Drew Prinster

I’m a fourth-year Computer Science PhD student at Johns Hopkins University. My research aim is to improve the reliability and regulatability of AI and machine learning systems for high-stakes settings such as healthcare. Specifically, I primarily develop statistical tools (e.g., related to conformal prediction) for black-box AI systems to ultimately communicate to end users whether individual predictions can be trusted and to help monitor post-deployment risks. I’ve also been working on the human-centered design of AI/ML systems for optimizing human-machine teaming in healthcare. I am advised by Professor Suchi Saria and Professor Anqi Liu, and 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