I am a PhD student in Machine Learning and Public Policy at Carnegie Mellon University. My research investigates how to make decision-making systems more reliable and more equitable. A central focus of my research is identifying when algorithms, data used for policy-making, and human decisions disproportionately impact marginalized groups. I am advised by Alexandra Chouldechova and Edward H. Kennedy. During my PhD, I have interned at Meta AI Applied Research, the Stanford Law School Regulation, Evaluation, and Governance Lab, and IBM Research AI as a Science for Social Good Fellow. I completed a BSE from Princeton University, where I was advised by Robert Schapire. Prior to my PhD, I worked at Microsoft, Teneo, and HiviSasa.