A. Feder Cooper

✨I am actively looking for a Summer 2023 internship! Please do not hesitate to reach out✨

About Me

I am a Ph.D. candidate in Computer Science at Cornell University, where I am very fortunate to be a member of the Relax ML Lab and advised by Chris De Sa. I am broadly interested in aligning the use of AI/ML with broader public values. My CS work has thus-far focused on empirically motivated, theoretically grounded problems in Bayesian inference, model selection, and (more recently) deep learning. My tech policy and ethics research aims to characterize the relationship between uncertainty, reliability, and accountability, for which I also engage methods from the law and relevant social sciences.

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!). During Summer 2022, I had the privilege of interning with Solon Barocas and Siddhartha Sen at Microsoft Research NYC. This has led to two collaborations, one focused on CS disciplinary contributions and a second intended for publication in a tech law journal. In 2021, I was named a "Rising Star in EECS" by MIT. Prior to attending Cornell, I worked for several years in industry at companies both big and small, and did my undergrad in Computer Science and Archaeology. I am a member of Cornell's initiative on Artificial Intelligence, Policy, and Practice (AIPP), and am engaged in a variety of outreach and service programs in CS mentorship and education. I am very grateful to have had my work supported by AIPP and the John T. and Catherine D. MacArthur Foundation.

In my free time, I am fully committed to perfecting the art of rolling fresh pasta 🍝. I am usually doing that, but if not you can find me reading, learning Italian, or practicing mischief.

Papers/Projects

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, Jianan Canal Li, Yimeng Zeng, Ruqi Zhang, and Christopher De Sa. "Bringing the Heat: Scaling Exact SG-MCMC with Higher Temperatures." Forthcoming preprint, 2023.
A. Feder Cooper, Madiha Zahrah Choksi, Solon Barocas, Christopher De Sa, James Grimmelmann, and Siddhartha Sen. "On Machine Learning Uncertainty, Arbitrariness, and Due Process." Data (Re)Makes the World Conference, Information Society Project at Yale Law School; Privacy Law Scholars Conference, Forthcoming, 2023.
Katherine Lee, A. Feder Cooper, James Grimmelmann, and David Mimno. "Talkin' 'Bout AI Generation: Copyright and Privacy in Generative AI Models." Privacy Law Scholars Conference, Forthcoming, 2023.
Kweku Kwegyir-Aggrey, Jessica Dai, A. Feder Cooper, John P. Dickerson, and Keegan Hines. "A Tale of Two Measures: Optimal Transport for Fair Classification at Any Decision Threshold." AAAI 2023, AI4SG Workshop. [workshop]

2022

A. Feder Cooper, Solon Barocas, Karen Levy, and Gili Vidan. "'We have met the enemy and it is us': Debating the ethics of computing in the pages of CACM." SIGCIS 2022
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 and Karen Levy. "Fast or Accurate? Governing Conflicting Goals in Highly Autonomous Vehicles." Colorado Technology Law Journal 2022, Vol. 20. [arxiv]
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 and Gili Vidan. "Making the Unaccountable Internet: The Changing Meaning of Accounting in the Early ARPANET." FAccT 2022. [arxiv | proceedings]
Benjamin Laufer, Sameer Jain*, A. Feder Cooper*, Jon Kleinberg, and Hoda Heidari (*Equal contribution). "Four Years of FAccT: A Reflexive, Mixed-Methods Analysis of Research Contributions, Shortcomings, and Future Prospects." FAccT 2022. [arxiv | proceedings]

2021

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 , Maria Antoniak, Christopher De Sa, Marilyn Migiel, and David Mimno. "'Tecnologica cosa': Modeling Storyteller Personalities in Boccaccio's Decameron." EMNLP 2021, SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature. [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]
Jessica Zosa Forde*, A. Feder Cooper*, Kweku Kwegyir-Aggrey, Christopher De Sa, and Michael Littman (*Equal contribution). "Model Selection's Disparate Impact in Real-World Deep Learning Applications." ICLR 2021, Workshop on the Science and Engineering of Deep Learning (SEDL), Contributed Talk. [arxiv]
A. Feder Cooper and Ellen Abrams. "Emergent Unfairness in Algorithmic Fairness-Accuracy Trade-Off Research." AIES 2021, Contributed Talk. [arxiv | proceedings]

2020

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]

2019

A. Feder Cooper. "Imperfection is the Norm: A Computer Systems Perspective on IoT and Enforcement." (Im)Perfect Enforcement Conference, Information Society Project at Yale Law School, 2019, Plenary Panel.

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