The goal of the research is to design, develop and test a inconspicuous, awareness-enhancement and monitoring device (AEMD) which will assist the treatment of trichotillomania (TTM), a disorder involving recurrent pulling of one’s hair resulting in noticeable hair loss. TTM is associated with significant impairments in social functioning and often has a profound negative impact on self-esteem and well being. Best practice treatment for TTM involves a form of behavioral therapy known as habit reversal therapy (HRT). HRT requires persons with trichotillomania to be aware of their hair pulling behaviors, yet the majority of persons with TTM pull most of their hair outside of their awareness . HRT also requires TTM sufferers to record the frequency and duration of their hair pulling behaviors yet it is obviously impossible for a person to monitor behaviors that they are unaware of. Our Phase I efforts have produced a prototype device (AEMD) that solves these two problems. The prototype AEMD signals the TTM sufferer if their hand approaches their hair, thereby bringing pulling-related behavior into awareness. The prototype AEMD also logs the time, date, duration, and user classification of hair pulling related events and can later transfer the logged data to a personal computer for analysis and data presentation. We continue to refine this device and seek to integrate it with smart-phones to better understand activities and locations associated with hair pulling or other body-focused repetitive behaviors (e.g., skin picking). In the future, we seek to pool data from users to get a better sense of common situations and other factors associated with elevated pulling rates. We intend to develop other electronic tools to detect, monitor and intervene with other mental disorders in the future.
My research group is engaged in fundamental research in the following areas: Statistical learning theory: We are developing theory and algorithms for predictions problems (e.g., learning to rank and multilabel learning) with complex label spaces and where the available human supervision is often weak. Sequential prediction in a game theoretic framework: We are trying to understand the power and limitations of sequential predictions algorithms when no probabilistic assumptions are placed on the data generating mechanism. High dimensional and network data analysis: We are developing scalable algorithms with provable performance guarantees for learning from high dimensional and network data. Optimization algorithms: We are creating incremental, distributed and parallel algorithms for machine learning problems arising in today’s data rich world. Reinforcement learning: We are synthesizing concepts and techniques from artificial intelligence, control theory and operations research for pushing the frontier in sequential decision making with a focus on delivering personalized health interventions via mobile devices. My research group is pursuing and continues to actively search for challenging machine learning problems that arise across disciplines including behavioral sciences, computational biology, computational chemistry, learning sciences, and network science.
I develop clinical trial designs and data analytic methods for informing sequential decision making in health. In particular I focus on methods for constructing real time individualized sequences of treatments (a.k.a., treatment policies or Just-in-Time Adaptive Interventions) delivered by mobile devices. This is an area of Precision Medicine. I develop new clinical trial designs (designs in which each person is randomized 100s or 1000s of times) and generalize reinforcement learning algorithms to analyze the data and construct treatment policies. I also generalize data analytic method from causal inference for use in analyzing mobile health data.