Starting July 2020, I will join UT Austin as an Assistant Professor in the Information, Risk and Operation Management Department at McCombs School of Business.
Currently, I am a PhD candidate in a dual program in Machine Learning and Public Policy at Carnegie Mellon University’s Machine Learning Department and Heinz College. I am co-advised by Prof. Artur Dubrawski and Prof. Alexandra Chouldechova, and I am part of the Auton Lab.
My research is focused on the risks and opportunities of algorithmic decision support in the context of sustainable societies. 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.
I hold a M.Sc. in Machine Learning from Carnegie Mellon University (2017) and a B.Sc. in Mathematics from Universidad Nacional de Colombia (2013). Prior to graduate school, I worked as a data science researcher and as a journalist. Data-driven investigative journalism continues to be one of my passions. I am the recipient of a Microsoft Research Dissertation Grant 2018, and a co-founder of ML4D.
03/03 (Austin) Conference on the Ethics of AI: Risks of compounding injustices with machine learning.
04/17 (San Francisco) Data Ethics Seminar, University of San Francisco: Understanding and mitigating semantic representation bias.
04/27 (Honolulu) CHI Conference: A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores.