Recidivism — a formerly incarcerated individual commits a new offense — is one of the most challenging issues facing the modern criminal justice system. Notably, many recidivated individuals suffer from substance use disorder (SUD). Rather than receiving aid/treatment from their local communities, SUD sufferers are often criminalized and incarcerated for minor offenses. Incarceration-diversion programs have been advocated to break this vicious cycle but often lack analytics-based decision support. In this research, we work with local community corrections and study the complex process flow of individuals with SUD through the criminal justice and social services support systems. We build MDP models to capture key tradeoffs among jail overcrowding, reoffending, and technical violations. We develop data-driven analytics based on machine learning and reinforcement learning to aid the allocation of limited treatment resources to maximize the benefit to society while reducing racial disparity in incarceration and recidivism.
Bio: Pengyi Shi is an associate professor at the Daniels School of Business, Purdue University. She received her Ph.D. degree in Industrial Engineering from Georgia Institute of Technology before joining Purdue in 2014. Her research interests include data-driven modeling and decision-making in healthcare and service operations. She has collaborated with practitioners from different healthcare organizations, including major hospitals in the US, Singapore, and China. Most recently, she is collaborating with community correctional programs to develop data-based evaluation and human-in-the-loop machine learning algorithms. Her research has won the first place of MSOM Responsible Research in OM Award in 2021, the first place of INFORMS Pierskalla Best Paper Award in 2018, and the second place of POMS CHOM Best Paper Award in 2019 and 2020.