319 Malone Hall
Research Areas
Machine learning
Causal inference

Michael Oberst is an assistant research professor of computer science and a member of the Malone Center for Engineering in Healthcare.

Oberst’s research focuses on reliable machine learning for decision-making in health care. His long-term goal is to ensure that machine learning systems are as reliable as any FDA-approved medication or diagnostic test. His work has been published in machine learning conferences such as NeurIPS, ICML, and AISTATS; his research has additionally appeared in Science Translational Medicine.

Oberst received a BS in statistics from Harvard University and a PhD in computer science from MIT. Prior to joining Johns Hopkins, he was a postdoctoral associate in the Machine Learning Department at Carnegie Mellon University.