Hi! I’m a fifth-year Ph.D. student in Computer Science at UC Berkeley, advised by Niloufar Salehi. 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. More broadly, I’m interested in exploring how developers, users, impacted communities, and policymakers can each play a role in facilitating accountability throughout the 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 and the Computational Research for Equity in the Legal System (CRELS) Fellowship. I was previously supported by the Berkeley AI Policy Hub Fellowship.

Publications

Berkeley

Liza Gak, Angela Jin, Andrea Gil, Niloufar Salehi.”Code Your Own Experience!”: Youth-Led Social Media Research and Action through Communities of Practice. Under submission.

Angela Jin, Niloufar Salehi. (Beyond) Reasonable Doubt: Challenges that Public Defenders Face in Scrutinizing AI in Court. In Proceedings of the 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]

Page last updated August 5, 2025.