Using data science and experimental methods to understand criminal justice reform, political behavior, public opinion, and the effects of AI on law and human decision-making.
I'm a Post-Doctoral Researcher at UC Berkeley's Criminal Law & Justice Center. My research lies at the intersection of democratic accountability, public perception of justice, and the evolving landscape of criminal legal politics.
My work examines the tangible impact of local elections and voter preferences on law enforcement practices and prosecutorial conduct, illuminating how these variables drive changes in incarceration outcomes. I'm also deeply interested in how AI and algorithmic tools are reshaping legal decision-making and public trust in governmental institutions.
I combine rigorous experimental methods with large-scale data analysis to produce actionable insights that inform both academic discourse and policy decisions. My research aims to foster a more equitable and responsive justice system.
Bridging academic rigor with practical impact through quantitative methods and experimental design
Designing and executing survey experiments, conjoint analyses, and field studies to test causal mechanisms in political behavior and legal decision-making.
Studying public perceptions of algorithmic decision-making in government and how AI tools shape legal outcomes and institutional legitimacy.
Analyzing the politics of crime control, prosecutorial behavior, and the determinants of public support for justice reform.
Advanced statistical modeling, causal inference techniques, and computational methods for social science research.
Peer-reviewed research advancing our understanding of criminal justice, political behavior, and AI in law
Voters support reducing the intensity of the criminal legal system but not its extent—they favor reducing harsh outcomes but not limiting the scope of prosecuted behavior. This finding has fundamental implications for the politics of criminal justice reform.
Racial and political group cues significantly shape public attitudes toward criminal justice reform. Both white respondents and people of color follow cues from Black voters, with racial attitudes playing an important moderating role.
Public perceptions of algorithmic legitimacy in government derive from procedural factors: notice about algorithm use, human involvement, decision explanation, and hearing opportunities. The criminal justice context is viewed as less fair than other domains.
How defendants explain their crimes affects public perceptions of dangerousness and even racial classification. Situational attributions can backfire when not accepted as valid, increasing perceptions of risk.
I'm always open to discussing research collaborations, speaking opportunities, or interesting positions
Criminal Law & Justice Center
UC Berkeley
California