Dan is a psychiatrist and data scientist interested in how we can make use of data-driven technologies to better understand mental illness. His work looks at how we can use methods from statistical- and machine-learning in prosaic ways to deliver actionable insights to patients and clinicians. Topics he has a particular interest in include a) defining what transdiagnostic phenotyping means and how it can be achieved using clinical data that may be at best, first-approximations (or proxies) for traditional diagnostics and syndromes b) unobtrusive and low-burden measurement and tracking of clinical and health states using technology c) how to capture aspects of clinical decision making so that data-driven technology (like artificial intelligence) can be aligned with clinical practice.
