I am an AI researcher whose work lies at the intersection of machine learning and human-decision making. The core of my research involves the development and evaluation of AI methods that augment and leverage human expertise to improve human-AI team decisions. As a part of my work, I designed interpretable rule-based methods to advise high-stakes decision makers and I am currently developing concept-bottleneck based methods to improve liver-transplantation decisions. I also study how AI systems can leverage network structure and human expertise to produce fairer AI-assisted fact-checking outcomes. I am a part of the Computational Data Science group at the McCombs School of Business, University of Texas @ Austin, advised by Professor Maytal Saar-Tsechansky. Prior to joining UT, I received an M.S. in Statistics from the University of North Carolina @ Chapel Hill.
04/07/23 - Excited to announce that my joint work with Terrence Neumann, "Does AI-Assisted Fact-Checking Disproportionately Benefit Majority Groups Online?", has been selected for publication at FAccT '23!
10/17/22 - Served as session chair at the 2022 INFORMS annual meeting. Big thanks to Stephanie Kelley, Max Schemmer, Patrick Hemmer, Anna Kawakami, and Luke Guerdan for sharing your amazing work on human-AI teams!
10/13/22 - Received UT Graduate School Professional Development Award.
9/6/22 - Received Workshop on Data Science 2022 Student Scholarship for my work: 'Leveraging Algorithm Discretion in AI-Advised Teams.'