About

I am an Assistant Professor at the College of Information and the Criminology and Criminal Justice department at the University of Maryland.

My research advances evidence-based criminal justice policy through two interconnected contributions: rigorously evaluating machine learning systems for high-stakes contexts and building computational infrastructure that enables trustworthy policy evaluation.

My work on prediction and prevention in criminal justice—particularly in domestic violence, gun violence, and pretrial reform—reveals a more nuanced reality than either ML advocates or critics claim: machine learning has limited impact for direct intervention, and can sometimes be counterproductive, but is genuinely valuable for measurement and enabling better science.

I also build methods, tools, and systems that make rigorous policy evaluation feasible at scale, including record linkage methods for administrative data, causal inference approaches for policy analysis, and AI-powered systems for measuring policy changes across jurisdictions.

I was previously a Research Director at the New York City office of Crime Lab, a University of Chicago research institute that partners with civic and community leaders to design, test, and scale evidence-based programs to reduce crime and violence. I finished my Ph.D. in Computer Science at New York University’s Tandon School of Engineering in January 2017.