Find Academic Supervisors

Politi PierluigiView profile

Professor

• Psychiatry
• Autism Spectrum Disorder
• Schizophrenia
• +3 more

Politi Pierluigi is a Full Professor of Psychiatry at the University of Pavia, Italy. He serves as Head of the Psychiatric Unit at ASST Pavia and previously held leadership roles including Head of the School of Specialization in Psychiatry and President of the Rehabilitation Graduate Program. His academic career spans over four decades, including a Medical Degree (1981, cum laude), Psychiatry Specialization (1985, cum laude), and a PhD in Psychiatry (1997). Roles: Full Professor (2011–present), Department Head (various roles), Visiting Professor at Anglia Ruskin University (2011–2017). Research Focus: Adult Autism Spectrum Disorder, Schizophrenia, Psychiatric Epidemiology, and Music Psychology. Education: MD in Medicine and Surgery, University of Pavia (1981). Specialization in Psychiatry, University of Pavia (1985). PhD in Psychiatry and Relationship Sciences, University of Milan (1997). Research interests integrate clinical psychiatry with biomedical data science. His current projects include studying religious beliefs' impact on suicidal ideation, music-based interventions in surgery, and neuroimaging correlates of schizophrenia symptoms. He also investigates sensory and biological markers in autism and intellectual disability. Active Grants and Collaborations: His work spans international collaborations (e.g., South Korea for religious beliefs study) and integrates interdisciplinary teams across psychiatry, neuroscience, and data science. No specific grants or awards are listed. Labs/Teams: Leads the Psychiatric Unit at ASST Pavia and collaborates with the Rehabilitation Graduate Program at the University of Pavia.

• Mathematical data processing
• Imaging sciences
• Ill-posed inverse problems
• +5 more

Mohamed Jalal Fadili is a Professor in Image and Signal Processing at the University of Caen, France, affiliated with GREYC CNRS UMR 6072 and ENSICAEN. His work bridges mathematical data processing, optimization, and computational harmonic analysis with applications in medical and astronomical imaging. Education: Engineering Degree (1996), MSc (1996), PhD (1999), and Habilitation (2010) in Signal and Image Processing from the University of Caen Research interests span ill-posed inverse problems, sparse recovery algorithms, nonsmooth and nonconvex optimization, and statistical learning. He collaborates on projects like Σ-Vision ERC (co-leader) and Sparse-Astro ERC , focusing on super-resolution and low-complexity models. Scientific awards include being a Junior member of the Institut Universitaire de France (2013-2018). He has advised numerous PhD students and postdoctoral researchers, many of whom now hold academic or research positions globally. Software: MCALab (Morphological Component Analysis), various toolboxes for image decomposition, inpainting, denoising, and fMRI analysis Organized minisymposiums at SIAM conferences on operator splitting algorithms, sparse recovery, and super-resolution

• Post-Traumatic Stress Disorder (PTSD)
• Neuroscience
• Decision-Making Under Uncertainty
• +4 more

Nachshon Korem is an Associate Research Scientist in Psychiatry at Yale School of Medicine. He holds a PhD in Biological Psychology from the University of Haifa (2019). His research focuses on understanding how psychopathology and prior experience shape decision-making processes at behavioral and neural levels, particularly in PTSD and trauma-exposed populations. Education: PhD in Biological Psychology, University of Haifa, 2019 Research Interests: PTSD mechanisms and treatment outcomes Decision-making under uncertainty Cannabinoid receptor involvement in trauma responses Neuroimaging of fear memory consolidation Ketamine neurobiology in psychiatric disorders His work integrates computational modeling, neuroimaging, and clinical data to advance PTSD understanding. Publications: Over 20 peer-reviewed articles in high-impact journals like JAMA Network Open , Biological Psychiatry , and Psychopharmacology . Recent work includes connectome-based PTSD prediction models and cannabinoid receptor imaging in trauma survivors. Awards: 2023 Leading Research Achievement Award (Brain & Behavior Research Foundation) Lab Affiliation: Harpaz-Rotem Lab, Yale School of Medicine

Pelagie BeesonView profile

Professor

• Aphasia
• Neurorehabilitation
• Cognitive Neuroscience
• +3 more

Pelagie Beeson is a Professor in the Department of Speech, Language, and Hearing Sciences at the University of Arizona. Her work focuses on neurorehabilitation of language disorders, particularly in primary progressive aphasia (PPA), apraxia of speech, and acquired dysgraphias/alexias. She leads the Beeson Lab , investigating neural mechanisms underlying language recovery and developing evidence-based treatments. Her research integrates neuroimaging (fMRI, DTI) with behavioral interventions to understand how treatments like phonological therapy and transcranial stimulation impact language networks. Key contributions include demonstrating durable improvements in written language for logopenic PPA patients and advancing structured text messaging interventions for aphasia. Beeson advocates for holistic admissions reform at land-grant institutions and collaborates with interdisciplinary teams on translational research. Her work emphasizes bidirectional links between cognitive models and clinical practice, with a focus on maximizing functional communication outcomes.

• Computational Biology
• Machine Learning
• Biomedical Data Science
• +3 more

David van Dijk is an Assistant Professor at Yale School of Medicine with joint appointments in the Department of Internal Medicine and Department of Computer Science . He leads a research group focused on applying machine learning to complex biomedical data. PhD in Computer Science (University of Amsterdam & Weizmann Institute) Postdoctoral Fellow (Yale Genetics & Computer Science) Research Interests span computational biology, deep learning, single-cell analysis, and neuroscience. His group develops algorithms for biomedical data including: Single-cell RNA sequencing Microbiome analysis Medical imaging Electronic health records His 2025-2024 publications focus on: AI-driven tumor heterogeneity analysis Neuroimaging markers for psychiatric conditions Transformer models for clinical data Neural integral equations Language models for biological data Scientific Recognition : 2025 NSF CAREER Award 2025 Google Cloud Award 2025 Roberts Innovation Award 2024 Stellar Abstract Award 2024 Colton Center for Autoimmunity Award He mentors trainees across disciplines and co-develops the vandijklab.org research platform.

• Speech Perception
• Language Processing
• Cognitive Neuroscience
• +3 more

Daniel Kleinman is an Associate Research Scientist at the Yale Child Study Center within the Yale School of Medicine. He holds a PhD in Psychology & Cognitive Science from the University of California, San Diego and a BA in Linguistics & Cognitive Science from Pomona College. His research focuses on speech perception, language processing, and cognitive neuroscience, with particular emphasis on autism spectrum disorders and bilingualism. Dr. Kleinman completed postdoctoral training at Haskins Laboratories (2020), University of Illinois at Urbana-Champaign (2018), and University of California, San Diego (2015). His research utilizes advanced methodologies including eye-tracking, event-related potentials, and neuroimaging to investigate speech and language processing across different populations. His research interests span multiple areas including speech perception in autism spectrum disorders, bilingual language control, phonemic restoration, and the neural correlates of language processing. Dr. Kleinman's work often examines how visual and auditory information integrate during speech perception and how these processes differ in neurodiverse populations. Dr. Kleinman has published extensively on topics related to language processing, with recent work examining the effects of the COVID-19 pandemic on language development, the temporal dynamics of bilingual language control, and neural correlates of speech perception in children with autism. His research shows consistent focus on methodological rigor while addressing clinically relevant questions about language development. His research has significant implications for understanding typical and atypical language development and may inform interventions for children with communication disorders, particularly those on the autism spectrum.

• Social Neuroscience
• Decision Neuroscience
• Computational Modeling
• +3 more

Ali Mahmoodi is a Postdoctoral Researcher specializing in Social and Decision Neuroscience at the University of Oxford. His work bridges electrical engineering and neuroscience, focusing on the neural mechanisms underlying social decision-making, conformity, and group dynamics. He utilizes advanced techniques such as fMRI, TMS, and computational modeling to investigate how humans process and integrate social information. Education: BSc in Electrical Engineering (Control Branch) MSc in Social Decision-Making (Psychophysics & Computational Modelling) PhD in Neuroscience (Neural Basis of Change of Mind in Social Contexts) Research Interests: Ali explores the 'why' behind neural operations in social contexts. Key areas include: Structuring social knowledge in multidimensional environments Attribution of group outcomes to individual competence Neural substrates of informational vs. normative conformity Frontopolar-temporal circuits in primate decision-making His work employs representational similarity analysis (RSA) and investigates how the brain selects relevant social information while disregarding irrelevant data. Publications: His recent work spans social cognition mechanisms in humans and macaques, including studies on hippocampal-prefrontal interactions during social inference and neural systems controlling motivational states. These studies highlight interdisciplinary approaches combining neuroscience with behavioral experiments. Grants & Advising: While no explicit grants are listed, his research is supported by collaborative projects at Oxford. No advisees are currently listed. Labs/Teams: Affiliated with the Social and Decision Neuroscience research group at Oxford, focusing on translational research between computational models and neuroimaging data.

• Biomedical Engineering
• Neuroimaging
• Cerebrovascular Disease
• +3 more

Dr. Joana Pinto is a Senior Research Associate at the University of Oxford's Department of Biomedical Engineering and a member of St Peter's College. She holds roles as Principal Investigator of the Maternal Brain Project and founder of the Women’s Health Book Club. Previously, she was a Postdoctoral Researcher in Professor Daniel Bulte’s group (2020–2023) and a Junior Research Fellow at Kellogg College (2021–2024). Education: PhD in Biomedical Engineering, University of Lisbon (2019) MSc and BSc (degrees unspecified) Research Interests: Non-invasive neuroimaging techniques for cerebrovascular disease biomarkers Maternal brain dynamics using advanced MRI Integration of cerebral blood flow (CBF) and cerebrovascular reactivity (CVR) metrics Her work bridges clinical MRI applications and women’s health, particularly pregnancy’s neurological impacts. Key Projects: Maternal Brain Project (Funders: ARUK Thames Valley, Wellcome Centre, John Fell Fund) Eye-Brain Project (EPSRC-funded) Awards: Early Career Fellowship (ESMRMB, 2021) Junior Fellowship (ISMRM, 2023) Premio Príncipe da Beira (2023) Grants & Leadership: Lead researcher in multi-funder maternal health initiatives Collaborator on EPSRC neuroimaging projects Lab Affiliations: Biomedical Image Analysis Group Maternal Brain Project team

Annmarie MacNamaraView profile

Associate Professor

• Affective Science
• Clinical Neuroscience
• Psychophysiology
• +3 more

Annmarie MacNamara is an Associate Professor in the Department of Psychiatry and Behavioral Sciences at Texas A&M University, leading the Multimethod Affect and Cognition (MAC) lab. Her research integrates neuroimaging and psychophysiological techniques such as fMRI, ERP, and eyeblink startle to investigate emotional processing in psychiatric health and disease. Her work focuses on understanding how emotional responses become maladaptive in anxiety and depression, aiming to improve diagnostic accuracy and treatment efficacy. Key research areas include affective neuroscience, cognitive factors influencing emotional reactivity, and translational approaches to mental health. She collaborates with the Institute for Neuroscience and actively accepts graduate students for 2024-2025. Her recent studies explore error-processing mechanisms, neurobiological predictors of anxiety/depression, and the generalization of emotion regulation strategies. Though no specific awards are listed, her work emphasizes high-impact translational research. Publications span ERP-based emotion regulation reviews, transdiagnostic fear models, and neuroimaging studies of comorbidity. Her lab's interdisciplinary approach bridges clinical and basic science to address emotional disorders' neural underpinnings.

• Cognitive Neuroscience
• Neurophysiology
• Language Processing
• +4 more

Matthias Schlesewsky is a Professor at the University of South Australia (UniSA), affiliated with UniSA Justice and Society. His research focuses on cognitive neuroscience, neurophysiology, and language processing, with a particular emphasis on EEG analysis and mindfulness interventions. He leads studies on how mindfulness training impacts resting-state EEG parameters in high-demand cohorts, such as military personnel. His work also explores the neurobiological foundations of syntax processing, predictive mechanisms in memory, and the role of individual alpha frequency in cognitive tasks. Key affiliations include the Cognitive Neuroscience Laboratory and the Australian Research Centre for Interactive and Virtual Environments. His research integrates experimental psychology, neuroimaging, and computational modeling to investigate language comprehension, attention, and decision-making. Notable projects include studies on EEG-based cognitive workload monitoring in military training and the effects of mindfulness on dynamic task performance. His articles highlight interdisciplinary approaches, combining EEG, ERP, and behavioral data to understand neural mechanisms underlying language, memory, and attention. Collaborations span institutions like Georgetown University, the University of Miami, and the Defence Science and Technology Group.

• Affective Neuroscience
• Cognitive Neuroscience
• Emotional Processing
• +3 more

Margaret Bradley is a Research Professor at the University of Florida , affiliated with the Center for the Study of Emotion and Attention . Her research focuses on neural and physiological mechanisms underlying affective and attentional processing, particularly in individuals with anxiety disorders. She employs neuroimaging techniques (fMRI, EEG) and physiological measures (heart rate, skin conductance) to study emotion, attention, and memory. Education details are not explicitly stated, but her work emphasizes interdisciplinary approaches to understanding emotional and cognitive processes. Key research areas include subcortical-cortical neural circuits, emotional memory, and psychophysiological responses to stimuli. Her studies often involve collaborations, as seen in co-authored works with researchers like Peter Lang and Valerio Costa. Publications from 2010–2011 highlight trends in emotion and attention, including ocular responses in Parkinson’s disease, defensive reflexes, and memory for emotional stimuli. While no formal awards are listed, her prolific output reflects sustained contributions to affective neuroscience. Collaborative projects at the Center emphasize translational research linking brain activity to behavioral outcomes. Her lab investigates mechanisms of emotion and attention, integrating neuroimaging and physiological data to map cognitive processes. Ongoing work explores how emotional states influence memory consolidation and attentional focus, with potential implications for clinical conditions like PTSD and anxiety disorders.

Zois BoukouvalasView profile

Assistant Professor

• Machine Learning
• Interpretable Models
• Multimodal Data Analysis
• +6 more

Zois Boukouvalas is an Assistant Professor in the Mathematics and Statistics Department at American University. His research focuses on interpretable machine learning models for analyzing multi-modal data, integrating information geometry, statistics, and optimization. He applies these methods to biomedical imaging, social/linguistic trends, and chemical data for drug discovery. Boukouvalas holds a PhD in Applied Mathematics from UMBC (2017) and has received grants from the Energetics Technology Center for projects on machine learning in energetic materials and misinformation detection. Education: BS in Mathematics (University of Patras, Greece), MS in Applied and Computational Mathematics (Rochester Institute of Technology), MS and PhD in Applied Mathematics (UMBC). His work bridges theoretical foundations with practical applications, emphasizing ethical AI and underrepresented student inclusion. Recent contributions include panel discussions on AI in energetic materials and co-organizing conferences on latent variable methods in machine learning. Research interests span misinformation detection, multimodal learning, and algorithmic transparency. He has published extensively on topics like hate speech detection, stock market prediction via news data, and data fusion techniques for high-impact events. Awards highlight his innovation in human-assisted machine learning and materials discovery.

• Neuroimaging
• Psychiatry
• Medical Physics
• +7 more

Ansel Hillmer, PhD, is an Adjunct Associate Professor in the Department of Psychiatry at Yale University, serving as the Associate Director of Imaging for the Positron Emission Tomography (PET) program. His primary affiliation is with the Yale School of Medicine. He holds additional roles in the Bioimaging Sciences Center, Center for Brain & Mind Health, and directs the Hillmer Lab. Hillmer’s research focuses on developing novel PET radiotracers and imaging methodologies to study neurobiological mechanisms in psychiatric disorders such as PTSD and alcohol use disorder. He also explores dopamine and glutamate systems, synaptic density quantification, and cortisol regulation in the brain. Education includes a BS in Physics from Valparaiso University (2009), followed by MS and PhD in Medical Physics from the University of Wisconsin, Madison (2011–2014). He completed a postdoctoral fellowship at Yale University (2016). His work integrates interdisciplinary approaches, combining engineering, AI, and clinical neuroscience to advance translational research. Key projects include characterizing immune-related targets in the brain, studying acute alcohol effects on neuroimmune systems, and assessing cannabis-induced dopamine release. He has secured significant grant funding, including a 2022 National Institute on Alcohol Abuse and Alcoholism (NIAAA) grant for developing a PET tool targeting colony stimulating factor receptor 1. His research emphasizes multimodal imaging data analysis (e.g., fMRI-PET fusion) and methodological innovations like Independent Component Analysis (ICA) for radiotracer interpretation. Hillmer collaborates extensively with colleagues such as Richard Carson, Kelly Cosgrove, and Irina Esterlis, contributing to peer-reviewed studies across neuroimaging and translational medicine.

• Mental Health Treatment Outcomes
• Predictive Modeling in Psychiatry
• Machine Learning Applications in Healthcare
• +4 more

Adam Chekroud is an Adjunct Assistant Professor in Psychiatry at Yale School of Medicine. His research focuses on improving mental health treatment outcomes through predictive modeling, machine learning, and large-scale data analysis. He co-founded Spring Health, a mental health startup providing employer-based care to companies like Gap and Whole Foods. His work spans depression treatment optimization, workplace mental health programs, and the ethical application of AI in psychiatry. Education: PhD in Psychology, Yale University (2018) MSc in Neuroscience, University of Oxford (2013) BA in Experimental Psychology, University of Oxford (2010) Research Interests: Adam's research integrates computational methods with clinical psychiatry to address gaps in personalized treatment. He develops statistical models predicting treatment initiation barriers, medication efficacy, and relapse risks. His work emphasizes real-world applications, such as employer-based mental health programs and scalable solutions for depression. Key Contributions: Highlighted in JAMA Psychiatry , Lancet Psychiatry , and Science Forbes 30 Under 30 in 2017 Co-developed Spring Health, raising over $8M and serving 600+ providers Lab/Team: Spring Health collaborates with Yale researchers to advance clinical prediction models and workplace mental health interventions. His academic work bridges computational psychiatry and translational research.

• Neuroimaging
• Medical Imaging
• Biomedical Engineering
• +3 more

Patrícia Figueiredo is a Full Professor at the Department of Bioengineering, Instituto Superior Técnico, University of Lisbon, and Principal Investigator at the Evolutionary Systems and Biomedical Engineering Lab (LaSEEB) within the Institute for Systems and Robotics (ISR-Lisboa). She holds a DPhil in Neuroimaging from the University of Oxford (2003) and has been a leading figure in biomedical engineering and neuroscience research. Her work focuses on noninvasive neuroimaging techniques, particularly MRI and EEG integration, to study brain function in health and neurological disorders. Education: Bachelor's in Physics Engineering, Instituto Superior Técnico (1996) DPhil in Neuroimaging, University of Oxford (2003) Research interests include cerebrovascular dynamics, multimodal neuroimaging (EEG-fMRI), neuromodulation, and applications in epilepsy, migraine, stroke, and psychiatric disorders. She has supervised over 20 PhD/MSc students and led numerous national/international grants, such as MIG_N2Treat and NeurAugVR. Awards include L’Oréal Portugal’s Women in Science Prize and the António Xavier Prize for her advisory work in MRI research. Teaching responsibilities include courses on Medical Imaging, Neuroimaging, and Neuroengineering. She actively participates in academic governance at IST and holds leadership roles in societies like ESMRMB (Vice-President) and ISMRM’s Brain Function Study Group (Vice-Chair). Labs/Teams: LaSEEB (ISR-Lisboa), collaborations with global institutions like UCL, EPFL, and the Martinos Center for Biomedical Imaging.