I am a researcher, data scientist, and project manager. Currently, I am an incoming 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.
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.
As algorithms become increasingly responsible for high-stakes decision-making, it is important for stakeholders and affected users to have influence over algorithmic policy. To lower the barrier to collective participation in ML systems, this research proposes an alternative method for incorporating stakeholder preferences in ML systems.
Innovation is important to growth and welfare, and nations sponsor research and craft legislation to encourage it. But how can we measure the impact of these investments? This research measures the spread of innovation with patent citation network analysis and explores the effect of recent legislation on knowledge flows between inventions.