Janet Jhih-Yi Hsieh |

About Me

I am interested in human-AI collaboration in decision-making and the social impact of AI. I very passionate about studying real societal applications, and I would also like to think about how my research can inform policy.

I graduated from Carnegie Mellon University with a Master’s in Computer Science where I was fortunate to have been advised by Nihar Shah and Aditi Raghunathan. Prior to that, I graduated from Carnegie Mellon University with a Bachelor’s in Computer Science where I was fortunate to be advised by Virginia Smith and Tian Li for my senior thesis.

News

[Jan 2026] I am visiting Frauke Kreuter’s SODA (Social Data Science and AI) Lab at LMU Munich as a KODAQS (Competence Center for Data Quality in the Social Sciences) guest researcher hosted by Fiona Draxler.
[August 2025] I will be speaking at the USENIX Security Symposium 2025 (Seattle) about our accepted paper.
[June 2025] Our paper about vulnerabilities in peer reviewer matching is accepted to USENIX Security 2025.

Publications

Vulnerability of Text-Matching in ML/AI Conference Reviewer Assignments to Collusions

Jhih-Yi (Janet) Hsieh, Aditi Raghunathan, Nihar B. Shah

The USENIX Security Symposium (USENIX Sec), 2025.

(Non-archival) Championing Open-source DEvelopment in Machine Learning Workshop at ICML (CODEML), 2025.

(Long abstract) International Congress on Peer Review and Scientific Publication, 2025.

(Poster) NSF AI-SDM Workshop on Human-AI Complementarity for Decision Making, 2025.

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Mathematical Justification of Hard Negative Mining via Isometric Approximation Theorem

Albert Xu, Jhih-Yi Hsieh, Bhaskar Vundurthy, Nithya Kemp, Eliana Cohen, Lu Li, Howie Choset

The International Conference on Learning Representations (ICLR), 2024.

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On Trade-Offs Between Fairness, Robustness, and Privacy Through Tilted Losses

Jhih-Yi Hsieh, Advised by Tian Li and Virginia Smith

Undergraduate Thesis, Technical Report, 2023

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