A. Feder Cooper is a scalable machine-learning researcher, working on reliable measurement and evaluation of ML and ML systems. Cooper ’s research develops nuanced quality metrics for ML behaviors, and making sure that we can effectively measure these metrics at scale and in practice. Cooper’s contributions span distributed training, hyperparameter optimization, uncertainty estimation, model selection, and generative AI. To make sure that our evaluation metrics can meaningfully measure our goals for ML, Cooper also leads research in tech policy and law, and spends a lot of time working to effectively communicate the capabilities and limits of AI/ML to the broader public.

Cooper is a CS Ph.D. candidate at Cornell University, an Affiliate at the Berkman Klein Center for Internet & Society at Harvard University, and co-founder of The Center for Generative AI, Law, and Policy Research (The GenLaw Center). Cooper has received many spotlight and oral awards at top conferences, and was named a "Rising Star in EECS" by MIT.