PROJECT SUMMARY/ABSTRACT Aggressive behavior is a transdiagnostic indicator of both youth and adult psychiatric disorders and a significant public health concern due to the direct harms to victims and its broader economic impact. Nonetheless, prediction of aggressive behavior is challenging due to significant variability in how, why, and when people act aggressively. This heterogeneity impedes efforts to establish etiological factors, identify biological substrates, and develop uniformly effective treatments. Though theories of aggression emphasize that it is a context-dependent, dynamic interpersonal behavior, research rarely attempts to study aggression in the contexts where it normally occurs and is most consequential (i.e., daily life). The current project seeks to improve on past research by studying the transdiagnostic mechanisms of aggression using novel analytic and measurement methodology necessary for pursuing a personalized medicine approach in aggressive behavior research and prevention. This project will use real-time data capture in conjunction with state-of-the-art analytic methods to deconstruct the heterogeneous behavioral phenotypes that relate to aggression. To achieve this, we will use relevant passively-sensed and self-reported data via smartphones from a sample of young adults (age=18-30; N=150) diagnosed with mental and behavioral disorders and at elevated risk for aggression. Data will be collected over the course of a 3-week ambulatory assessment protocol. We will apply machine learning methods capable of uncovering and modeling the complex dynamic processes observed in aggression at the level of each individual (i.e., personalized models) to prospectively predict aggressive urges and behavior. The results will pave the way for scalable just-in-time adaptive interventions tailored to an individual’s specific antecedents of aggression. The proposed study will contribute to NIMH Strategic Priorities 3.2 by 1) focusing on personalized models that can accommodate the complex topography of aggression and its antecedents and 2) applying innovative computational approaches (i.e., machine learning) to multiple streams of data (passively sensed, self-report) to identify potential just-in-time intervention targets for aggressive individuals. The comprehensive research and training plan detailed in this proposal will allow this candidate to address the primary research questions of the proposal and develop the expertise necessary to be an independent scientist. Specifically, this candidate will receive training in 1) personalized models of psychopathology and aggression; 2) methods for carrying out EMA-based studies and modeling intensive longitudinal data; and 3) collecting, processing, and predictive modeling with passive sensor data. This candidate has assembled a team of expert mentors (Wright, Jacobson) and consultants who possess the expertise to supervise the project and provide the training necessary to support the candidate...