I am an Assistant Professor of Mathematics & Statistics and Computing & Data Sciences at Boston University. My formal bio has more details about my background.
My group’s research lies at the intersection of statistics and machine learning, with a focus on developing methods that are mathematically principled, scalable, and useful in practice. A central theme is that uncertainty quantification should remain trustworthy even when models are imperfect and inference is approximate — challenges that are especially acute in scientific applications involving heterogeneous data, latent structure, and substantial computational constraints. Our work spans four interconnected areas: (1) scalable generalized Bayesian learning, including theory and methods for robust, reproducible inference under model misspecification; (2) automation and validation of posterior approximation algorithms; (3) discovery of interpretable latent structure in complex scientific data; and (4) large-scale data assimilation and forecasting. Current applied work is focused on developing computational methods and software tools for large-scale ecological and Earth science forecasting and for scientific discovery from high-throughput genomic data. Increasingly, these efforts are also motivating a systems-oriented direction aimed at making end-to-end probabilistic workflows more scalable, transparent, and reproducible.
If you are currently enrolled in, or accepted to, a BU graduate program, feel free to reach out to me about research opportunities. I advise PhD students in Statistics and Computing & Data Sciences. 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: A LaTeX package that aims to streamline LaTeX writing, particularly math. It automatically includes and configures commons packages, and provides functionality to, among other things, (1) make LaTeX math code shorter and more readable, (2) avoid the verbose commands and boilerplate common in LaTeX, and (3) avoid multi-key presses (curly braces, capital letters, etc.) where reasonable. It is being developed by myself, Trevor Campbell, and Jeffrey Negrea.
Dr. Jonathan Huggins is an Assistant Professor of Mathematics & Statistics and of Computing & Data Sciences at Boston University. His group’s research lies at the intersection of statistics and machine learning, with a focus on developing methods that are mathematically principled, scalable, and useful in practice. A central theme is that uncertainty quantification should remain trustworthy even when models are imperfect and inference is approximate — challenges that are especially acute in scientific applications involving heterogeneous data, latent structure, and substantial computational constraints. Our work spans four interconnected areas: (1) scalable generalized Bayesian learning, including theory and methods for robust, reproducible inference under model misspecification; (2) automation and validation of posterior approximation algorithms; (3) discovery of interpretable latent structure in complex scientific data; and (4) large-scale data assimilation and forecasting. Current applied work is focused on developing computational methods and software tools for large-scale ecological and Earth science forecasting and for scientific discovery from high-throughput genomic data. Increasingly, these efforts are also motivating a systems-oriented direction aimed at making end-to-end probabilistic workflows more scalable, transparent, and reproducible.
Jonathan is also a Data Science Faculty Fellow and an affiliated faculty member of the Department of Computer Science, the BU URBAN Program, and the BU Program in Bioinformatics. He is a recipient of an NSF CAREER award (2024) and a Blackwell–Rosenbluth Award (2023), which recognizes outstanding junior Bayesian researchers based on their overall contribution to the field and to the community. His research has been supported by the National Institutes of Health, the National Science Foundation, and the Department of Defense.
Prior to joining BU, Jonathan 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 (Summa Cum Laude) and an S.M. in Computer Science from the Massachusetts Institute of Technology.