Drew Prinster
Hi, I’m Drew. I’m currently a PhD student in computer science at Johns Hopkins University. My research aim is to improve the safety, reliability, and regulatability of AI and machine learning systems in consequential settings, especially healthcare and bioscience. To these ends, I mainly develop statistical tools (e.g., related to conformal prediction and hypothesis testing) for the design of trustworthy AI systems or safeguards. For instance, I work on:
- AI Control: - Methods to ensure that AI agents provably respect our declared safety constraints.
- AI Monitoring: - Continual monitoring, safety testing, and root-cause analysis of deployed AI systems.
- Uncertainty Quantification - Trustworthy predictive inference under distribution shift.
- Human-AI Teaming - Human-centered design for clinical decision-making.
I am advised by Professor Suchi Saria and Professor Anqi Liu. In 2025, I interned with Prescient Design at Genentech, where I began developing conformal policy control. I have also worked closely with Professor Chien-Ming Huang.
Previously at Yale University, I completed my B.S. (with distinction) in computer science and mathematics and was a Yale Global Health Scholar. At Yale I contributed to global health volunteering and research with the nonprofit Unite For Sight; to microbiome, mental health, and social-network research with Dr. Nicholas Christakis at the Yale Human Nature Lab; and to statistical testing of metagenome alignments with Dr. Mark Gerstein in the Gerstein Lab.
Contact: drew [at] cs [dot] jhu [dot] edu
Selected Publications
AI Control: Safety Constraints for AI Agents
@inproceedings{prinster2026conformal,
title = {Conformal Policy Control},
author = {Prinster, Drew and Fannjiang, Clara and Park, Ji Won and Cho, Kyunghyun and Liu, Anqi and Saria, Suchi and Stanton, Samuel Don},
booktitle = {Forty-third International Conference on Machine Learning},
year = {2026},
url = {https://arxiv.org/abs/2603.02196}
}
AI Monitoring: Post-Deployment Safety Monitoring
@InProceedings{pmlr-v267-prinster25a,
title = {{WATCH}: Adaptive Monitoring for {AI} Deployments via Weighted-Conformal Martingales},
author = {Prinster, Drew and Han, Xing and Liu, Anqi and Saria, Suchi},
booktitle = {Proceedings of the 42nd International Conference on Machine Learning},
pages = {49830--49859},
year = {2025},
editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry},
volume = {267},
series = {Proceedings of Machine Learning Research},
month = {13--19 Jul},
publisher = {PMLR},
url = {https://proceedings.mlr.press/v267/prinster25a.html}
}
@article{sadhuka2025valuator,
title = {E-valuator: Reliable agent verifiers with sequential hypothesis testing},
author = {Sadhuka, Shuvom and Prinster, Drew and Fannjiang, Clara and Scalia, Gabriele and Berger, Bonnie and Regev, Aviv and Wang, Hanchen},
journal = {arXiv preprint arXiv:2512.03109},
year = {2025},
url = {https://arxiv.org/abs/2512.03109}
}
Uncertainty Quantification under Distribution Shift
@InProceedings{pmlr-v235-prinster24a,
title = {Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them)},
author = {Prinster, Drew and Stanton, Samuel Don and Liu, Anqi and Saria, Suchi},
booktitle = {Proceedings of the 41st International Conference on Machine Learning},
pages = {41086--41118},
year = {2024},
editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix},
volume = {235},
series = {Proceedings of Machine Learning Research},
month = {21--27 Jul},
publisher = {PMLR},
url = {https://proceedings.mlr.press/v235/prinster24a.html}
}
@InProceedings{pmlr-v202-prinster23a,
title = {{JAWS}-X: Addressing Efficiency Bottlenecks of Conformal Prediction Under Standard and Feedback Covariate Shift},
author = {Prinster, Drew and Saria, Suchi and Liu, Anqi},
booktitle = {Proceedings of the 40th International Conference on Machine Learning},
pages = {28167--28190},
year = {2023},
editor = {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan},
volume = {202},
series = {Proceedings of Machine Learning Research},
month = {23--29 Jul},
publisher = {PMLR},
url = {https://proceedings.mlr.press/v202/prinster23a.html}
}
@inproceedings{prinster2022jaws,
title = {JAWS: Auditing Predictive Uncertainty Under Covariate Shift},
author = {Prinster, Drew and Liu, Anqi and Saria, Suchi},
booktitle = {Advances in Neural Information Processing Systems},
volume = {35},
publisher = {Curran Associates, Inc.},
year = {2022},
url = {https://papers.nips.cc/paper_files/paper/2022/hash/e944bacecce6b06374ac39b260348db0-Abstract-Conference.html}
}
Human-AI Teaming: Designing AI for Effective Collaboration
@article{prinster2024care,
title = {Care to explain? AI explanation types differentially impact chest radiograph diagnostic performance and physician trust in AI},
author = {Prinster, Drew and Mahmood, Amama and Saria, Suchi and Jeudy, Jean and Lin, Cheng Ting and Yi, Paul H and Huang, Chien-Ming},
journal = {Radiology},
volume = {313},
number = {2},
pages = {e233261},
year = {2024},
publisher = {Radiological Society of North America},
url = {https://pubs.rsna.org/doi/abs/10.1148/radiol.233261}
}