Janet Jhih-Yi Hsieh |

About Me

I am an incoming first-year PhD student in Computer Science at Princeton University advised by Professor Lydia T. Liu. I am interested in human-AI collaboration in decision-making and the societal impacts of AI. Previously, I graduated from Carnegie Mellon University with MS+BS in Computer Science where I was fortunate to have been advised by Professors Nihar Shah, Aditi Raghunathan, Virginia Smith and Tian Li.

News

[Aug 2026] I will be starting my PhD at Princeton this fall!
[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

Bias Begins with Data The FairGround Framework and Corpus for Robust and Reproducible Research on Algorithmic Fairness

Jan Simson, Alessandro Fabris, Janet Jhih-Yi Hsieh, Cosima Fröhner, Frauke Kreuter, Christoph Kern

AAAI 2027 Artificial Intelligence for Social Impact Track (Under Submission).

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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