I am a Postdoctoral Research Fellow in the Department of Biostatistics at Harvard, working with Jeff Miller and Scott Carter. The primary focus of my research is developing reliable approximate inference methods that are scalable to large datasets and complex models. My goal is to create algorithms with finite-data accuracy guarantees that users can trust in safety-critical domains such as clinical and medical diagnostic settings. Previously, I was a Ph.D. candidate at MIT, where I was advised by Tamara Broderick. My short bio has more details about my background.
Ph.D. in Computer Science, 2018
Massachusetts Institute of Technology
B.A. in Mathematics, 2012
Jonathan Huggins is a Postdoctoral Research Fellow in the Department of Biostatistics at Harvard. He is also affiliated with the Dana-Farber Cancer Institute and the Broad Institute of MIT and Harvard. He completed his Ph.D. in Computer Science at the Massachusetts Institute of Technology in 2018. Previously, he received a B.A. in Mathematics from Columbia University and an S.M. in Computer Science from the Massachusetts Institute of Technology. His recent research has focused on developing reliable approximate Bayesian inference methods that are scalable to large datasets and complex models.