.

Dani S. Bassettمشاهده پروفایل

استاد

• Network Science
• Complex Systems
• Brain Connectivity
• +۵ مورد دیگر

Dani S. Bassett is the J. Peter Skirkanich Professor at the University of Pennsylvania with primary appointment in the Department of Bioengineering (School of Engineering and Applied Science) and secondary appointments in Physics & Astronomy, Electrical & Systems Engineering, Neurology, and Psychiatry. They serve as an external professor at the Santa Fe Institute and lead a research group focused on complex systems and network science. B.S. in Physics, Penn State University (2004) Ph.D. in Physics, University of Cambridge as Churchill Scholar and NIH Health Sciences Scholar (2009) Postdoctoral position at UC Santa Barbara and Junior Research Fellow at Sage Center for the Study of the Mind Their research integrates complex systems science, statistical mechanics, and applied mathematics to study network dynamics in physical and biological systems. Key areas include brain connectivity mechanisms, cognitive processes, neurological disease modeling, granular matter physics, and collective human curiosity. Bassett employs advanced methodologies including algebraic topology, network control theory, and multilayer network analysis to investigate how network architecture influences system function across diverse domains. Recent publications reveal a strong trend toward interdisciplinary network science applications, particularly in modeling human curiosity through Wikipedia navigation patterns and analyzing brain network reconfiguration during cognitive development. Their work bridges physics, neuroscience, and behavioral science with emphasis on topological network properties and dynamical processes. American Psychological Association's Rising Star (2012) MacArthur Fellow Genius Grant (2014) Lagrange Prize in Complex Systems Science (2017) Erdos-Renyi Prize in Network Science (2018) American Physical Society Fellow (2021) Web of Science Highly Cited Researcher (3 consecutive years) Bassett's research is supported by major agencies including NSF, NIH, DoD, ONR, and private foundations (MacArthur, Sloan, Paul Allen). Their lab actively recruits students from physics, engineering, neuroscience, and computer science backgrounds, emphasizing diversity in academic perspectives. Current projects include the 'Curious Minds' initiative exploring collective knowledge building and network-based models of neurological disorders. Bassett co-authored the MIT Press book 'Curious Minds: The Power of Connection' with philosopher Perry Zurn.

• Topology of Complex Systems
• Higher-order Networks
• Topological Neuroscience
• +۷ مورد دیگر

Giovanni Petri is a Professor in the Network Science Institute at Northeastern University London, where he joined in June 2023. Previously, he held positions at CENTAI as a Principal Researcher and at IMT Lucca as a Guest Scholar, with earlier affiliations at ISI Foundation and Imperial College London. His educational background includes a PhD in Complex Networks from Imperial College London (2012), an MSc in Theoretical Physics from the University of Pisa (2008), and a BSc in Physics from the University of Pisa (2005). Petri's research spans the analysis of neuroimaging data and AI systems with topological techniques, the formalization of cognitive control models with tools of statistical mechanics and network theory, and the study of the predictability of socio-technical systems. His work in Topological Neuroscience explores brain architecture using algebraic topology, while his research in Cognitive Neuroscience focuses on neural mechanisms underlying human cognition. He is particularly known for his work on higher-order networks, using mathematical frameworks like hypergraphs and simplicial complexes to model systems with multi-way interactions. His recent publications (2023-2025) demonstrate a strong focus on higher-order network theory applied to neuroscience, with particular emphasis on topological approaches to brain connectivity, social contagion models, and the physics of complex systems. These works reveal consistent themes in understanding how multi-body interactions shape system dynamics across biological, social, and technological domains. European Research Council Consolidator Grant (RUNES: Reconstruction and unification of neural and ecological systems, 2024) As Principal Investigator of the NPLab, Petri advises numerous PhD and postdoctoral researchers including Marilyn Gatica, Andrea Santoro, and Simone Poetto. His RUNES project, funded by the ERC Consolidator Grant, represents a significant research initiative. The lab maintains active collaborations with CENTAI, Project CETI (Cetacean Translation Initiative), and various international institutions. The NPLab investigates the role of topology and geometry in the collective dynamics of complex systems, ranging from neuroscience to society, using statistical mechanics, algebraic topology, and innovative computational approaches. Current projects include Topological Neuroscience, Cognitive Neuroscience, Higher-order Networks, Project CETI, and RUNES.

Markus Axerمشاهده پروفایل

استاد

• Connectome
• Neuroimaging
• Polarization Microscopy
• +۳ مورد دیگر

Prof. Markus Axer is a Professor and Deputy Head of the Structural and Functional Organisation of the Brain (INM-1) at the Institute of Neuroscience and Medicine (INM) within Forschungszentrum Jülich. His research focuses on connectomics, neuroimaging technologies (e.g., 3D-Polarized Light Imaging), and high-performance computing applications in brain architecture analysis. He leads the 'Fiber Architecture' working group, advancing microscopy techniques like scattered light imaging and MRI-histology correlation for studying brain microstructure. His work bridges experimental neuroscience with computational methods, aiming to decode brain organization at meso- and macroscales. Key achievements include developing the HippoMaps atlas of the human hippocampus and improving fiber orientation mapping in brain tissue. Awards include Fellowship in the Royal Netherlands Academy of Arts and Sciences (2024). Research emphasizes cross-modal data integration, with applications in Alzheimer’s disease biomarker validation and primate brain evolution studies. He collaborates with academic institutions like the University of Wuppertal and contributes to international initiatives like the BigBrain Analytics Learning Laboratory.

Genevera Allenمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Interpretable Machine Learning
• Data Integration
• +۸ مورد دیگر

Genevera Allen is an Associate Professor of Electrical and Computer Engineering, Statistics, and Computer Science at Rice University. She is also an Investigator at the Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital and Baylor College of Medicine, and the Founding Director of the Rice D2K Lab, a hub for data science education and real-world problem-solving. Education: Ph.D. in Statistics from Stanford University (2010), B.A. in Statistics from Rice University (2006). Her research develops statistical machine learning tools for reproducible discoveries in neuroscience and bioinformatics, focusing on interpretable models, graphical networks, and data integration. She pioneers methods for high-dimensional and multi-modal data, including convex clustering, sparse PCA, and fairness-aware algorithms. The 15 most recent publications span statistical machine learning theory, neuroscience applications, and bioinformatics. Key trends include graphical model estimation, convex optimization for clustering, latent variable adjustment, and ethical AI frameworks. Applications emphasize neuroimaging and single-cell genomics. Scientific Awards: NSF Career Award (2016), Duncan Achievement Award (2021), Curriculum Innovation Award (2020), Research and Teaching Excellence Award (2017), Forbes 30 Under 30 (2014), elected Fellow ASA (2022), Member ISI (2021). Dr. Allen leads the Rice D2K Lab, which connects students with industry and academic data science projects. She serves as Editor for the Journal of Machine Learning Research and Springer Texts in Statistics . Her teaching innovations include client-sponsored capstone programs and courses on machine learning.