Hi there! I’m a third-year Ph.D. student in Computer Science at UC Berkeley working with professors Niloufar Salehi and Rediet Abebe. My current research applies methods from human-computer interaction (HCI) and machine learning (ML) to evaluate the reliability and validity of statistically-driven systems used to produce evidence in the U.S. criminal legal system. I’m interested in exploring how developers, users, impacted communities, and policymakers can each play a role in ensuring responsible and accountable development, deployment, and use of algorithmic decision-making systems.

Prior to joining Berkeley, I graduated from Cornell University with a B.S. in Computer Science, where I had the privilege of working with and learning from Shuang Chen, José F. Martínez, Christina Delimitrou, and many others.

My research is supported by an NSF Graduate Research Fellowship.

Publications

Berkeley

Angela Jin, Niloufar Salehi. “(Beyond) Reasonable Doubt: Challenges that Public Defenders Face in Scrutinizing AI in Court.” To appear in ACM Conference on Human Factors in Computing Systems (CHI) 2024. [paper]

Rediet Abebe, Moritz Hardt, Angela Jin, John Miller, Ludwig Schmidt, Rebecca Wexler. “Adversarial scrutiny of evidentiary statistical software.” In Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2022. [paper]

Cornell

Shuang Chen, Angela Jin, Christina Delimitrou, José F. Martínez. “ReTail: Opting for Learning Simplicity to Enable QoS-Aware Power Management in the Cloud.” In Intl. Symp. on High Performance Computer Architecture (HPCA), 2022. [paper]

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