Find Academic Supervisors

Carolyn EmeryView profile

Professor

• Injury Prevention
• Concussion Research
• Pediatric Rehabilitation
• +3 more

Dr. Carolyn Emery is a Professor in the Faculty of Kinesiology at the University of Calgary, with joint appointments in Pediatrics and Community Health Sciences at the Cumming School of Medicine. She holds leadership roles as Chair of the Sport Injury Prevention Research Centre (an International Olympic Committee Research Centre) and the Alberta Children’s Hospital Research Institute. Her affiliations include the McCaig Institute for Bone and Joint Health, Hotchkiss Brain Institute, and O’Brien Institute for Public Health. Dr. Emery’s education includes a BScPT from Queen’s University (1988), MSc in Epidemiology from the University of Calgary (1999), and PhD in Epidemiology from the University of Alberta (2004). Her research focuses on injury prevention in youth sports, concussion, and pediatric rehabilitation, aiming to reduce long-term health consequences such as osteoarthritis and post-concussion syndrome. She leads major grants including CIHR’s “SHRed Injuries” and NFL-funded concussion surveillance programs. Her research interests emphasize practical interventions for injury prevention, including neuromuscular training and policy changes. Recent work explores concussion outcomes in adolescents, biomechanical injury mechanisms, and global sport safety initiatives. She has published extensively on topics like sport-related musculoskeletal injuries, concussion management, and the epidemiology of youth sports. Dr. Emery has received over 20 awards, including the Royal Society of Canada Fellowship, Killam Professorship, and CIHR Canada Research Chair. Her work bridges clinical practice, policy, and community engagement to enhance public health outcomes in sports and youth recreation. Her lab, the Sport Injury Prevention Research Centre, collaborates internationally to develop evidence-based strategies for injury prevention. Current projects include evaluating the impact of tackle laws in rugby, head acceleration in wrestling, and long-term consequences of pediatric concussions.

Pascal FrossardView profile

Professor

• Machine Learning
• Data Science
• Graph Signal Processing
• +6 more

Pascal Frossard is a Full Professor at the Department of Electrical Engineering in the School of Engineering (STI) at EPFL, with a courtesy appointment in the School of Computer and Communication Sciences. He founded and directs the LTS4 laboratory since 2003, co-leads the EPFL AI Center and Swiss Data Science Center, and serves as Associate Dean for Research at STI. Research Focus: Machine Learning, Graph Signal Processing, AI Applications in Healthcare, Computer Vision Academic Leadership: IEEE Fellow, ELLIS Fellow, Conference Chair roles Key Projects: Digital Pathology for Oncology, Cardiac Digital Twins, Robust Machine Learning Research Interests: His work bridges signal processing, machine learning, and applied mathematics, emphasizing biomedical applications. Recent research includes adversarial robustness in classifiers, network representation learning, and 360-degree video analysis. Scientific Awards: IEEE Fellow ELLIS Fellow Leadership in IEEE technical committees Advising & Grants: Supervised 20+ PhD students and postdocs. Secured major grants from PHRT, Hasler Foundation, FNS-Sinergia, Armasuisse, Google, and Cisco.

Harris H. WangView profile

Associate Professor

• Systems Biology
• Synthetic Biology
• Genome Engineering
• +17 more

Harris H. Wang is an Associate Professor in the Department of Systems Biology and Department of Pathology and Cell Biology at Columbia University's Vagelos College of Physicians and Surgeons, where he also serves as Interim Chair of Systems Biology. He is affiliated with the Center for Computational Biology and Bioinformatics (C2B2) and the Integrated Program in Cellular, Molecular and Biomedical Studies (CMBS). B.S., Physics and Mathematics, MIT Ph.D., Biophysics, Harvard University Dr. Wang's research lies at the intersection of systems and synthetic biology, focusing on developing foundational technologies for genome engineering, microbiome manipulation, and synthetic genomics. His lab pioneers methods such as MAGE, MAGIC, CAST, and CAMII to enable high-throughput genetic manipulation, in situ microbiome engineering, and AI-driven microbial culturomics. Key research themes include understanding microbial community dynamics, engineering cellular memory systems, designing biocontained genetic circuits, and applying synthetic biology to human health challenges in personalized medicine and infectious disease. His recent publications reveal a strong trend in spatial and functional metagenomics, CRISPR-based microbiome editing, and synthetic biology tools for data storage and genetic stability. The articles span high-impact journals like Nature , Science , and Nature Biotechnology , reflecting his leadership in developing scalable, programmable biological systems. Scientific Awards: NIH Director’s Early Independence Award Forbes 30 Under 30 in Science Sloan Research Fellowship NSF CAREER Award ONR Young Investigator Award Burroughs Wellcome Fund PATH Award Schaefer Scholar Blavatnik National Award Vilcek Prize PECASE Dr. Wang has advised numerous PhD and postdoctoral researchers, many of whom have gone on to independent scientific careers. His lab is supported by major grants from NIH, NSF, DARPA, DOE, and foundations including the Bill & Melinda Gates Foundation and CZ Biohub NY. He is actively involved in educational initiatives, including organizing Columbia’s iGEM team and the Cold Spring Harbor Laboratory Synthetic Biology course. The Wang Lab is based at the Columbia University Irving Medical Center and is part of national consortia such as the Engineering Biology Research Consortium (EBRC) and the Genome Project-Write (GP-Write) initiative. The lab develops and applies cutting-edge technologies in automation, machine learning, and synthetic biology to engineer microbiomes for applications in medicine, global health, and climate change.

• Tissue Engineering
• Biofabrication
• Cartilage Regeneration
• +3 more

Prof. Marcy Zenobi-Wong is a Full Professor at ETH Zurich's Department of Health Sciences and Technology, specializing in biofabrication and tissue engineering. Her research focuses on cartilage regeneration using advanced biomaterials, including nanofilm coatings and 3D printing techniques. She holds patents in tissue engineering and has pioneered methods like filamented light (FLight) biofabrication for creating anisotropic tissues. Her academic journey includes a B.Sc. from MIT (1985), M.Sc. and Ph.D. from Stanford (1987, 1990), followed by postdoctoral work at the University of Michigan. She leads the Biofabrication Group at ETH, developing therapies for joint repair and regenerative medicine. Courses taught include Biomedical Engineering and Materials and Mechanics in Medicine . Research highlights include engineered hydrogels for cartilage protection, CRISPR-driven gene editing in chondrocytes, and biohybrid neural interfaces. Her work bridges material science, cell biology, and clinical applications, with a focus on translational medicine. Collaborative projects involve creating elastic cartilage grafts for microtia reconstruction and volumetric printing of complex tissue constructs.

• Neuroscience
• Alzheimer's Disease
• Metabolism
• +2 more

Aisha Ameen is a Researcher in the Department of Veterinary and Animal Sciences (Pathobiological Sciences) at the University of Copenhagen's Faculty of Health and Medical Sciences, holding dual appointments as Research Assistant and Guest Researcher. Her research centers on Alzheimer's disease mechanisms with emphasis on astrocyte metabolism, glucose/glutamate dynamics, and short/medium-chain fatty acid roles. She employs functional metabolic mapping, dMRI, immunohistochemistry, and hiPSC models to investigate neurodegenerative processes at cellular and systemic levels. Analysis of her 2022-2025 publications reveals consistent focus on metabolic dysregulation in Alzheimer's pathology, particularly astrocyte energy substrate competition and fatty acid interventions. Her work bridges neuroscience, metabolism, and translational disease modeling with significant citation impact and media attention.

Jean-Philippe ThiranView profile

Professor

• Computational Imaging
• Medical Image Analysis
• Diffusion MRI
• +5 more

Prof. Jean-Philippe Thiran is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he serves as Director of the Signal Processing Laboratory (LTS5) and Director of the Institute of Electrical and Micro Engineering. He also maintains a part-time Associate Professor position with the Department of Radiology of the University Hospital Center (CHUV) and University of Lausanne (UNIL). Born in Namur, Belgium in 1970, he received his Electrical Engineering degree and PhD from the Université catholique de Louvain (UCL), Belgium, in 1993 and 1997 respectively. He joined EPFL in 1998 and has established himself as a leading researcher in computational imaging. His research focuses on computational imaging , with significant contributions to medical image analysis (particularly diffusion MRI, ultrasound imaging, and digital pathology) and computer vision . His recent work integrates advanced modeling, simulation, and machine learning techniques to extract microscopic tissue information from macroscopic MRI signals. This approach combines hyper-realistic synthetic tissue models, advanced Monte-Carlo simulations, and ML-based estimation techniques for brain microstructure analysis with potential applications to other tissues. Senior Member of IEEE Fellow of the European Association for Signal Processing (EURASIP) Prof. Thiran has authored or co-authored 1 book, 9 book chapters, 250 journal papers and over 270 peer-reviewed conference papers, and holds 12 international patents. He previously served as Co-Editor-in-Chief of the Signal Processing journal (2001-2005) and associate editor of IEEE Transactions on Image Processing. He has chaired major conferences including EUSIPCO 2008 and IEEE ICIP 2015. His laboratory at EPFL brings together interdisciplinary researchers to develop innovative imaging techniques that bridge macroscopic measurements and microscopic tissue properties, with significant potential for medical diagnostics and treatment planning applications.

• Anatomy
• Morphology
• Vascular Anatomy
• +4 more

Prof. Dr. Kenan Aycan is a Professor of Anatomy at Ahi Evran University's Faculty of Medicine, Department of Basic Medical Sciences, where he has served since 2019. He also holds the position of Department Head since 2020. Previously, he worked at Erciyes University as a School Director from 2011-2016. His academic career spans over four decades with significant contributions to anatomical sciences. Dr. Aycan earned his PhD in Basic Medical Sciences from Ege University's Faculty of Medicine (1983-1986) following his Bachelor's degree in Science from Ege University's Faculty of Science (1970-1975). He also holds a Certificate of Use of Experimental Animals from Erciyes University (2008). His research focuses on anatomical morphology, vascular structures, and developmental processes. Dr. Aycan has pioneered anatomical techniques including the 'Aycan's method' for corrosion preparations. His work spans comparative anatomy across various species, morphometric analyses of anatomical structures, and investigations into teratogenic effects and protective agents. He has extensively studied the foramen magnum using golden ratio principles, vascular anatomy of reproductive organs in ruminants, and auditory ossicles in sheep. Analysis of his recent publications (2021-2025) reveals consistent focus on anatomical methodology development, morphometric studies of key anatomical structures, vascular anatomy investigations, and research on developmental processes and teratology. His work often employs plastic injection and corrosion techniques, CT imaging, and comparative approaches across human and animal models. Dr. Aycan has mentored numerous graduate students, serving as primary advisor for over 20 Master's and PhD theses covering diverse anatomical topics from vascular variations to developmental studies. His collaborative network includes researchers like Tufan Ulcay, Burcu Kamaşak, and others across Turkish institutions.

Christopher KananView profile

Associate Professor

• Artificial Intelligence
• Deep Learning
• Continual Learning
• +5 more

Christopher Kanan is a tenured Associate Professor of Computer Science at the University of Rochester, leading the AI Initiative within the Hajim School of Engineering & Applied Sciences. He holds secondary appointments in Brain and Cognitive Sciences, the Goergen Institute for Data Science and AI (GIDS-AI), and the Center for Visual Science. His research focuses on deep learning systems for artificial general intelligence (AGI), including continual learning, medical computer vision, and visual question answering. Previously, he was an Associate Professor at RIT’s Carlson Center for Imaging Science and a leader at Paige.AI, contributing to the FDA-cleared Paige Prostate system. Kanan earned his PhD from UC San Diego, completed postdoctoral work at Caltech, and worked at NASA JPL. Education: PhD in Computer Science, UC San Diego MS in Computer Science, University of Southern California Bachelor’s in Philosophy and Computer Science, Oklahoma State University Research Interests: Kanan’s work spans foundational AI capabilities like continual learning, medical imaging (pathology and radiology), multi-modal reasoning, and cognitive science-inspired models. His lab develops bias-robust AI systems and applies deep learning to healthcare and fusion research. Articles Trends: His recent work emphasizes out-of-distribution generalization, foundation models in pathology, and stability in continual learning. Key themes include AI applications in healthcare, model robustness, and neuroscience-inspired algorithms. Awards: NSF CAREER Award Senior Member, AAAI and IEEE DoE and NSF grants totaling $5M+ DARPA/ARL awards Advising & Grants: Mentored over 10 PhD students, including Robik Shrestha and Usman Mahmood. Secured grants for AI in nuclear fusion and medical imaging. Led RIT’s Center for Human-aware AI (CHAI) as Associate Director. Labs & Teams: Heads the University of Rochester AI Initiative, collaborates with Paige.AI, and leads teams advancing AI in pathology and robotics. His lab’s KLab (klab.cis.rit.edu) focuses on vision and learning systems.

• Brain Plasticity
• Motor Learning
• Neurorehabilitation
• +5 more

Dr. Cassandra Sampaio Baptista is a Lecturer at the University of Glasgow's School of Psychology & Neuroscience. Her research focuses on brain plasticity in adulthood, particularly exploring how experiences like skill learning or rehabilitation influence structural and functional changes in the brain. She employs neuroimaging techniques such as fMRI neurofeedback and MRI to investigate mechanisms of myelin and white matter plasticity. Her work emphasizes translational applications, including stroke rehabilitation and promoting healthy aging. Key contributions include demonstrating myelin's role in motor learning and developing MRI protocols for white matter analysis. She collaborates on projects funded by the BIAL Foundation (2025–2026) and has supervised multiple postgraduate students. Recent publications highlight studies on oligodendrocyte dynamics, neurofeedback interventions for stroke survivors, and cross-species neuroscience approaches. While no specific awards are listed, her extensive publication record reflects her leadership in neuroplasticity research.

• Biomedical Data Privacy
• Genomics
• Machine Learning
• +3 more

Hyunghoon Cho is an Assistant Professor at Yale School of Medicine in the Department of Biomedical Informatics & Data Science, with a secondary appointment in the Department of Computer Science. He received his PhD in Electrical Engineering and Computer Science from MIT (2019) and MS/BS in Computer Science from Stanford University (2013). His research focuses on computational challenges in biomedical data privacy, single-cell genomics, and network biology. Assistant Professor (Primary): Biomedical Informatics & Data Science Assistant Professor (Secondary): Computer Science Appointments: Yale School of Medicine | Broad Institute (Schmidt Fellow) Research Themes: Privacy-Enhancing Technologies for genomic and health data Scalable AI/ML tools for omics data analysis Structured biological modeling for system-level discovery His work includes secure GWAS, transcriptomic privacy assessment, and sfkit - a federated genomic analysis toolkit. He received the NIH Director's Early Independence Award and leads NSF-funded projects on confidential genome analytics. Awards: NIH Director's Early Independence Award Lab Members: Haris Smajlović (Postdoc), Vincent Angelo (CBB MS), Denis Loginov (Senior Software Engineer), Lucy Zheng (CBB PhD)

Birgit FrauscherView profile

Professor

• Epilepsy
• Sleep Medicine
• Electroencephalography
• +4 more

Dr. Birgit Frauscher is the Lincoln Financial Group Distinguished Professor in Neurobiology at Duke University School of Medicine, where she serves as Professor of Neurology and holds a secondary appointment in the Department of Biomedical Engineering at the Duke Pratt School of Engineering. She is currently the Director of the Duke Comprehensive Epilepsy Center and leads the Analytical Neurophysiology (ANPHY) Lab. Her clinical and research work focuses on epilepsy and sleep medicine, utilizing both invasive and non-invasive electrical recordings to study brain activity in humans. Dr. Frauscher completed her medical training, neurology residency, and subspecialty training in electroencephalography, epilepsy, and sleep medicine at the Medical University of Innsbruck in Austria. After completing her clinical training in 2008, she earned her habilitation degree in 2011. She further specialized in intracranial EEG and signal analysis during a visiting professorship at the Montreal Neurological Institute and Hospital, McGill University (2013-2015), where she later served as an Attending Epileptologist and Group Leader of Epilepsy. Her research interests focus on developing novel seizure-independent EEG markers for the epileptogenic zone, investigating sleep-epilepsy interactions, and using intracranial EEG to study brain physiology during wakefulness and sleep. Her work aims to improve epilepsy diagnosis, prognosis, and treatment outcomes by better localizing the epileptic focus. Dr. Frauscher's recent publications demonstrate her continued leadership in epilepsy research, with over 170 peer-reviewed papers and an H-index of 62. Dr. Frauscher has received several prestigious awards including the Clinician-Scientist awards of the FRSQ (2018-2023), the Michael Prize of the International League against Epilepsy (2019), and the Ernst Niedermeyer Prize from the Austrian Epilepsy Society (2015). Her scholarly work has significantly advanced clinical knowledge in epilepsy and sleep medicine, establishing her as a leading figure in the field. As Director of the Duke Comprehensive Epilepsy Center and head of the ANPHY lab, Dr. Frauscher oversees a research program dedicated to advancing neuroscience through innovative approaches to studying brain activity. Her lab employs quantifiable tools to investigate neurophysiological and pathological processes related to epilepsy and sleep, with the ultimate goal of improving patient outcomes through better understanding of brain function.

John P. O'DohertyView profile

Professor

• Decision Neuroscience
• Reward-related Learning
• Computational Modeling
• +3 more

John P. O'Doherty serves as the Fletcher Jones Professor of Decision Neuroscience within Caltech's Division of Humanities and Social Sciences, holding continuous faculty appointments since 2004 (Assistant Professor 2004-07, Associate Professor 2007-09, Professor 2009-present, Fletcher Jones Professor 2021-present). He previously directed the Caltech Brain Imaging Center (2013-17) and maintains affiliations with the T&C Chen Center for Social and Decision Neuroscience. His educational background includes a B.A. from University of Dublin, Trinity College (1996) and D.Phil. from University of Oxford (2000). His research focuses on computational and neural mechanisms of reward-based learning and decision-making , employing fMRI, intracranial recordings, and mathematical modeling to investigate how the brain solves complex decision problems through evolutionarily conserved algorithms. Key areas include Reinforcement learning systems (model-based/model-free arbitration) Observational and social learning mechanisms Neural representation of value, risk, and uncertainty Computational phenotyping of mental disorders Temporal dynamics of goal persistence Analysis of his 2023-2025 publications reveals dominant trends in computational psychiatry (problem gambling, autism traits), hierarchical decision-making, and neuroeconomic modeling of social behavior. His work consistently integrates cross-species computational frameworks with human neuroimaging to identify transdiagnostic mechanisms. While specific awards beyond his endowed professorship aren't detailed, his leadership as Brain Imaging Center Director and prolific high-impact publications demonstrate significant recognition. Current advising includes graduate researcher Sneha Aenugu on goal-persistence projects, with administrative support from Mary A. Martin (mmartin@caltech.edu). His active research program continues to pioneer computational approaches to understanding decision pathologies.

Xiaoxiao LiView profile

Assistant Professor

• Federated Learning
• Medical Imaging Analysis
• AI Fairness and Trustworthiness
• +7 more

Dr. Xiaoxiao Li is an Assistant Professor in the Electrical and Computer Engineering Department at the University of British Columbia (UBC), with joint appointments in Computer Science (Associate Member) and the School of Medicine at Yale University (Adjunct Assistant Professor). She is also a Canada CIFAR AI Chair and Canada Research Chair (Tier II) in Responsible AI. Her research focuses on enhancing trustworthiness, fairness, and efficiency in AI algorithms and foundation models, particularly in healthcare applications. Education: B.S. (Honors) in Zhejiang University (2015), Ph.D. in Biomedical Engineering from Yale University (2020), Postdoc at Princeton University (2020-2021). She leads the Trusted and Efficient AI (TEA) Lab at UBC, which develops algorithms for federated learning, medical imaging analysis, and interpretable AI systems. Research interests include federated learning, generative models, medical image analysis, AI fairness, and graph-based methods for neuroimaging. Recent projects include GMValuator (data valuation for generative models), FairMedFM (fairness benchmarking in medical AI), and FedTextGrad (textual gradient-based FL optimization). Grants: Canada Foundation for Innovation Grant (2023), UBC Green Lab Fund (2023), Vector Institute funding Teaching: Courses on machine learning, federated learning, and AI ethics at UBC Awards & Recognition: Best Paper Award at FL@FM WWW 2024, Editorial Board Member of Medical Image Analysis , multiple top-tier conference acceptances (NeurIPS, ICLR, CVPR, MICCAI). Lab & Teams: TEA Lab collaborates with industry and hospitals to translate AI research into clinical tools. Current projects address AI fairness in healthcare, federated learning for medical data, and multimodal medical analytics.

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +3 more

Professor Daniel Rueckert is a leading academic in Artificial Intelligence and Medical Imaging, holding dual positions at Imperial College London (as Professor of Visual Information Processing) and Technical University of Munich (Alexander von Humboldt Professor for AI in Medicine and Healthcare). He obtained his MSc from Technical University Berlin (1993) and PhD from Imperial College London (1997), followed by postdoctoral work at King’s College London. At Imperial, he led the Department of Computing (2016–2020) and founded the Biomedical Image Analysis group. His research focuses on AI-driven medical image analysis, including algorithms for image reconstruction, registration, and clinical decision support. His research interests span AI applications in healthcare, machine learning for medical imaging, and computational methods for clinical diagnostics. Notable contributions include over 500 publications and 60+ PhD graduates, with key works in federated learning, cardiac motion analysis, and biomarker development. Awards include the Leibniz Prize (2025), Royal Academy of Engineering Fellowship (2015), and multiple ERC grants. He leads the BioMedIA research group and is an editorial board member of Medical Image Analysis . Recent publications highlight advancements in AI-driven medical imaging, such as secure federated learning frameworks and deep learning models for disease prediction. His work bridges academic and industrial sectors through initiatives like IXICO, an Imperial spin-out. Current affiliations include roles at both Imperial and TUM, emphasizing interdisciplinary collaboration in healthcare technology. Advising and grants: Supervised over 60 PhD students and 40 post-docs. Secured grants including ERC Synergy (2013) and ERC Advanced (2020). Active in labs focused on biomedical image computing and AI in healthcare systems. Collaborative efforts include the BioMedIA group and TUM’s AI initiatives. Labs/teams: Leads the Biomedical Image Analysis group at Imperial and the TUM AI in Medicine team. Collaborates extensively on projects like cardiac imaging analysis and federated learning for healthcare.

David VocadloView profile

Professor

• Bioorganic Chemistry
• Chemical Biology
• Glycobiology
• +3 more

David Vocadlo is a Distinguished Professor of Chemistry and Molecular Biology & Biochemistry at Simon Fraser University (SFU), holding the Canada Research Chair in Chemical Biology. His research focuses on Chemical Glycobiology, investigating carbohydrate-processing enzymes and developing chemical tools to study glycan roles in health and disease. His lab explores O-GlcNAc signaling, neurodegenerative disorders (e.g., Alzheimer’s, Parkinson’s), and enzyme inhibitors for therapeutic applications. Education: PhD from University of British Columbia (UBC), followed by a CIHR postdoctoral fellowship at UC Berkeley. Key roles include E.W.R. Steacie Memorial Fellow and Royal Society Fellow. Research highlights include O-GlcNAcase inhibitors for neuroprotection, glycan structure-function relationships, and enzyme activity imaging tools. Collaborates globally with experts in glycobiology and employs cutting-edge techniques like chemical synthesis, mass spectrometry, and live-cell imaging. Awards: Distinguished Professor title, Canada Research Chair, Royal Society Fellowship. Active in training researchers through SFU’s graduate programs, emphasizing interdisciplinary approaches. Lab members work on topics ranging from enzyme mechanisms to disease modeling.