In the US, unhealthy behaviors -- such as sedentary lifestyle, overeating, substance use, and tobacco use -- account for approximately 40% of the risk of premature deaths. While successful changes to these unhealthy behaviors can mitigate the risk of harm, behavior change is often difficult because of high self-management burden. Fortunately, mobile phones can reduce the burden of self-management because they can deliver the right intervention at the right time by using data-capture and computational capabilities of the phone. Creating such just-in-time interventions, however, is a complex multi-disciplinary challenge. Mashfiqui Rabbi, postdoctoral fellow at Harvard University, works in two areas of just-in-time interventions. First, he designs new therapeutic interventions that use fine-grained mobile data and AI. These therapeutic interventions use novel computational algorithms and mobile data to dynamically personalize interventions to the individual in ways that were previously unachievable (e.g., relating treatments to one's daily routine). Second, he works on engagement interventions, where the goal is to keep people engaged in frequent self-report both to collect data to advance science in behavior change as well as to provide individual states that cannot yet be sensed. Engagement interventions are critical in mobile health because most people stop using health apps after only a few days. In his talk, "Computational Interventions for Behavior Change," Rabbi will present one novel therapeutic intervention and one engagement intervention. MyBehavior is a novel therapeutic intervention that provides personalized and low-burden suggestions to improve physical activity and dietary intake. SARA is an engagement intervention that optimizes timely incentives to increase the self-reporting of substance use data from a youth population. After discussing MyBehavior and SARA, Rabbi will outline his future plans to make more effective therapeutic/engagement interventions and his long-term vision of creating behavior change interventions for mental health. Mashfiqui Rabbi is a postdoctoral fellow at Harvard University, where he works with Susan Murphy, professor of statistics and computer science. Prior to his postdoc, he earned a PhD in information science from Cornell University. For his thesis, he created the MyBehavior app, which is the first mobile recommender system to automatically generate personalized health feedback from mobile phone data. Rabbi's work has been published in Ubicomp, Journal of Medical and Internet Research, and IEEE Pervasive Computing, and he has been featured in MIT Technology Review, New Scientist, The Economist, Mashable, and The New York Times. He has received several pilot grants from Cornell University, the University of Michigan, and Pennsylvania State University.