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, where I was co-advised by Prof. Artur Dubrawski and Prof. Alexandra Chouldechova, and was a part of the Auton Lab.
My research is focused on human-AI complementarity, and the risks and opportunities of algorithmic decision support. 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, the sensitive task of creating decision support tools is complicated by several 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 on algorithmic fairness and accountability, human-AI complementarity, and its applications in high-stakes settings, 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.