Assistant Professor, conducts research at the interface of computational science, artificial intelligence and mental health. Her laboratory develops novel computational approaches with the goal of advancing our understanding of risk trajectories, outcomes and mechanisms in mental health and substance use disorders, enabling more impactful and efficient prevention and intervention. She is particularly known for innovation in sequence data such as digital phenotyping and electronic health records and her neuroscientific work developing innovative mathematical models of brain function and its relationship with psychiatric illness. Dr. de Lacy uses artificial intelligence and machine learning approaches well-suited to problems such as individual prediction, precision psychiatry and the identification of psychiatric biotypes and biomarkers. Dr. de Lacy earned her undergraduate degree at the University of Oxford, her medical degree at UCSF and completed her Adult Psychiatry Residency (Neuroscience Track) and her Fellowship in Child and Adolescent Psychiatry at the University of Washington.
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