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.
