A fundamental problem in psychiatry is that there are no biological markers for diagnosing mental illness or for indicating how best to treat it. Treatment decisions are based entirely on symptoms, and doctors and their patients will typically try one treatment, then if it does not work, try another, and perhaps another. Our group hopes to change this picture, and our research suggests that individual brain scans and speaking patterns can hold valuable information for guiding psychiatrists and patients. Current areas include depression, suicide, anxiety disorders, autism, Parkinson disease, and brain tumors.
To support this broader goal, our group develops novel analytic platforms that use such information to create robust, predictive models around human health. Our research interests span computer science and neuroscience, specifically in the areas of applied machine learning, signal processing, and translational medicine. Our current research portfolio comprises projects on spoken communication, brain imaging, and informatics to address gaps in scientific knowledge in three areas: The neural basis and translational applications of speaking, precision psychiatry and medicine, and preserving information for reproducible research.
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