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 learning and inference 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 developing software tools and computational methods for (1) accelerating and improving large-scale forecasting of ecological systems and (2) enabling 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: 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.
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, the BU URBAN Program, the BU Program in Bioinformatics. He is a recipient of the Blackwell–Rosenbluth Award, which recognizes outstanding junior Bayesian researchers based on their overall contribution to the field and to the community. 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 learning and inference 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 developing software tools and computational methods for (1) accelerating and improving large-scale forecasting of ecological systems and (2) enabling more effective scientific discovery from high-throughput and multi-modal genomic data. His research is supported by the National Institutes of Health, the National Science Foundation, and the Department of Defense.