Stanford University
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Eric Lin
Clinical Assistant Professor (Affiliated), Psych/General Psychiatry and Psychology (Adult)
Staff, Psychiatry and Behavioral SciencesBioEric Lin, MD, is a Clinical Assistant Professor (Affiliated) of Psychiatry and Behavioral Sciences at Stanford University School of Medicine and an addiction psychiatrist at VA Palo Alto. His academic work focuses on artificial intelligence, large language models, machine learning, and psychiatry, with particular interest in the clinical evaluation, safety, and governance of AI systems used in mental health care and related psychological contexts.
Dr. Lin studies how AI systems should be assessed when they interact with patients, clinicians, or psychologically vulnerable users. His work addresses the limitations of benchmark-driven evaluation and the need for clinically meaningful approaches to AI evaluation that incorporate psychiatric expertise, real-world clinical complexity, and post-deployment risk. His broader interests include psychopathology, personality assessment, psychoanalytic and psychodynamic models of mind, and the translation of complex clinical judgment into rigorous evaluation frameworks for emerging technologies.
Dr. Lin completed psychiatry residency at Yale University, where he trained in the Neuroscience Research Training Program, and later completed a medical informatics fellowship through VA Boston, with research at Harvard Medical School/McLean Hospital on computational and digital approaches to psychiatric phenotyping. He is board certified in psychiatry and clinical informatics. His clinical and teaching work in addiction psychiatry grounds his interest in psychiatric complexity, risk assessment, care navigation, and pragmatic implementation of AI tools in health care. He is interested in collaborations across psychiatry, computer science, human-centered AI, health policy, digital mental health, and responsible technology development. -
Feng Vankee Lin
Associate Professor of Psychiatry and Behavioral Sciences (Public Mental Health & Population Sciences)
Current Research and Scholarly InterestsMy career has been devoted to understanding the neural mechanisms involved in brain aging and brain plasticity, with a special focus on early detection and prevention of Alzheimer’s disease (AD). My research approach integrates principles and findings from cognitive theory, clinical neuroscience, and computational neuroscience.
