A. Feder Cooper


Gates Hall
Department of Computer Science
Cornell University
afc78 [AT] cornell [DOT] edu

arxiv scholar
A. Feder Cooper

I am a scalable machine-learning (ML) researcher, working on reliable measurement and evaluation of ML. I am a co-founder of the GenLaw Center, a CS Ph.D. candidate at Cornell University, an Affiliate at the Berkman Klein Center for Internet & Society at Harvard University, and a student researcher at Google Research. I am an incoming postdoctoral researcher at Microsoft Research, and will be affiliated with Stanford HAI and RegLab, working with Percy Liang and Dan Ho on research and policy questions regarding the governance of foundation models.

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, model selection, distributed training, hyperparameter optimization, and security of generative-AI systems. To make sure that our evaluation metrics can meaningfully measure our goals for ML, I also lead collaborations in tech policy and law, and spend a lot of time working to effectively communicate the capabilities and limits of AI/ML to the broader public. My work has received various spotlight, oral, and best paper accolades at top AI/ML and interdisciplinary venues.

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


Selected work

*Equal contribution; full list