I am an Assistant Professor at the Information, Risk and Operation Management Department at McCombs School of Business at the University of Texas at Austin. I am also a core faculty member in the interdepartmental Machine Learning Laboratory. I hold a joint PhD in Machine Learning and Public Policy from Carnegie Mellon University’s Machine Learning Department and Heinz College.
My research is focused on algorithmic fairness and human-AI complementarity. As part of my work, I characterize how societal biases encoded in historical data may be reproduced and amplified by ML models, and develop algorithms to mitigate these risks. Moreover, effective human-AI collaboration is often complicated by other factors, such as the fact that experts often care about constructs that are not well captured in the available labels. In my research, I aim to understand the limits and risks of using ML in these contexts, and to develop human-centered ML that can improve expert decision-making.
Prospective students: If you are interested in working with me, please apply to the Information Systems track of the IROM PhD program and mention my name in your application. If you are a current student at UT Austin interested in collaborating, please send me an email.
1/21 – Our paper “The effect of differential victim crime reporting on predictive policing systems” accepted to FAccT’21 (joint w/ N.J. Akpinar & A. Chouldechova).
11/20 – I will serve as Diversity & Inclusion Chair for ACM FAccT 2021-2022.
10/20 – I was awarded a Google AI Award for Inclusion Research.