Stanford University
Showing 51-60 of 245 Results
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Rachel Schuck
Postdoctoral Scholar, Psychiatry
Current Research and Scholarly InterestsMy research interests center on improving support services for autistic individuals—particularly by incorporating feedback from the autistic/autism community—and increasing access to high quality supports. I am also interested in assessing attitudes toward neurodiversity and promoting understanding of neurodiversity amongst the general population in the hopes of improving quality of life for neurodivergent people and their families.
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Narayan Schutz
Postdoctoral Scholar, Psychiatry
Current Research and Scholarly InterestsI work on using digital health technologies to detect and monitor aging relevant health indicators and outcomes using cutting-edge machine and deep learning approaches, with the goal to make our healthcare system more personalised and proactive.
Current research topics include remote gait and mobility assessments, learning health representations from large-scale smartphone data, and using novel ambient intelligence approaches to foster independent living in older adults. -
Mojtaba Sedigh Fazli
Visiting Instructor/Lecturer, Cardiovascular Institute
Postdoctoral Scholar, Cardiovascular InstituteBioDr. Mojtaba Fazli is a leading artificial intelligence scientist and Lecturer at the Stanford Cardiovascular Institute and Stanford School of Medicine. His research connects machine learning, biomedical imaging, and cardiovascular physiology to address a central challenge in clinical AI: translating complex biomedical data into interpretable insights that can support better clinical decisions.
Working under the mentorship of François Haddad and Suzanne Tamang, Dr. Fazli develops AI approaches that integrate electrocardiography, echocardiography, invasive hemodynamic measurements, and clinical data. His current research spans pulmonary hypertension, right-heart function, and the estimation of cardiovascular pressures and function from noninvasive measurements. A defining focus of his work is explainability—understanding not only what a model predicts, but also how its predictions relate to clinically meaningful physiological patterns. Through this work, he investigates how AI can complement established cardiovascular assessment and support more informative, accessible approaches to disease evaluation.
Before his current appointment, Dr. Fazli held successive postdoctoral positions in Stanford’s Division of Immunology and Rheumatology and at the Cardiovascular Institute. Under the supervision of Suzanne Tamang and Rob Fairchild, his rheumatology research focused on AI-enabled ultrasound analysis, rheumatoid arthritis assessment, and the integration of multimodal clinical data. This experience helped shape his broader research approach: connecting imaging, quantitative measurements, and clinical context to address questions that matter in patient care.
His background spans academic medicine and pharmaceutical research, including postdoctoral training and senior research experience at the Harvard Ophthalmology Artificial Intelligence Lab and Harvard AI and Robotics Lab. He also served as a Senior Open Innovation Scholar and Gates Fundation Fellow at the Novartis Institutes for BioMedical Research, applying computational methods to biomedical research and drug discovery. Across these settings, his work has encompassed computer vision, 2D and 3D biomedical image analysis, computational disease modeling, and AI-driven analysis of complex biological data.
Dr. Fazli’s broader research interests include multimodal learning, computer vision, agentic AI, generative AI, and large language models for medical data analysis, integration of clinical knowledge, and clinical decision support. Across these areas, he emphasizes rigorous evaluation, transparent modeling, and close collaboration between computational scientists and clinicians.
He holds a PhD in Computer Science, with a minor in Mathematics, from the University of Georgia, a Doctorate in Business Administration, and master’s degrees in Economics and Management and in Artificial Intelligence and Robotics. This interdisciplinary foundation informs his approach to developing AI that is technically rigorous, clinically grounded, and responsive to the practical challenges of healthcare.
