جست‌وجوی استاد و پژوهشگر

Yuuki Obataمشاهده پروفایل

استادیار

• Gut Microbiota Research
• Immune System Regulation
• Enteric Nervous System Development
• +۳ مورد دیگر

Dr. Yuuki Obata is an Assistant Professor in the Department of Immunology and Neuroscience at UT Southwestern Medical Center, USA. He holds an Investigator role at the O'Donnell Brain Institute. Previously, he served as a postdoctoral fellow at the Francis Crick Institute (2016-2021) and the University of Tokyo (2015-2016). His research focuses on how environmental signals (microbiota, diet, circadian rhythms) shape intestinal neural circuits and immune networks. He employs advanced techniques including imaging, viral tracing, gnotobiotic systems, and multi-omics approaches. Education: PhD in Immunology from Chiba University (RIKEN Joint Graduate School Program), Japan. Postdoctoral training included JSPS Superlative Postdoctoral Fellowships (Japan) and EMBO/HFSP fellowships (UK). Research interests span microbiota-immune interactions, enteric nervous system development, gut-brain axis communication, and epigenetic regulation of immune responses. His lab investigates mechanisms linking microbial metabolites to host physiology, particularly in metabolic and neural contexts. Key findings include microbiota's role in IgA responses, T cell differentiation, and neuronal programming. Collaborations emphasize inter-organ communication and translational applications in immunology and neuroscience. Laboratory: The Obata Lab at UT Southwestern employs interdisciplinary methods to address how environmental factors reprogram gut neural and immune systems. Future work includes exploring microbiota-based therapies and neuroimmune interactions in disease models.

• Artificial Intelligence
• Cybersecurity
• Machine Learning
• +۷ مورد دیگر

Vahid Behzadan is an Assistant Professor in Data Science and Computer Science at the University of New Haven's Tagliatela College of Engineering. He leads the SAIL Lab, focusing on AI safety and security, particularly in autonomous systems like driverless cars and smart cities. His work addresses adversarial attacks on machine learning and reinforcement learning, with applications in cybersecurity and healthcare. Behzadan has held prior positions at Kansas State University, University of Nevada Reno, and University of Birmingham, UK. He holds a Ph.D. in Computer Science and an M.S. from the University of Nevada Reno, and a B.Eng. from the University of Birmingham. His research spans AI ethics, cybersecurity, and complex systems. Behzadan advises the UNH hacking team and actively participates in policy initiatives, including Connecticut's AI Working Group and the Connecticut AI Alliance. He has contributed to over 30 peer-reviewed articles and frequently engages in media discussions on topics like AI safety, facial recognition, and cybersecurity threats. Key research areas include adversarial machine learning, AI forensics, and ethical AI design. His work bridges theoretical advancements with real-world applications in transportation, healthcare, and national security. Behzadan collaborates with organizations such as the Transportation Research Laboratory (TRL) and Open Web Application Security Project (OWASP).

• Neuroscience
• Artificial Intelligence
• Neuronal Modeling
• +۵ مورد دیگر

Eilif B. MULLER is a Professor in the Department of Neurosciences at Université de Montréal, Principal Investigator of the Architectures of Biological Learning Lab (ABL-Lab) at CHU Sainte-Justine Research Center, and Associate Faculty at Mila (Quebec AI Institute). His work bridges neuroscience and artificial intelligence, focusing on understanding how sensory perception is learned in the neocortex through biophysical simulations and deep learning models. He holds affiliations with IVADO (Institute for Data Valorization) and contributes to strategic initiatives like the UNIQUE Québec Center. His research integrates empirical neurophysiology with computational models, exploring dendritic processing and synaptic plasticity to inform both biological understanding and AI advancements. Teaches NSC-6044 and NSC-6045 (Neuroscience Colloquia) at Université de Montréal. Leads projects on neocortical learning mechanisms and their implications for neurodevelopmental disorders. Recipient of grants from CRSNG (Natural Sciences and Engineering Research Council), FRSQ (Health Research Fund), and institutional funding. Publications span topics in computational neuroscience, neural network modeling, and interdisciplinary AI-neuroscience research. Collaborates extensively across institutions to advance large-scale brain simulations and data-driven models.

Dana Brooksمشاهده پروفایل

استاد پژوهشی

• Biomedical Signal Processing
• Medical Imaging
• Statistical Signal Processing
• +۸ مورد دیگر

Dana Brooks is a Research Professor in the Department of Electrical and Computer Engineering at Northeastern University, with affiliations in Bioengineering. He holds a PhD from Northeastern University (1991) and has received the Søren Buus Outstanding Research Award (2006). His primary research focuses on biomedical signal and image processing, medical imaging techniques (including MRI and electrocardiography), and neuromodulation technologies such as transcranial magnetic stimulation (TMS). He is also involved in protein conformation estimation using X-ray scattering and optimization algorithms for medical applications. Dr. Brooks leads the Biomedical Signals Processing Lab and collaborates with the Center for Integrative Biomedical Computing . His work bridges engineering and medicine, with recent grants including a $400K NSF MRI grant for advanced TMS systems and a $600K NSF grant for motor cortical organization studies. He has advised students like Setareh Ariafar (PhD’20) and contributed to innovations in image mosaicking for confocal microscopy and machine learning applications in dermatology. His publications span computational neuroscience, cardiac imaging, and uncertainty quantification in biomedical simulations. Notable achievements include developing algorithms for ECG imaging, optimizing TMS protocols, and creating tools like UncertainSCI for simulation reliability assessment.

Mark Kramerمشاهده پروفایل

استاد

• Neuroscience
• Computational Neuroscience
• Mathematical Modeling of Neural Activity
• +۵ مورد دیگر

Mark Kramer is a Professor in the Department of Mathematics & Statistics at Boston University. He belongs to the Applied Mathematics research group, focusing on mathematical, statistical, and machine learning approaches to characterize brain activity. His work bridges data-driven neuroscience with computational methods, exploring topics like biophysical models of neurons, field models of neural populations in epilepsy, and theoretical questions about brain rhythms. His research interests include: Biophysical modeling of single-neuron dynamics Neural population activity in pathological states Machine learning for detecting abnormal brain rhythms Analysis of cross-frequency coupling and coherence Kramer has developed educational resources like Case Studies in Neural Data Analysis using both MATLAB and Python. These materials teach practical data analysis techniques for spike trains and field data, emphasizing hands-on implementation over theoretical mathematics. He has received funding from NIH and NSF for computational neuroscience projects. His recent publications focus on epilepsy research, sleep spindle analysis, and neural signal processing. The work spans from developing statistical frameworks to understanding network dynamics in seizure termination and exploring phase consistency in neural data. Notably, his coherence studies revealed non-intuitive coupling patterns between brain regions, demonstrating that low-amplitude rhythms can be more informative than dominant ones.

Paul Marcoglieseمشاهده پروفایل

استادیار

• Neurological Disease Mechanisms
• Molecular Genetics
• Functional Genomics
• +۷ مورد دیگر

Dr. Paul Marcogliese is an Assistant Professor in the Department of Biochemistry & Medical Genetics at the University of Manitoba’s Max Rady College of Medicine. He leads the Marcogliese Lab, focused on understanding the molecular and cellular mechanisms underlying neurological disorders such as autism, Parkinson’s disease, and rare conditions like NEDAMSS. His work employs Drosophila melanogaster and mouse models to assess genetic variants and develop therapeutic strategies. Dr. Marcogliese holds a PhD in Neuroscience from the University of Ottawa and completed postdoctoral training at Baylor College of Medicine, where he studied neurodegenerative disease mechanisms. His research integrates functional genomics, disease modeling, and drug screening to identify genetic drivers of neurological disorders and potential treatments. Key research interests include: Functional assessment of human disease variants using Drosophila and murine models Role of Wnt signaling in neurodegeneration and neurodevelopmental disorders Identification of novel therapeutic targets for rare and common neurological conditions Dr. Marcogliese is affiliated with the Children’s Hospital Research Institute of Manitoba (CHRIM) and co-leads the ENRRICH and MGM research groups. He currently accepts Master’s and PhD students. His lab has secured grants from Brain Canada and the Manitoba Medical Service Foundation, among others. Recent achievements include the 2023 Shaun Lamoureux Legacy Award for grant writing and the 2024 Brain Canada Future Leaders award. His work has been published in Genome , Neurobiology of Disease , and other high-impact journals.

Elena A. Eroshevaمشاهده پروفایل

استاد

• Hierarchical Modeling
• Longitudinal Data Analysis
• Latent Variable Modeling
• +۴ مورد دیگر

Elena A. Erosheva is a Professor in the Department of Statistics and Social Work at the University of Washington , where she also serves as the Associate Director of the Center for Statistics and the Social Sciences (CSSS). Her research focuses on hierarchical modeling , longitudinal data analysis , and latent variable models , with applications in social sciences, disability research, and health statistics. She has taught courses like Multivariate Data Analysis, Sample Survey Techniques, and Statistical Modeling with Latent Variables. Education : PhD in Statistics, University of Washington (2002) Current Research : Measuring gender-based homophily in collaborations, modeling life course transitions, partial mastery cognitive diagnosis models, and bias detection in grant review Scientific Awards : 2019 NIH Center for Scientific Review First Prize for Most Creative Idea for Detection of Bias in Peer Review 2013 Mitchell Prize, International Society for Bayesian Analysis 2015 ASA Student Paper Award (with Maryclare Griffin) Her students and postdoctoral researchers include Sheridan Grant, Y. Samuel Wang, and Jonathan Gruhl. She uses R software extensively in her teaching and research. Contact: erosheva@uw.edu | Personal Website

Mia Liljeströmمشاهده پروفایل

پژوهشگر

• Magnetoencephalography
• Functional Connectivity
• Brain Networks
• +۳ مورد دیگر

Mia Liljeström is a Staff Scientist at the Department of Neuroscience and Biomedical Engineering, Aalto University. She holds a Doctoral Degree in Engineering and Technology from Aalto University (2010) and a Master's Degree in Engineering and Technology from Helsinki University of Technology (2002). Doctoral Degree: Aalto University, 2010 Master's Degree: Helsinki University of Technology, 2002 Her research focuses on Magnetoencephalography (MEG) , Functional Connectivity , and Brain Networks . She explores Transcranial Magnetic Stimulation (TMS) , Functional MRI , and Neural Networks to map language-critical brain areas and study cortical dynamics. Recent work includes automated speech artefact removal from MEG data and test-retest reliability of brain connectivity metrics. Mia actively participates in conferences like MEG Nord and has presented invited talks on language processing and large-scale brain networks. Her publications emphasize MEG-informed TMS, cortical beta modulation, and brain stimulation precision. She contributes to the UN Sustainable Development Goal of Quality Education through neuroimaging research.

• Internet of Things
• Edge Computing
• Artificial Intelligence
• +۶ مورد دیگر

Professor Xiaodong Liu is a faculty member at Edinburgh Napier University, affiliated with the School of Computing, Engineering and the Built Environment . His research spans Internet of Things , Edge Computing , Artificial Intelligence , and Cybersecurity , with a focus on decentralized systems and data-driven decision-making. Research Themes : IoT orchestration, federated learning, smart city infrastructure, building maintenance optimization, and automotive cybersecurity. Current Projects : Leading Swarmchestrate (EU-funded), Long-range Perceptive Autonomous Vehicles (Royal Society), and Met-Bot for Disaster Surveillance (Royal Society). His recent publications emphasize privacy-preserving edge learning , semantic IoT data validation , and deep learning for weather prediction . As a supervisor, he has guided PhD students in areas like federated learning, smart building systems, and IoT security. Collaborations include partnerships with institutions in Scotland, China, and Italy, alongside funding from European Commission , Royal Society , and Scottish Funding Council . He contributes to international conferences and journals, with notable work in IEEE Transactions , ACM TAAS , and MDPI publications.

James Curleyمشاهده پروفایل

دانشیار

• Social Dynamics
• Social Neuroscience
• Social Determinants of Health
• +۴ مورد دیگر

James Curley is an Associate Professor in the Department of Psychology at the University of Texas at Austin, part of the College of Liberal Arts. He is Co-Director of the Behavioral and Social Data Science Major and Principal Investigator of the Social Dynamics Lab. His research focuses on the neurobiological basis of social behavior, social hierarchies, and long-term physiological changes linked to social status. He earned his B.A. from the University of Oxford and a Ph.D. from the University of Cambridge, followed by postdoctoral work at Cambridge before joining Columbia University in 2012. At UT Austin since 2017, he has received awards including the Affordable Education Champion (2021), Dr. Wendy Domjan Excellence in Teaching Award (2021), and President’s Associates Teaching Excellence Award (2022). His teaching includes courses on statistics, R programming, and social behavior analysis. Curley’s lab investigates social dynamics through interdisciplinary approaches, combining behavioral studies with genomic, transcriptomic, and neuroendocrine analyses. He emphasizes open educational resources, co-authoring an open-access textbook for statistics and developing interactive learning tools. His work spans animal and human social systems, exploring how early life experiences shape behavior and how social hierarchy impacts neural and physiological processes. He has published extensively on topics like social dominance, gene expression, and the application of network models to understand social interactions. Teaching highlights include PSY 317L (statistics for behavioral sciences), PSY 120R (R programming), and UGS 302 (Being Social). His commitment to affordable education and innovative pedagogy has been recognized nationally. Research collaborations include studies on primate microbiota, stress vulnerability in depression models, and transgenerational epigenetic effects. He maintains affiliations with platforms like GitHub and Google Scholar, reflecting his embrace of open science principles.

John O'Dohertyمشاهده پروفایل

استاد

• Decision Neuroscience
• Reward Learning
• Neural Mechanisms of Decision-Making
• +۴ مورد دیگر

John O'Doherty is the Fletcher Jones Professor of Decision Neuroscience at the California Institute of Technology (Caltech). He holds a B.A. from Trinity College Dublin (1996) and a D.Phil. from the University of Oxford (2000). His academic trajectory includes roles as Assistant Professor (2004–2007), Associate Professor (2007–2009), and Professor (2009–present), culminating in his current endowed chair since 2021. From 2013–2017, he served as Director of the Caltech Brain Imaging Center. Research Focus: O'Doherty’s work investigates how the brain processes decisions under uncertainty, encodes reward values, and learns from experience. His group studies neural systems involved in decision-making, including arbitration between model-based and model-free learning strategies, the role of prefrontal and striatal circuits in value representation, and social learning mechanisms. They utilize computational modeling combined with neuroimaging (fMRI) and electrophysiological techniques. Key Contributions: His research bridges cognitive neuroscience and psychiatry, addressing topics like gambling disorder, autism spectrum traits, anorexia nervosa, and obsessive-compulsive behaviors. He has pioneered studies on the neural substrates of reward prediction errors, hierarchical decision-making, and the interplay between habitual and goal-directed actions. Affiliations: Caltech Brain Imaging Center (Former Director) Leading international collaborations in neuroeconomics and computational psychiatry Grants & Funding: Extensive support from NIH, NSF, and private foundations for projects on decision neuroscience and translational mental health research.

• Computational Neuroscience
• Visual System
• Sensory Coding
• +۷ مورد دیگر

Thomas Wachtler-Kulla is a Professor in the Department of Biology II at Ludwig-Maximilians-University Munich, where he leads the Computational Neuroscience research group. He is a GSN full member and serves as the GSN Ombudsperson, offering neutral and confidential counseling for students. He is also the Group Leader and Scientific Director at the German Neuroinformatics Node (G-Node), contributing significantly to neuroscience data infrastructure. His research focuses on how the brain processes sensory signals, particularly in the visual system, aiming to understand neural coding, perceptual stability, and color vision under natural conditions. He employs neurophysiology, psychophysics, and computational modeling to investigate sensory processing, eye movement compensation, and efficient coding mechanisms. At G-Node, he develops software and hardware tools for organizing, storing, analyzing, and sharing neurophysiological data, promoting reproducible research. His recent work spans Bayesian models of hue perception, data management frameworks like DataLad and odML, and studies on honeybee neuroethology. He actively supervises graduate students and contributes to major initiatives such as NFDI-Neuro and the International Neuroinformatics Coordinating Facility, advancing standards for open and FAIR neuroscience. Scientific Contributions and Leadership: Scientific Director, German Neuroinformatics Node (G-Node) GSN Ombudsperson for student conflict resolution Key contributor to NFDI-Neuro and INCF Developer of tools for metadata management and data sharing He advises several current and former graduate students and is deeply involved in shaping data policies and infrastructure for the neuroscience community, ensuring scientific rigor and accessibility.

Dominique Hansenمشاهده پروفایل

استاد

• Exercise Physiology
• Cardiometabolic Disease
• Cardiac Rehabilitation
• +۳ مورد دیگر

Prof. Dominique Hansen is a Full Professor in Exercise Physiology and Rehabilitation of Internal Medicine at Hasselt University's Faculty of Rehabilitation Sciences, and a research associate at the Hasselt Heart Center (Jessa Hospital). His research focuses on 'precision exercise' to combat cardiometabolic diseases like obesity, diabetes, and cardiovascular disease, emphasizing tailored exercise protocols over generic recommendations. He co-developed the EXPERT tool, a digital decision-support system used across Europe to create personalized exercise prescriptions for cardiovascular patients. Education: Advanced degrees in Exercise Physiology and Medicine (details not specified) Key Collaborations: Prof. Paul Dendale, Prof. Karin Coninx, and the 30+ member EXPERT Network Group spanning 10+ European countries His research has produced over 200 peer-reviewed publications, including European guidelines for cardiac rehabilitation. Two major research lines include: (1) developing precision exercise guidelines for CVD prevention and treatment, and (2) exploring exercise's role in protecting non-cardiac organs (e.g., the brain in type 2 diabetes patients). He advocates for exercise as a core component of secondary prevention, emphasizing the need to move beyond 'one-size-fits-all' approaches. Current projects include optimizing physical activity monitoring via wearable tech and mHealth interventions for older adults. Labs/Teams: Hasselt Heart Center Research Group, EXPERT Network Group Grants: Funding details not specified, but receives donations via Hasselt Heart Center

Ellen Fitzsimmons-Craftمشاهده پروفایل

دانشیار

• Eating Disorders
• Mental Health Disparities
• Digital Mental Health Interventions
• +۷ مورد دیگر

Ellen Fitzsimmons-Craft is an Associate Professor in the Department of Psychological & Brain Sciences and the Department of Psychiatry at Washington University in St. Louis. She leads the ACCESS Lab, which focuses on scalable digital solutions for eating disorders and mental health disparities. Her work integrates technology, implementation science, and public health to bridge research-practice gaps. Ph.D., University of North Carolina at Chapel Hill M.A., University of North Carolina at Chapel Hill B.A., University of Notre Dame Her research centers on digital interventions for eating disorders, particularly through mobile apps and chatbots. She emphasizes equitable access to care, prevention, and evidence-based treatments using technology. Her work is grounded in cognitive behavioral therapy and implementation science, aiming to scale interventions across college campuses and broader populations. Recent publications highlight her leadership in chatbot development, online screening, and cluster randomized trials of digital CBT. Her work spans mobile health (mHealth) , implementation science , college mental health , and public health innovation . She has published in top journals such as JAMA Network Open , International Journal of Eating Disorders , and Contemporary Clinical Trials . Translational Impact Award, ISRII (2025) Transformation of Mental Health Care grant, Klingenstein Foundation (2024) She mentors graduate and undergraduate students and leads major funded projects, including an NIH R01 grant on chatbot optimization. She also serves as Director of the mHealth Research Core and Treasurer of the Academy of Psychological Clinical Science. Her lab collaborates with industry, non-profits, and government agencies to translate research into real-world impact. The ACCESS Lab develops and tests digital tools to expand access to eating disorder care. Current projects include state-wide university screening platforms, adolescent chatbots, and provider training in digital CBT. The lab emphasizes diversity, equity, and scalability in mental health innovation.

Psyche Louiمشاهده پروفایل

دانشیار

• Music Perception and Cognition
• Auditory Cognitive Neuroscience
• Creativity and Creative Practice
• +۳ مورد دیگر

Psyche Loui is an Associate Professor of Creativity and Creative Practice in the Department of Music at Northeastern University, leading the Music, Imaging, and Neural Dynamics (MIND) Lab. She holds a PhD in Psychology from UC Berkeley and dual bachelor’s degrees in Psychology and Music from Duke University. Her research focuses on the neuroscience of music perception, including how music influences attention, emotion, and neurological conditions like ADHD. Her work has been supported by major grants from the NSF and NIH, and she has received awards such as the Templeton Positive Neuroscience Award and a NSF CAREER Award. Education : PhD in Psychology (Cognition, Brain, and Behavior), University of California, Berkeley Bachelor’s in Psychology and Music, Duke University Research & Grants : Loui’s research explores how neural mechanisms underpin music perception and cognition, with recent studies investigating the efficacy of algorithmically generated music for improving focus in ADHD. Her grants include NIH funding for projects like ‘Gamma-Music-Based Intervention for Alzheimer’s’ ($2.7M) and NSF support for ‘Reward Prediction in Creative Perception.’ Awards : National Science Foundation CAREER Award (2020) Templeton Positive Neuroscience Award (2010) Multiple Grammy Awards (text reference) Labs & Teams : Loui directs the MIND Lab, which uses neuroimaging and electrophysiology to study music-brain interactions. Collaborations include work with Brain.fm on attention-focused music interventions and global studies on cross-cultural music patterns. Teaching : Courses include Music Cognition , Acoustics and Psychoacoustics , and Creative Cognition , bridging theory with practical applications in neuroscience and education.