Serena L. Wang

I just received my PhD in Electrical Engineering and Computer Science in May 2024 at the University of California, Berkeley, advised by Michael I. Jordan. In fall 2024, I will be a postdoctoral fellow at Harvard’s Center for Research on Computation and Society (CRCS), hosted by Ariel Procaccia. After that, I am excited to be joining the University of British Columbia as an assistant professor of Computer Science.

I have also concurrently worked with Google Research at 20% time for the last six years, where I have had the privilege of working with amazing mentors including Ravi Kumar, Preston McAfee, and Maya Gupta. My PhD research was supported by the Apple Scholars in AI/ML PhD Fellowship and the NSF Graduate Research Fellowship.

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.

Email: serenalwang (at) berkeley.edu

News

• February 26-27, AAAI 2024: Co-organized the Workshop on AI for Education: Bridging Innovation and Responsibility at AAAI 2024. Many thanks to the amazing co-organizers, speakers, and participants!

Selected Publications

• Serena Wang, Stephen Bates, P. M. Aronow, Michael I. Jordan. On Counterfactual Metrics for Social Welfare: Incentives, Ranking, and Information Asymmetry. International Conference on Artificial Intelligence and Statistics (AISTATS), oral presentation, 2024. [paper] [arXiv]

• Lydia Liu^*, Serena Wang^*, Tolani Britton^†, Rediet Abebe^†. Reimagining the Machine Learning Life Cycle to Improve Educational Outcomes of Students. Proceedings of the National Academy of Sciences (PNAS), 2023. (^*equal contribution, ^†equal contribution)[paper][arXiv]

• Serena Wang^*, Wenshuo Guo^*, Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta, Michael I. Jordan. Robust Optimization for Fairness with Noisy Protected Groups. 34th Conference on Neural Information Processing Systems (NeurIPS), 2020. (^*equal contribution) [paper]