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. 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. We believe that solving this problem will require complex integration of different types of sensors into an adaptive learning system together with patient, caregiver, and community feedback.
Many of the tools we develop can be used across domains. If you have a need we can address, we would like to hear from you. If you have solved problems associated with any of the projects below, we would love to hear from you too — for us, a solution typically implies available data, code, and/or replicated results.
