A. Feder Cooper, Ph.D.

Postdoctoral Researcher, Microsoft Research
Affiliate Researcher, Stanford HAI
Future Assistant Professor, Yale CS
afedercooper [AT] gmail [DOT] com || acoo [AT] microsoft [DOT] com

arxiv scholar
A. Feder Cooper

I research topics on reliable measurement and evaluation of machine learning systems. I'm a co-founder of the GenLaw Center, an Affiliate at the Berkman Klein Center for Internet & Society at Harvard University, a postdoctoral researcher at Microsoft Research, and a postdoctoral affiliate at Stanford HAI, RegLab, and CRFM working with Percy Liang and Dan Ho on research and policy questions regarding the governance of foundation models. In 2026, I'll be appointed as an Assistant Professor of Computer Science at Yale University. I'll also be affiliated with the Information Society Project at Yale Law School, the Center for Algorithms, Data, and Market Design, and the Institute for Foundations of Data Science.

My research develops quality metrics for ML capabilities, and makes sure that we can effectively measure these metrics at scale and in practice. My contributions span uncertainty estimation, privacy and security of generative-AI systems, distributed training, hyperparameter optimization, and model selection. I also do work in tech policy and law, and spend a lot of time finding ways to effectively communicate the capabilities and limits of AI/ML to interdisciplinary audiences and the public.

In the past I interned at Microsoft Research and at Google Research, and was named a "Rising Star in EECS" by MIT. My doctoral work was generously supported by the John T. and Catherine D. MacArthur Foundation through AIPP.

Prior to my research career, I worked for several years as a software engineer at companies both (really) big and (really) small. I specialized in designing, building, and monitoring large-scale backend data-processing systems.

(I am not recruiting students for Fall 2025 -- my appointment at Yale starts in 2026.)

Selected work

*Equal contribution; full list