I am a final-year PhD student in Computer Science at University of California, Berkeley, advised by Michael I. Jordan. I am generously supported by the NSF Graduate Research Fellowship and the Apple Scholars in AI/ML PhD fellowship. I have also concurrently worked at Google Research at 20% time for the last six years, where I am part of the Discrete Algorithms Group with Ravi Kumar and have also worked with Maya Gupta on the Glassbox Machine Learning Research team.
My research focuses on understanding and improving the long term societal impacts of machine learning by rethinking ML algorithms and their surrounding incentives and practices. I’m particularly interested in gaps between metrics and goals, and how those gaps may be bridged through algorithmic improvements, analysis of multi-agent incentives and interdependence, and better understanding of intervention context. I employ tools from machine learning, robust optimization, causal inference, and most recently, economics.
serenalwang (at) berkeley.edu
December 14, CIRM: Excited to be speaking at the Centre International de Rencontres Mathematiques (CIRM) as part of their conference, From Matchings to Markets. A tale of Mathematics, Economics and Computer Science!
September 21, Yale: Guest lectured for the Causal Inference and Research Design Seminar at Yale taught by P Aronow.
August 3, MIT: Grateful to have had the opportunity to present our work on Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetry for Ashia Wilson’s group at MIT!
August 1, UAI 2023: Presented our work on Robust Distillation for Worst-class Performance: On the Interplay Between Teacher and Student Objectives at UAI 2023.