جست‌وجوی استاد و پژوهشگر

Najim Dehakمشاهده پروفایل

دانشیار

• Machine Learning
• Speech Processing
• Audio Classification
• +۷ مورد دیگر

Najim Dehak is an Associate Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, part of the Whiting School of Engineering. His research focuses on machine learning applied to speech processing, audio classification, and health applications. He is renowned for developing the I-vector representation for speaker recognition, introduced in 2008 during a workshop at Johns Hopkins’ Center for Language and Speech Processing. Prior to this role, he was a research scientist at MIT’s Computer Science and Artificial Intelligence Laboratory. Dehak holds a PhD from the School of Advanced Technology in Montreal (2009). He is a Senior Member of IEEE and contributes to the IEEE Speech and Language Technical Committee. His work bridges AI, healthcare, and signal processing, with notable contributions to neurodegenerative disease detection via speech and handwriting analysis. Research interests include adversarial attacks on speech systems, multimodal biomarker discovery, and robust speech processing across demographics. His lab’s tools, like the Hermespeech Recorder, enable scalable data collection for clinical and research applications. Education: PhD in Advanced Technology (2009), Montreal Affiliations: Johns Hopkins University, IEEE Labs/Teams: Center for Language and Speech Processing (CLSP) His recent work explores AI’s role in aging research, including Alzheimer’s and Parkinson’s disease detection through speech, eye tracking, and handwriting analysis. Ongoing projects address fairness in speaker verification and robustness against adversarial attacks in ASR systems.

Robert Wilsonمشاهده پروفایل

دانشیار

• Computational Cognitive Neuroscience
• Reinforcement Learning
• Decision Making
• +۷ مورد دیگر

Robert Wilson is an Associate Professor in the School of Psychology at Georgia Institute of Technology. His research focuses on computational cognitive neuroscience, reinforcement learning, decision making, and their applications in psychiatry and aging. Wilson leads a lab integrating computational modeling with behavioral experiments, neuroimaging, and neurostimulation to explore the algorithms underlying human cognition. He holds a Ph.D. in Bioengineering from the University of Pennsylvania (2009). Education: Ph.D. in Bioengineering, University of Pennsylvania, 2009 Research Interests: Wilson’s work spans computational models of decision making, reinforcement learning dynamics, and the neural mechanisms of explore-exploit trade-offs. His lab investigates phishing detection cognition, navigation strategies, and perceptual decision making, with recent studies on aging-related cognitive decline and psychiatric disorders. Publications: His recent work includes studies on computational models of exploration strategies, phishing susceptibility (PEST task), and aging effects on decision-making. Articles often bridge neuroscience and AI, such as applying reinforcement learning to large language models. Awards & Grants: While no formal awards are listed, his research has been supported by grants exploring computational psychiatry and neuroimaging. Labs & Teams: Wilson directs a multidisciplinary lab at Georgia Tech, collaborating with experts in AI, neuroscience, and clinical psychology to advance computational theories of the mind-brain relationship.

Sebastian Raischمشاهده پروفایل

استاد

• Strategic Management
• Organizational Ambidexterity
• Artificial Intelligence in Organizations
• +۷ مورد دیگر

Sebastian Raisch is a Full Professor of Strategic Management at the Geneva School of Economics and Management (GSEM), University of Geneva, where he also serves as Director of the Executive MBA program. He is affiliated with the Institute of Management and holds a Ph.D. from the University of Geneva. His academic work is deeply rooted in understanding how organizations balance stability and change, particularly in the face of digital transformation. Research Interests: His primary areas of expertise include strategic management, organizational ambidexterity, organizational paradox, artificial intelligence in organizations, and digital transformation. His research investigates the dynamics of corporate growth and decline, strategic renewal, and the integration of human and artificial intelligence in organizational decision-making. He is especially interested in how firms navigate tensions between competing demands such as exploration and exploitation, automation and augmentation, and stability and innovation. Publications and Trends: His recent publications (2023–2025) show a strong shift toward the role of artificial intelligence in management, with a focus on hybrid human-AI systems, ethical AI, and the strategic implications of AI adoption. Earlier works center on ambidexterity, paradox theory, and corporate turnaround, establishing him as a leading scholar in these domains. Scientific Awards: Strategic Management Society’s Best PhD Paper Award Emerald Publishing Group’s Citation of Excellence Award Journal of Management’s Scholarly Impact Award Editorial and Professional Service: Sebastian Raisch serves as Associate Editor at the Academy of Management Review and sits on the editorial boards of the Academy of Management Journal and the Strategic Management Journal . He has advised numerous doctoral students and supervised research projects on organizational innovation and digital strategy, though specific names are not listed in the provided materials. He has not received explicit mention of research grants, but his extensive publication record suggests active research funding. Labs and Research Teams: While no formal lab is mentioned, Raisch leads or contributes to research initiatives within the Institute of Management at GSEM, particularly in strategic management and digital innovation. He collaborates with scholars across Europe and the U.S., including notable co-authors like J. Schad, S. Krakowski, and M. Tushman.

John MacFarlaneمشاهده پروفایل

استاد

• Philosophy of Language
• Philosophical Logic
• Metaphysics
• +۸ مورد دیگر

John MacFarlane is a Professor of Philosophy at the University of California, Berkeley , with affiliations to the Group in Logic and the Methodology of Science and co-organizer roles in the Meaning Sciences Club and Townsend Center Working Group . His research spans philosophy of language, philosophical logic, metaphysics, epistemology, philosophy of mathematics, history of logic , and ancient philosophy , particularly Aristotle . He has held visiting positions at institutions like École des Hautes Études en Sciences Sociales (EHESS), Paris . Education : Ph.D. in Philosophy (2000, University of Pittsburgh), M.A. in Classics (1997) and Philosophy (1994), A.B. summa cum laude in Philosophy (1991, Harvard). Research Interests focus on semantic relativism , particularly assessment sensitivity as explored in his book Assessment Sensitivity: Relative Truth and Its Applications (Oxford, 2014). He investigates how epistemic modals and vagueness interact with truth evaluation. His work in philosophical logic includes a textbook Philosophical Logic: A Contemporary Introduction (Routledge, 2021). Secondary interests include ancient philosophy (Aristotle, Plato), formal logic , and fiddlosophy . Article Trends reveal contributions to relativism , epistemic modals , vagueness , and logicism . His recent work (2025) examines Plato’s Protagoras and belief utility , while 2022–2020 articles address semantic indecision and probabilistic knowledge . Earlier works (2008–2000) include foundational studies on Frege, Kant, and Aristotle . Scientific Awards include a 2023 Residential Fellowship at Wissenschaftskolleg, Berlin (declined), 2016–17 Fellow at IEA Paris , and 2015 American Academy of Arts and Sciences membership . He has received multiple Humanities Research Fellowships at Berkeley (2016, 2008, 2003) and Research Enabling Grants (2000–2009). Advising includes supervising 15+ PhD students in areas like logic, epistemology, and philosophy of language . Notable advisees include Richard Lawrence (2017) and Sophie Dandelet (2021). He also guided honors theses at Berkeley, such as Benjamin Logan’s work on epistemic modals (2018). Labs and Teams involve co-organizing the Townsend Center Working Group in History and Philosophy of Logic, Mathematics, and Science and contributing to the Group in Logic and Methodology of Science . His interdisciplinary approach integrates philosophy, logic, and computational tools , including creating Pandoc for document conversion.

Wim Geversمشاهده پروفایل

استاد

• Cognitive control
• Metacognition
• Working memory
• +۳ مورد دیگر

Wim Gevers is a faculty member at the Université libre de Bruxelles (ULB) and leads the CS4S – Cognitive Control & Sleep laboratory within the CRCN research centre. His work bridges cognitive psychology, neuroscience and sleep research to understand how the brain exerts control over thoughts and actions and how sleep contributes to these processes. Research Interests Cognitive Control & Metacognition: Investigating how subjective experiences such as confidence and the "urge-to-err" guide strategic adjustments in behaviour. Working Memory & Ordinal Cognition: Examining how order information is maintained and manipulated, and how these processes relate to mathematical competence. Sleep, Memory & Decision Making: Exploring how sleep-dependent consolidation influences motor learning and decision strategies. Across his 2022–2025 publications a clear trend emerges: a focus on metacognitive monitoring —how humans evaluate their own cognitive states—and the role of emotional and temporal context in shaping those evaluations. Studies range from reaction-time introspection and confidence judgements in perceptual tasks to the impact of aging and depression on metacognitive accuracy. Doctoral Supervision & Mentoring Whitney Stee (PhD 2024) – Sleep-dependent structural brain reorganization & motor learning Gaia Corlazzoli (PhD 2024) – Subjective experience in decision-making Myrtille Dewulf (PhD 2023) – Ordinal coding mechanisms in working memory Rebeca Sifuentes-Ortega (PhD 2023) – REM sleep and memory reactivation All dissertations were defended at ULB, Faculté des Sciences psychologiques et de l’éducation, with Wim Gevers formally listed as Promotor . Laboratory & Collaborative Networks As head of CS4S, Gevers coordinates a multidisciplinary team that combines behavioural experimentation, EEG/MEG, computational modelling and sleep polysomnography. The lab is embedded in the larger CRCN ecosystem, fostering collaborations with groups such as CO3 (consciousness), LCLD (language & deafness), and UR2NF (neurofunctional imaging).

• Microbial Genomics
• Gut Microbiome
• Aging and Healthy Aging
• +۴ مورد دیگر

Paul O'Toole is a Professor of Microbial Genomics and Principal Investigator at the APC Microbiome Ireland, University College Cork. His research focuses on the gut microbiome's role in health, aging, and disease, particularly in the context of diet and probiotics. He leads projects like the ELDERMET study on elderly nutrition and the NU-AGE project exploring Mediterranean diets' anti-aging effects. He holds a BA (Mod.) from Trinity College Dublin and a PhD from Lund University, with postdoctoral training in Canada and New Zealand. Key grants include studies on dairy-derived microbiota, probiotic strain improvement, and microbiome analysis in aging populations. He has published extensively on Lactobacillus genomics, gut-brain interactions, and microbiome-driven health outcomes. His work bridges fundamental microbiology with clinical applications, emphasizing translational research. Scientific highlights include discovering microbiome links to cognitive decline, demonstrating dietary modulation of gut microbes to combat obesity, and identifying keystone species in healthy aging. He advocates for sustainability in conservation and food systems, reflecting his interdisciplinary approach to global health challenges.

Takako Fujiokaمشاهده پروفایل

دانشیار

• Auditory Perception
• Neural Oscillations
• Auditory-Motor Coupling
• +۵ مورد دیگر

Takako Fujioka is an Associate Professor of Music at Stanford University, affiliated with the Center for Computer Research in Music and Acoustics (CCRMA). Her research focuses on the neural mechanisms underlying auditory perception, auditory-motor coupling, and music-supported therapy for neurorehabilitation. She holds a Ph.D. in Physiology from the Graduate University for Advanced Studies, Japan, and M.Sc./B.Eng. degrees in Electrical Engineering from Waseda University. Her work combines neurophysiological techniques such as MEG and EEG to study brain plasticity in development, aging, and stroke recovery. Notable contributions include investigating how music influences motor and cognitive recovery in stroke patients, as well as exploring the neural basis of musical perception through rhythmic synchronization and pitch discrimination studies. Supported by awards from the Canadian Institutes of Health Research during her postdoctoral work at the Rotman Research Institute, her research bridges clinical neuroscience and music cognition. Dr. Fujioka’s expertise spans auditory neuroscience, neurorehabilitation, and technology-assisted music therapy. She has pioneered studies on tactile mapping for cochlear implant users and networked music performance systems, emphasizing cross-modal perception and human-technology interaction. Her findings contribute to both theoretical understanding of auditory processing and practical applications in medical and educational settings. Awards: Canadian Institutes of Health Research Awards (postdoctoral phase) Labs/Teams: CCRMA, Stanford Music Perception Laboratory, Rotman Research Institute collaborations Key Themes: Neuroplasticity, Music-Mediated Rehabilitation, Auditory-Motor Integration, Multisensory Processing Her recent work examines aging-related changes in binaural hearing and the role of beta/gamma oscillations in rhythmic processing. She advocates for translational research that connects neural mechanisms with real-world therapeutic interventions.

Kevin Pisciottaمشاهده پروفایل

استادیار

• Empirical Corporate Finance
• Initial Public Offerings (IPOs)
• Information Intermediaries
• +۵ مورد دیگر

Kevin Pisciotta is an Assistant Professor of Finance at the University of Kansas School of Business, specializing in empirical corporate finance, initial public offerings (IPOs), and information intermediaries. His work bridges corporate finance theory with real-world market dynamics, focusing on IPO processes, analyst behavior, and economic impacts of social issues like opioid abuse and sports betting. Education: Ph.D. in Business Administration, Pennsylvania State University M.S. in Finance, University of Delaware B.S. in Finance, University of Delaware Research Interests: His research explores how IPO bookbuilding mechanisms influence market outcomes, the role of sell-side analysts in information dissemination, and the macroeconomic consequences of phenomena like opioid abuse and sports gambling. He also investigates labor economics through the lens of corporate innovation and workforce productivity. Publications Trends: Recent work examines the interplay between sports betting and household financial stability (2024), IPO marketing strategies post-JOBS Act reforms (2018), and the spillover effects of opioid abuse on regional innovation ecosystems (2023). His studies frequently employ large-scale datasets to analyze market microstructure and policy impacts. Grants/Advising: No specific grants or advisee names are listed in available materials. Teaching experience includes undergraduate courses in investments, international finance, and corporate financial management. Labs/Teams: No dedicated lab affiliation is mentioned, though collaborations with institutions like Pennsylvania State University and the University of Delaware are implied through his educational background and co-authorships.

Ashli Owen-Smithمشاهده پروفایل

پژوهشگر

• Mental Health
• Behavioral Science
• Trauma
• +۶ مورد دیگر

Ashli Owen-Smith is a behavioral scientist affiliated with the School of Public Health at Georgia State University , where her research focuses on mental health disparities, suicide prevention, and integrative/complementary approaches for complex mental-physical health conditions. She works with underserved populations including refugees/immigrants, incarcerated individuals, and LGBTQ+ communities through community-based participatory research and mixed-methods frameworks. Her current projects are funded by CDC , DBHDD , and DPH . Education: Ph.D. in Behavioral Sciences (Emory, 2009), S.M. in Public Health (Harvard, 2005), B.A. in Psychology (Smith College, 2001) Her research spans mental health , trauma , suicide prevention , and mindfulness-based interventions . Recent work examines telehealth adaptations for chronic pain and mental health, vaccine hesitancy in refugee communities, and social determinants of suicide . She leads studies on gender-affirming care and health disparities in LGBTQ+ populations. Key article trends include epidemiological analysis of suicide risk factors, COVID-19 impacts on mental health, ICD-10 coding for autism, and complementary medicine in trauma recovery. Subfields span telehealth , health equity , mental-physical comorbidity , and community engagement . Students she has mentored include C.A. Scarlett , T. Griner , and M.M. Sesay . Her work integrates public health policy , clinical research , and health systems analysis .

Mark Leeمشاهده پروفایل

استاد مدعو

• Human Factors Engineering
• Healthcare Safety
• Data Analytics in Manufacturing
• +۳ مورد دیگر

Mark Lee is an Adjunct Professor in the People Analytics department at NYU’s Tandon School of Engineering, specializing in Technology Management and Innovation. He holds a Ph.D. in Engineering Psychology from Georgia Institute of Technology (1996). Currently, he serves as Head of Research, Analytics, and Business Development at UL ComplianceWire, focusing on pharmaceutical and medical device manufacturing training. His research leverages large datasets to improve healthcare safety through regulatory compliance and best practices. Courses taught include Human Factors Engineering, Workplace Design, and Predictive Analytics. Education: Ph.D. in Engineering Psychology, Georgia Tech (1996) Key Roles: Adjunct Professor, Head of Research at UL ComplianceWire Research Focus: Human Factors, Training Systems Design, Healthcare Compliance His work spans auditory display systems for aviation (e.g., 3D audio cockpit interfaces) and ergonomic design for industrial products. Recent projects emphasize data-driven solutions for regulatory challenges in life sciences. Publications highlight studies on visual search strategies, age-related cognitive performance, and application of signal detection theory in decision-making. He actively collaborates with industry and government entities, exemplified by the FDA-UL Cooperative Research Agreement.

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.

Jennifer A. Smithمشاهده پروفایل

دانشیار

• Genetic Epidemiology
• Epigenomics
• Aging
• +۴ مورد دیگر

Jennifer A. Smith, PhD, MPH is an Associate Professor of Epidemiology and Research Associate Professor of Survey Research at the University of Michigan School of Public Health . She also serves as Director of the Certificate in Public Health Genetics and is Assistant Director of the Cohort Development working group for U-M Precision Health and the Michigan Genomics Initiative. Education: PhD, Epidemiology, University of Michigan (2011) MA, Statistics, University of Michigan (2009) MPH, Health Management and Policy, University of Michigan (2005) BS, Biological Sciences, Cornell University (2001) Research Interests: Dr. Smith is a genetic epidemiologist whose work lies at the intersection of genomics, epigenomics, and social epidemiology . She investigates how genetic, epigenetic, and transcriptomic variation influence age-related chronic diseases such as cardiovascular disease, hypertension, dementia, and cognitive decline. A major focus is understanding how social, psychosocial, and neighborhood determinants interact with genetic risk to shape socioeconomic and racial/ethnic health disparities . Research Projects: Her work leverages large, multi-ethnic cohorts including the Health and Retirement Study (HRS), GENOA, SWAN, MESA, and LASI. She is a core faculty member of the Center for Social Epidemiology and Population Health (CSEPH) , and affiliated with MiCDA, the Center for Midlife Science, and the PNG Program . She also collaborates with leading consortia such as CHARGE and TOPMed. Selected Trends in Publications: Her recent work (2022–2025) emphasizes epigenetic mediation of social determinants on cardiovascular and cognitive health, polygenic risk scores across diverse ancestries , and multi-omics integration in aging and disease. These studies consistently highlight how social environments and genetic architecture jointly influence health outcomes across populations. Contact: Email: smjenn@umich.edu Office: 734-615-9455 Address: 2631 SPH I, 1415 Washington Heights, Ann Arbor, MI 48109

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.

Priya Ramanمشاهده پروفایل

دانشیار

• Molecular mechanisms of vascular smooth muscle cell phenotypic switching
• Non-lipid causes of vascular disease in metabolic syndrome
• Mechanistic links between metabolic syndrome, vascular dysfunction, and Alzheimer’s disease
• +۳ مورد دیگر

Priya Raman, Ph.D., FCVS, is an Associate Professor of Integrative Medical Sciences at Northeast Ohio Medical University (NEOMED). She holds tenure and serves as Co-Director of the Basic and Translational Biomedicine (BTB) Graduate Program and Chair of the Kent State University-Biomedical Sciences Pharmacology Graduate Program. Her academic roles include teaching in NEOMED’s medical curriculum, focusing on pharmacology, cardiovascular systems, and clinical therapeutics. Raman’s research explores molecular mechanisms linking metabolic disorders (e.g., diabetes, metabolic syndrome) to vascular dysfunction and Alzheimer’s disease, using mouse models and cellular/molecular techniques. She has published extensively on thrombospondin-1, O-GlcNAc signaling, and atherosclerosis pathogenesis. Education: B.Pharm. and M.Pharm. (India), Ph.D. in Pharmacology (University of Louisiana at Monroe). She has over 25 years of postdoctoral and faculty experience, including roles at the Cleveland Clinic and Indiana University School of Medicine. Raman serves on editorial boards for journals like International Journal of Cardiology and Frontiers in Cardiovascular Medicine , and reviews grants for the American Heart Association and NIH. Research focus areas include: (1) Vascular smooth muscle cell phenotypic switching in metabolic diseases, (2) Non-lipid mechanisms of vascular disease in metabolic syndrome, and (3) Interactions between metabolic disorders and neurodegeneration. Her lab employs biochemical assays, mouse models (e.g., ApoE-/-, KKAy), and advanced imaging techniques to study these pathways. Notable recent work includes discovering that O-GlcNAc transferase deletion reduces atherosclerosis in hyperglycemic mice and identifying thrombospondin-1’s role in leptin-driven vascular pathology. She has also linked metabolic syndrome-induced O-GlcNAc deficits to Alzheimer’s-like cognitive impairment in aging mice. Raman is actively involved in interprofessional education and mentoring, directing graduate programs and teaching courses in pharmacology, molecular signaling, and diabetes/vascular disease. Her work bridges basic science and clinical applications, aiming to develop novel therapies for metabolic syndrome-related vascular complications.

• Nuclear Magnetic Resonance (NMR)
• In vivo imaging
• MRI pulse sequence development
• +۷ مورد دیگر

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.