I am a PhD student at Carnegie Mellon's Heinz College of Information Systems & Public Policy. Previously, I studied Computational Economics at the George Washington University. I am also an alum of the North Carolina School of Science and Mathematics.
My research interests span algorithmic bias, private & fair machine learning, and public policy.
I am co-advised by Alessandro Acquisti and Alexandra Chouldechova. My undergraduate work was advised by Aylin Caliskan, Rahul Simha, and Ben Williams.
I'll be joining Oracle to work on fairness & bias as a research intern this summer.
I'm (virtually) attending FAccT 2021 and presenting our paper on unsupervised bias in computer vision - DM me on Twitter or in the conference platform if you want to chat!
This summer, I contributed to an awesome transparency tool for the Cook County Assessor's Office. Check out our presentation at the SolveForGood DataFest!
Our paper was accepted to the Participatory Approaches to Machine Learning workshop! We propose and test an alternative way to elicit & incorporate stakeholder preferences in ML systems.
I'm excited to join Carnegie Mellon's Heinz College of Information Systems & Public Policy to start my PhD this fall. Thank you to all the wonderful people who helped me along the way!
I'm very proud to have (virtually) defended my undergraduate thesis in front of a wonderful committee of GWU faculty.
I'll be hanging out at AIES-2020! Say hi if you're around - I'm excited to learn about new work in the field.