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 and a Good Systems researcher. 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. 

I am currently a member of FAccT‘s Executive Committee. Previously, I served as co-Chair of Diversity & Inclusion for FAccT 2021-2022, and local arrangements co-Chair for WITS 2021. In 2017 I co-founded ML4D, which I co-led for five years.

Advising: I am lucky to work with many fantastic collaborators and students. I serve as a Postdoctoral Advisor for Jakob Schoeffer, and as a PhD co-advisor for Terry Neumann (IROM), Yunyi Li (IROM), and Soumyajit Gupta (CS).

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.

News

04/24 – Our CHI ‘24 paper “Explanations, fairness, and appropriate reliance in human-AI decision-making” won an Honorable Mention Award (top 5% of submissions).

03/24 – Honored to receive a 2024 McCombs Research Excellence Grant.

03/24 – Our work A Critical Survey on Fairness Benefits of XAI led by PhD student Luca Deck (joint w/ J. Schoeffer, N. Kuehl), accepted to ACM FAccT 2024.

01/24 – Our work Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Making led by postdoc Jakob Schoeffer (joint w/ N. Kuehl), accepted to ACM CHI’24.