I am an Assistant Professor in the Department of Mathematics & Statistics and the Faculty of Computing & Data Sciences at Boston University. My formal bio has more details about my background.
My research centers on the development of fast, trustworthy machine learning and AI methods that balance the need for computational efficiency and the desire for statistical optimality with the inherent imperfections that come from real-world problems, large datasets, and complex models. My current applied work is focused on methods to enable more effective scientific discovery from high-throughput and multi-modal genomic data.
If you are currently enrolled in, or accepted to, a BU graduate program, feel free to reach out to me about research opportunities. I can advise students in Math & Statistics, Computer Science, Bioinformatics, and CDS. I am not able to reply to all inquiries from students who are at other universities or are applying to BU graduate programs.
Ph.D. in Computer Science, 2018
Massachusetts Institute of Technology
B.A. in Mathematics, 2012
Columbia University
Stochastic Methods for Data Science: An in-progress book that provides an introduction to the interplay between stochastic process theory and algorithms in data science, with a focus (large-scale) stochastic optimization and Markov chain Monte Carlo. It is designed to be accessible to advanced undergraduates, graduate students, and researchers working in machine learning, statistics, and related fields.
VIABEL: A Python package that provides two core features:
Easy-to-use, lightweight, flexible variational inference algorithms that are agnostic to how the model is constructed (just provide a log density and its gradient).
Post hoc diagnostics for the accuracy of continuous approximations to (unnormalized) distributions. A canonical application is to diagnose the accuracy of variational approximations.
ShorTeX.sty: A LaTeX style file that includes a number of packages and macros to help make typesetting mathematical documents in LaTeX less painful and LaTeX code easier to read. Written with Trevor Campbell and Jeffrey Negrea.
Jonathan Huggins is an Assistant Professor in the Department of Mathematics & Statistics and the Faculty of Computing & Data Sciences at Boston University. He is also a Data Science Faculty Fellow and an affiliated faculty member of the Department of Computer Science and the BU Program in Bioinformatics. Prior to joining BU, he was a Postdoctoral Research Fellow in the Department of Biostatistics at 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 research centers on the development of fast, trustworthy machine learning and AI methods that balance the need for computational efficiency and the desire for statistical optimality with the inherent imperfections that come from real-world problems, large datasets, and complex models. His current applied work is focused on methods to enable more effective scientific discovery from high-throughput and multi-modal genomic data.