Publications
* denotes equal contribution. Also see my Google Scholar profile.
Journal Articles (Published or Accepted)
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The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review
Journal of the American Statistical Association, 2025
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The 2020 United States Decennial Census Is More Private Than You (Might) Think
Proceedings of the National Academy of Sciences, 2025
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Self-rankings as a predictor of scientific impact beyond peer review
Nature Computational Science, 2026
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Rejoinder: The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review
Journal of the American Statistical Association, 2026
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Recommending Best Paper Awards for ML/AI Conferences via the Isotonic Mechanism
Journal of Machine Learning Research, accepted, 2026
Conference Proceedings
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Unified Enhancement of Privacy Bounds for Mixture Mechanisms via f-Differential Privacy
Neural Information Processing Systems (NeurIPS), 2023
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Mitigating the Privacy–Utility Trade-off in Decentralized Federated Learning via f-Differential Privacy
Neural Information Processing Systems (NeurIPS), 2025
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Use and Effects of LLMs in Peer Review: A Randomized Experiment and Survey at ICML 2026
AI-Native Academia Workshop at NeurIPS 2026
Preprints
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The Exact Risks of Reference Panel-Based Regularized Estimators
Under major revision at the Journal of the American Statistical Association, 2024
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On Self-Training of Summary Data with Genetic Applications
Under major revision at the Journal of the Royal Statistical Society Series B, 2025
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A Sieve-Accelerated Quadrature Method for Exact Privacy Accounting in the 2020 U.S. Decennial Census
arXiv:2606.29835, 2026
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Supervised Fine-tuning with Synthetic Rationale Data Hurts Real-World Disease Prediction
arXiv:2606.10279, 2026
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Unifying Privacy Accounting: Information Equivalence and Information Loss
arXiv:2610.02414, 2026