Dr. Matthew McDermott received his PhD in Computer Science from MIT, studying representation learning algorithms within machine learning for health and biomedicine in Professor Peter Szolovits’ clinical decision making group. Now, as a Berkowitz Postdoctoral Fellow at Harvard Medical School in Professor Isaac Kohane’s lab, he builds high-capacity “foundation models” and other representation learning systems over structured electronic health record (EHR) data to help build the next generation learning health system. His research historically has produced seminal results including one of the earliest and the most widely used pre-trained clinical language models, one of the first theoretical frameworks that identifies how pre-training losses used in representation learning induce structural constraints that motivate fine-tuning task performance, and multiple widely used software packages for performing machine learning analyses over structured EHR data. Prior to his PhD, Dr. McDermott studied mathematics at Harvey Mudd College, worked as a software engineer in data engineering at Google, and co-founded the startup Guesstimate.