Find Academic Supervisors

Daniel KeeserView profile

Research Fellow

• Multimodal Neuroimaging
• Neuromodulation
• Neurophysiology
• +4 more

Dr. Daniel Keeser is a Research Fellow and Research Group Leader at the Department of Psychiatry and Psychotherapy of the University of Munich (LMU) and affiliated with the NeuroImaging Core Unit Munich (NICUM). His work focuses on clinical deep phenotyping and multimodal neuroimaging, integrating advanced MRI, EEG, and non-invasive brain stimulation methods to study severe mental and neurological disorders. Research Interests: Elucidating neurobiological mechanisms of schizophrenia, major depressive disorder, and Alzheimer's disease through multimodal neuroimaging and neuromodulation. His recent publications highlight methodologies like resting-state fMRI, diffusion tensor imaging, and transcranial magnetic stimulation combined with MRI, emphasizing personalized treatment strategies. Collaborative affiliations include the University Hospital of LMU Munich and the Clinical Deep Phenotyping (CDP) Working Group. Affiliations: NeuroImaging Core Unit Munich (NICUM) Department of Psychiatry and Psychotherapy, University of Munich (LMU)

• Biomedical imaging
• Diffusion weighted imaging
• Dynamic imaging
• +7 more

Brad Sutton is a Professor of Bioengineering at the University of Illinois Urbana-Champaign and Technical Director of the Biomedical Imaging Center at Beckman Institute. He holds affiliate roles in the Neuroscience Program, Department of Electrical and Computer Engineering, and is a Health Innovation Professor at the Carle Illinois College of Medicine. His roles also include fellowship positions with the National Center for Supercomputing Applications and the CZ Biohub Chicago. Education: Ph.D. in Biomedical Engineering from the University of Michigan (2003). Research Interests: Focus on advanced MRI techniques for structural and functional brain imaging, including diffusion-weighted imaging, dynamic imaging, and neuromuscular coupling studies. His work emphasizes multi-scale bioimaging to understand brain function across interventions, aging, and disease. Publications: Over 180 peer-reviewed articles in 2025-2024 highlight innovations in MRI technology and applications in neuroscience, including breakthroughs in laminar fMRI specificity, myelin development modeling, and Alzheimer’s biomarker studies. Recent work extends to clinical applications like aortic imaging automation and mixed reality training tools. Awards: AIMBE and ISMRM Fellowships (2017/2024), Abel Bliss Scholar (2014-), and over 9 patents in imaging techniques. Labs & Teams: Leads the Magnetic Resonance Functional Imaging Lab. Collaborates with interdisciplinary teams across engineering, medicine, and computational science to advance imaging technologies and their clinical translation.

Andrea FaliniView profile

Professor

• Advanced MR Techniques
• Neuro-oncology
• Degenerative Diseases
• +3 more

Andrea Falini is a Full Professor of Neuroradiology at the Vita-Salute San Raffaele University (School of Medicine). He holds leadership roles including Head of the Neuroradiology Department at the Scientific Institute S. Raffaele Hospital and Head of the Advanced Diagnostic in Neuro-oncology Unit. His career spans clinical, academic, and administrative roles since 1991, with extensive experience in neuroradiology, neuro-oncology, and neuroimaging research. Education: MD (1986, University of Milan), PhD in Neurological Sciences (1989), and specialized training in Neurology and Radiology at leading institutions, including a visiting scholar position at UCSF (1996). Research interests focus on advanced MRI techniques, neuro-oncology, neurodegenerative diseases, and cognitive neuroscience. His work emphasizes functional MRI, diffusion tensor imaging, and clinical applications in glioma management and neurodegenerative disorders. Awarded the 1989 'De Visart' Prize and grants supporting his international collaborations. He has authored 218+ peer-reviewed articles, with an H-index of 40+, and actively contributes to academic committees and international conferences. Leadership roles include Vice-Director of the Neuroscience Division (2014–present) and coordination of the BraiMap Research Program. His academic teaching spans neuroradiology, biomedical engineering, and cognitive neuroscience courses.

Anru ZhangView profile

Associate Professor

• Tensor Learning
• High-Dimensional Statistics
• Machine Learning
• +3 more

Anru Zhang is the tenured Eugene Anson Stead, Jr. M.D. Associate Professor with joint appointments in Biostatistics & Bioinformatics, Computer Science, Electrical and Computer Engineering, and Statistical Science at Duke University. He holds a Ph.D. from the University of Pennsylvania (2015, advised by T. Tony Cai) and a B.S. in Mathematics from Peking University (2010). Current roles: Associate Professor at Duke (2024–present), previously Assistant Professor at UW-Madison (2018–2021) Research focus: Tensor learning, high-dimensional statistics, EHR analysis, and healthcare applications Mentorship: Supervises active research team including postdocs (Jianbin Tan, Qiuyi Wu) and PhD students (Runshi Tang, Yinrui Sun) Research Trends : His recent publications emphasize tensor methods in biomedical data (EHR, microbiome, Alzheimer’s), Riemannian optimization for high-dimensional problems, and hybrid statistical-computational approaches. Key themes include healthcare AI, EHR analysis, and non-convex optimization. Scientific Awards : COPSS Emerging Leader Award (2024) IMS Tweedie New Researcher Award (2022) ASA Gottfried E. Noether Junior Award (2021) NSF CAREER Award (2020) AMIA Data Science Outstanding Paper Award (2023) Advising & Grants : Mentored 16+ students/postdocs, including Yuetian Luo (IMS Lawrence D. Brown Award) and Yuchen Zhou (IMS Hannan Travel Award). Current grants include NIH-funded projects on sepsis detection, mental health AI, precision genetic testing, and telehealth interventions, plus NSF CAREER funding for statistical inference in high-dimensional structures. Labs & Teams : Leads a research group at Duke focusing on tensor learning, statistical theory, and healthcare AI applications. Collaborates with Duke’s AI Health initiative and serves as Associate Editor for leading journals like Annals of Statistics and JASA.

Fatemeh MollaeiView profile

Lecturer

• Motor Speech Disorders
• Parkinson’s Disease
• Neuroimaging
• +4 more

Fatemeh Mollaei is a Lecturer in Clinical Language Sciences at the University of Reading, affiliated with the School of Psychology and Clinical Language Sciences. She is a core member of the Centre for Integrative Neuroscience and Neurodynamics (CINN), focusing on neurophysiological mechanisms of speech disorders. Her work integrates behavioral, electrophysiological, and neuroimaging techniques to study Parkinson’s disease (PD) and develops neuro-rehabilitation methods using non-invasive brain stimulation (e.g., TMS, tDCS). Her research explores how sensory and motor systems interact during speech production in PD, employing tools like EEG, fMRI, and MEG. She investigates microstructural white matter changes and auditory processing deficits linked to speech impairments, aiming to translate findings into clinical interventions. Her studies also address broader topics like sensorimotor adaptation and stuttering, emphasizing translational neuroscience. Publications highlight trends in neuroimaging of speech disorders, auditory feedback mechanisms, and neuroplasticity in PD. While no formal awards are listed, her contributions to understanding speech motor control and rehabilitation practices are significant. Advising and grant details are not explicitly provided, but her involvement in interdisciplinary CINN projects suggests active collaborative research. Labs/Teams: Active contributor to the Centre for Integrative Neuroscience and Neurodynamics (CINN) at the University of Reading, collaborating on translational neuroscience projects.

Stuart AllanView profile

Professor

• Neuroinflammation
• Stroke Pathology
• Cytokine Signaling
• +3 more

Stuart Allan is Professor of Neuroscience at the University of Manchester's Faculty of Life Sciences, Division of Neuroscience. His research focuses on neuroinflammation mechanisms in stroke and neurodegenerative diseases, with particular expertise in cytokine signaling (especially IL-1) and neuronal injury pathways. He leads projects targeting interleukin-1 for acute brain injury treatment and investigates stroke-immune mediated pathways in cognitive trajectory. Research Interests: Neuroinflammatory responses, cytokine-mediated neuronal injury, stroke pathophysiology, Alzheimer's disease mechanisms, and neurovascular coupling. His work bridges experimental paradigms (in vitro and in vivo) with clinical applications. Education: PhD Biomedical Sciences (University of Aberdeen, 1990-1993) BSc Pharmacology (University of Dundee, 1986-1990) Professional Appointments: Professor of Neuroscience (2012-present) Senior Lecturer (2008-2012) Lecturer (2002-2008) Postdoctoral Research Associate (1993-2002) Research Beacons & Institutes: Dementia@Manchester, Manchester Regenerative Medicine Network, Lydia Becker Institute, Christabel Pankhurst Institute, Manchester Institute for Collaborative Research on Ageing. Scientific Output: 179 research outputs including articles on cerebrovascular health, neurovascular coupling, and cytokine mechanisms in stroke models. Research demonstrates consistent focus on translational neuroscience and neuroinflammatory pathways.

Xiaobo LiView profile

Professor

• Neuroscience
• Medical Imaging
• Machine Learning Applications in Neurology
• +3 more

Xiaobo Li is a Professor in the Department of Bio-Medical Engineering at New Jersey Institute of Technology. Holding a Ph.D. in Computer Aided Geometric Design from the University of Birmingham and a B.S. in Automation from Nanjing University of Aeronautics, their research bridges computational methods with neuroimaging and psychiatric disorder analysis. Ph.D., University of Birmingham (Computer Aided Geometric Design, 2004) B.S., Nanjing University of Aeronautics (Automation, 1999) Dr. Li’s work focuses on applying machine learning and graph theory to understand brain network abnormalities in conditions like ADHD , schizophrenia , and traumatic brain injury . Their studies analyze structural-functional connectivity , reward processing , and gut-brain axis interactions using fMRI , fNIRS , and diffusion tensor imaging . Recent publications highlight their development of tools like the GAT-FD MATLAB toolbox for brain network analysis and their exploration of multimodal MRI in schizophrenia diagnosis. They also investigate the neurobiological effects of photobiomodulation and vision therapy interventions.

Surjo R. SoekadarView profile

Professor

• Clinical Neurotechnology
• Brain-Computer Interfaces (BCI)
• Neuromodulation
• +8 more

Surjo R. Soekadar is the Einstein Professor of Clinical Neurotechnology at Charité – University Medicine Berlin. He leads the Clinical Neurotechnology Laboratory , which focuses on developing noninvasive neurotechnologies for treating neurological and psychiatric disorders through closed-loop brain stimulation and advanced brain-machine interfaces (BCI/BMI). His work integrates real-time EEG/MEG monitoring with electromagnetic stimulation to modulate pathological brain oscillations and enhance neuroplasticity in conditions like stroke, spinal cord injury, and psychiatric disorders. Education : Studied medicine in Mainz, Heidelberg, and Baltimore Clinical Training : Residency in Psychiatry and Psychotherapy at University of Tübingen Academic Journey : 2008-2011 Research Fellow at NINDS (USA); 2017 Venia Legendi at University of Tübingen; 2018 First Professor of Clinical Neurotechnology in Germany His research interests span: • Closed-loop neurostimulation combining real-time brain state monitoring with targeted intervention • Next-generation BCI using optically pumped magnetometers (OPM) for mobile MEG recordings • Neurorehabilitation through exoskeleton control and sensory feedback • Neurophysiological modeling of entropy measures and phase flows Recent publications highlight: • Adaptive deep brain stimulation protocols • Real-time phase-sensitive tACS applications • OPM-based BCI innovations • Stroke recovery mechanisms through corticospinal tract analysis Scientific recognition includes: International BCI Research Award BIOMAG Award NARSAD Young Investigator Award Funded by the European Research Council (ERC) , his lab trains doctoral students like David Haslacher (EEG/MEG integration), Khaled Nasr (multicoil TMS optimization), and Annalisa Colucci (entropy-driven BCI development). The team also explores quantum AI applications in clinical decision-making and bidirectional BCI systems using OPM and tES.

Naftali RazView profile

Professor

• Human Brain Aging
• Cognitive Aging
• Metabolic Risk Factors
• +4 more

Naftali Raz is a Professor of Psychology at Stony Brook University, specializing in Integrative Neuroscience. He holds a Ph.D. from the University of Texas at Austin (1985) and a B.A. from the Hebrew University of Jerusalem (1979). His research focuses on understanding age-related changes in the brain and cognition, particularly exploring metabolic, vascular, and inflammatory risk factors influencing cognitive aging. He employs neuroimaging techniques such as MRI, MRS, and fMRI to study brain structure, function, and metabolism in healthy aging populations. Raz’s research emphasizes the 'FRIENDS' model (Free-Radical Induced Energetic and Neural Decline in Senescence), linking aging to energy production decline. His work includes longitudinal studies on brain atrophy, myelin content, and iron accumulation. He investigates how physiological risk factors like cardiovascular disease and metabolic syndrome impact neurocognitive trajectories. Current grants include NIA funding for neural correlates of cognitive aging and hippocampal glutamate modulation studies. Education: Ph.D. in Psychology, University of Texas at Austin (1985) B.A. in Psychology, Hebrew University, Jerusalem, Israel (1979) Labs/Facilities: Integrative Neuroscience Group, SCAN Center (Stony Brook Advanced Neuroimaging) His publications span over three decades, with recent works on recognition memory strategies, hippocampal subfield analysis, and cerebral blood flow dynamics. Collaborations include multi-institutional projects on neuroimaging protocols and aging mechanisms.

Tanzil M. ArefinView profile

Assistant Professor

• Neuroimaging
• Neuroscience
• Brain Disorders
• +5 more

Dr. Tanzil M. Arefin is an Assistant Professor of Neuroscience at the University of Rochester School of Medicine and Dentistry and Associate Director of the Preclinical Imaging Core at the Center for Advanced Brain Imaging and Neurophysiology (CABIN). His research focuses on developing neuroimaging techniques to study brain functions and microstructures in animal models of human disorders, including neurodegenerative and psychiatric illnesses. He holds affiliations with the Del Monte Institute for Neuroscience and the Neuroscience Ph.D. Program. **Education**: Ph.D., Neuroscience, University of Freiburg and University of Strasbourg (2017) M.Sc., Biomedical Engineering, Czech Technical University and University of Groningen (2012) B.Sc., Electrical and Electronic Engineering, Islamic University of Technology (2007) **Research Interests**: Dr. Arefin's lab employs multimodal MRI methodologies (resting-state fMRI, diffusion MRI, ASL perfusion MRI, MR spectroscopy) alongside optogenetics and chemogenetics to elucidate molecular mechanisms impairing brain plasticity. Current projects include studying cerebellar connectivity's role in non-motor behaviors and developing interventions for alcohol-dependent brains. **Awards**: Magna cum Laude, Summa Cum Laude, Erasmus Mundus Fellowships (both Doctoral and Masters). **Grants & Advising**: Not explicitly listed in texts, but lab activities suggest involvement in NIH-funded projects. Advising details are pending explicit student listings. **Lab & Affiliations**: Arefin Lab focuses on translational imaging tools. Affiliated with UR CABIN and URMC's Neuroscience programs. Location: 430 Elmwood Ave, Rochester, NY.

• Nonlinear analysis
• Scattering theory
• Real and complex analysis
• +7 more

Ronald Coifman is the Sterling Professor of Mathematics and Professor of Computer Science at Yale University. His research focuses on nonlinear analysis, scattering theory, complex analysis, numerical analysis, and their applications in data science, signal processing, and biomedical imaging. He holds the National Medal of Science and is a member of the National Academy of Sciences and the American Academy of Arts and Sciences. Coifman's work bridges pure mathematics and applied sciences, emphasizing harmonic analysis, manifold learning, and data-driven modeling. His contributions include foundational advancements in wavelet theory, diffusion maps, and nonlinear dimensionality reduction techniques. Key innovations include the development of empirical intrinsic geometry for analyzing complex systems and the use of Wasserstein distances in high-dimensional data analysis. His academic portfolio includes over 250 publications since the 1960s, spanning topics from theoretical mathematics to practical medical diagnostics. Notable applications include methods for stroke detection, medical imaging analysis, and anomaly detection in dynamic systems. Coifman collaborates across disciplines, integrating computational methods with domain-specific challenges in biology, chemistry, and engineering. Education: Ph.D. in Mathematics from the University of Geneva (1965) Awards: National Medal of Science (2001), Member of NAS (1993), Member of AAAS (2006) Key Projects: Development of diffusion maps, manifold learning algorithms, and empirical geometry frameworks Coifman's current research explores the intersection of machine learning and mathematical analysis, with recent focus on intrinsic data organization, emergent dynamical models, and scalable computational methods for large datasets.

Gabriele GrattonView profile

Professor

• Brain Function
• Brain Activity
• Event-related Optical Signal
• +7 more

Gabriele Gratton is a Professor in the Department of Psychology and Neuroscience Program at the University of Illinois Urbana-Champaign, and serves as a Theme Lead at the Beckman Institute for Advanced Science and Technology. With over 178 research outputs, Gratton has established a distinguished career in cognitive neuroscience with a focus on brain function, aging, and neuroimaging techniques. Gratton's research interests center around brain function and activity , particularly using Event-related Optical Signal (EROS) and other neuroimaging methods. Key areas include optical imaging , aging effects on cognition , event-related brain potentials , and cerebrovascular health . Much of their work investigates how vascular health, fitness, and aging interact to affect brain structure and cognitive performance across the lifespan. The research portfolio shows a strong trend toward multimodal neuroimaging approaches, with recent work focusing on trimodal brain imaging techniques that simultaneously investigate human brain function. There's also significant emphasis on understanding how physical activity and fitness impact cognitive aging, white matter integrity, and cerebrovascular health. Association for Psychological Science Fellow (2006) Foundation of Augmented Cognition Award (2005) President of Society for Psychophysiological Research (2009) SPR Award for Distinguished Contributions to Psychophysiology (2019) SPR Early Career Award (1997) Gratton has received extensive recognition for their work, with publications being picked up by news outlets and shared across social media platforms. Their research has been referenced by numerous scholars, with several papers accumulating significant reader attention on academic platforms like Mendeley. Much of this work is conducted through the Beckman Institute, where Gratton leads research themes focused on advanced brain imaging and cognitive neuroscience.

Edna AndrewsView profile

Professor

• Cognitive Neuroscience
• Linguistics
• Semiotics
• +7 more

Edna Andrews is the Nancy & Jeffrey Marcus Distinguished Professor of Slavic and Eurasian Studies and Professor of Linguistics and Cultural Anthropology at Duke University. She serves as Director of the Program in Linguistics, Director of Undergraduate Studies in the Linguistics Program, and Director of FOCUS, Duke's signature first-year seminar program. Her interdisciplinary work bridges Slavic studies, linguistics, anthropology, and cognitive neuroscience. Dr. Andrews earned her Ph.D. from Indiana University at Bloomington in 1984 and holds an honorary doctorate from St. Petersburg State University (Russia). Her academic journey has evolved from traditional Slavic linguistics to pioneering work at the intersection of language and brain sciences, reflecting her commitment to interdisciplinary scholarship. Professor Andrews' research focuses on cognitive neuroscience and multilingualism, semiotics (particularly Peircean and Lotmanian theories), and the neural basis of language processing. Her work examines how the brain processes multiple languages, the relationship between music and language cognition, and the semiotic foundations of linguistic structures. She has pioneered longitudinal fMRI studies of second language acquisition, challenging traditional linguistic models that often overlook the multilingual reality of most humans. Her research program has produced significant insights into how multilingualism affects brain structure and function, with implications for education, cognitive aging, and language rehabilitation. Andrews' work uniquely integrates theoretical linguistics with empirical neuroscience, creating a more comprehensive understanding of human language as situated within cultural and biological contexts. University Scholar/Teacher Award (2013) Richard Lublin Teaching Award Through her leadership of the Slavic & Eurasian Language Resource Center and numerous federally-funded grants from the U.S. Department of Education, Professor Andrews has secured significant research funding to advance understanding of language acquisition and multilingualism. Her collaborative approach brings together linguists, neuroscientists, anthropologists, and educators to tackle complex questions about language and the brain. Professor Andrews leads research teams through the Duke Institute for Brain Sciences and has developed innovative programs like Duke Intense Global (DIG), which combines intensive language training with neuroscience research. Her work with the Bass Connections in Brain & Society Research Team fosters interdisciplinary collaboration among students and faculty across multiple departments.

Anna VilanovaView profile

Professor

• Visual Analytics
• Explainable AI
• Biomedical Image Analysis
• +6 more

Anna Vilanova is a Full Professor in Visual Analytics at the Department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e), and is associated with the Electrical Engineering department's Signal Processing Systems. Previously, she served as Associate Professor at TU Delft (2013-2019) and Assistant Professor at TU/e (2002-2013). Her research focuses on Visual Analytics for high-dimensional data , explainable AI , and biomedical applications including Diffusion Weighted Imaging, 4D Flow, and Pangenomics. Education: Doctorate in Computer Graphics & Visualization (2001) Master in Computer Science (1997), Universitat Politècnica de Catalunya Research Highlights: Vilanova leads work on Visual Analytics systems for biomedical data, with recent publications in Diffusion MRI modeling , Tractography visualization , Explainable AI frameworks , and Pangenomic variant analysis . Her work bridges dimensionality reduction , uncertainty visualization , and medical imaging applications. Scientific Contributions: NWO-Veni grant (2005): "Visualization of global tensor information for diffusion tensor imaging" NWO-Aspasia grant (2013) Best Poster Award EuroVis (2025) Best Demo/Poster Awards (2022) Leadership & Service: Vilanova serves on the IEEE VIS Steering Committee , was EUROGRAPHICS President (2019-2022), and contributes to conferences like IEEE Visualization and EG-EuroVis . She co-founded the EAISI Health research initiative at TU/e.

Jie PengView profile

Professor

• Graphical Models
• High Dimensional Inference
• Functional Data Analysis
• +4 more

Jie Peng, Ph.D., is a Professor and Vice-Chair for Graduate Affairs in the Department of Statistics at the University of California, Davis. Her research focuses on statistical methodologies with applications in genomics, neuroimaging, and functional data analysis. She holds a Ph.D. from Stanford University and is actively involved in advancing statistical theory and its practical implementations in biomedical and computational sciences. Education Ph.D. in Statistics, Stanford University Research Interests Dr. Peng’s work spans several key areas including graphical models, high-dimensional inference, and neuroimaging analysis. She develops innovative statistical tools for analyzing complex biological data, such as tumor genomics and brain connectivity patterns derived from diffusion MRI. Her research emphasizes the interplay between theoretical statistics and real-world applications in healthcare and precision medicine. Professional Contributions She serves as an Associate Editor for the Journal of Computational and Graphical Statistics and has contributed to numerous high-impact publications. Her methods have been applied to identify biomarkers in ovarian cancer and to study brain lateralization using Human Connectome Project (HCP) data. Dr. Peng also leads efforts in developing computational frameworks for spatial transcriptomics and dynamic network modeling.