Michael Oberst is an assistant 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 in machine learning has been presented at the Conference on Neural Information Processing Systems, the International Conference on Machine Learning, and the Society for Artificial Intelligence and Statistics; 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 the Massachusetts Institute of Technology. Prior to joining Johns Hopkins, he was a postdoctoral associate in the Machine Learning Department at Carnegie Mellon University.
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New faculty join the department, with research areas ranging from human-centered artificial intelligence and machine learning to natural language processing and computer networking.