I am a Ph.D. candidate in Computer Science
at Cornell University, where I study how to make more reliable conclusions when using machine learning (ML) methods in practice. My work focuses on empirically motivated, theoretically grounded problems in Bayesian inference, model selection, and deep learning. I also do associated tech policy and ethics research, which aims to characterize the relationship between reliability and accountability in AI/ML. I am a co-organizer of GenLaw
, a community of researchers and practitioners interested in emerging issues at the interface of generative AI and the law (our first in-person workshop will be co-located with ICML '23
), and am the leader of the arbitrar.ai
project. During Summer 2022, I interned at Microsoft Research NYC
. In 2021, I was named a "Rising Star in EECS"
by MIT. I am a member of Cornell's initiative on Artificial Intelligence, Policy, and Practice
(AIPP), which has very generously supported my work through funding from the John T. and Catherine D. MacArthur Foundation.
I am actively looking for a part-time job. If you are looking for a CS tutor, please don't hesitate to reach out.
- A. Feder Cooper*, Wentao Guo*, Khiem Pham*, Tiancheng Yuan, Charlie F. Ruan, Yucheng Lu, and Christopher De Sa. "CD-GraB: Coordinating Distributed Example Orders for Provably Accelerated Training." Under submission, 2023.
- A. Feder Cooper, Solon Barocas, Christopher De Sa, and Siddhartha Sen. "Variance, Self-Consistency, and Arbitrariness in Fair Classification." Under submission, 2023. [arxiv]
- A. Feder Cooper, Jonathan Frankle, and Christopher De Sa. "Non-Determinism and the Lawlessness of Machine Learning Code." CSLAW 2022. [arxiv | proceedings]
- A. Feder Cooper*, Emanuel Moss*, Benjamin Laufer, and Helen Nissenbaum (*Equal contribution). "Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning." FAccT 2022. [arxiv | proceedings]
- A. Feder Cooper, Karen Levy, and Christopher De Sa. "Accuracy-Efficiency Trade-Offs and Accountability in Distributed ML Systems." EAAMO 2021, Contributed Talk. [arxiv | proceedings]
- A. Feder Cooper, Yucheng Lu, Jessica Zosa Forde, and Christopher De Sa. "Hyperparameter Optimization Is Deceiving Us, and How to Stop It." NeurIPS 2021. [arxiv | proceedings]
- A. Feder Cooper and Ellen Abrams. "Emergent Unfairness in Algorithmic Fairness-Accuracy Trade-Off Research." AIES 2021, Contributed Talk. [arxiv | proceedings]
- Ruqi Zhang*, A. Feder Cooper*, and Christopher De Sa (*Equal contribution). "Asymptotically Optimal Exact Minibatch Metropolis-Hastings." NeurIPS 2020, Spotlight. [arxiv | proceedings]
- Ruqi Zhang, A. Feder Cooper, and Christopher De Sa. "AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC." AISTATS 2020. [arxiv | proceedings]