Projects

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:

  1. 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).

  2. 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.

Preprints & Working Papers

Structurally Aware Robust Model Selection for Mixtures

arXiv:2403.00687 [stat.ME], 2024.

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Reproducible Parameter Inference Using Bagged Posteriors

arXiv:2311.02019 [stat.ME], 2023.

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Tuning Stochastic Gradient Algorithms for Statistical Inference via Large-Sample Asymptotics

arXiv:2207.12395 [stat.CO], 2022.

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Calibrated Model Criticism Using Split Predictive Checks

arXiv:2203.15897 [stat.ME], 2022.

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Robust, Automated, and Accurate Black-box Variational Inference

arXiv:2203.15945 [stat.ML], 2022.

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Publications

More Publications

Independent finite approximations for Bayesian nonparametric inference

Bayesian Analysis, 2023+.

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A Targeted Accuracy Diagnostic for Variational Approximations

In Proc. of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS), Valencia, Spain. PMLR: Volume 108, 2023.

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Reproducible Model Selection Using Bagged Posteriors

Bayesian Analysis 18(1): 79-104, 2023.

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The Mutational Signature Comprehensive Analysis Toolkit (musicatk) for the Discovery, Prediction, and Exploration of Mutational Signatures

Cancer Research 81(23), 2021.

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Challenges and Opportunities in High-dimensional Variational Inference

In Proc. of the 35th Annual Conference on Neural Information Processing Systems (NeurIPS), 2021.

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Bidirectional contact tracing could dramatically improve COVID-19 control

Nature Communications 12(232), 2021.

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Robust, Accurate Stochastic Optimization for Variational Inference

In Proc. of the 34th Annual Conference on Neural Information Processing Systems (NeurIPS), 2020.

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Validated Variational Inference via Practical Posterior Error Bounds

In Proc. of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), Palermo, Italy. PMLR: Volume 108, 2020.

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LR-GLM: high-dimensional Bayesian inference using low-rank data approximations

In Proc. of the 36th International Conference on Machine Learning (ICML), Long Beach, California. PMLR: Volume 97, 2019.

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The kernel interaction trick: fast Bayesian discovery of pairwise interactions in high dimensions

In Proc. of the 36th International Conference on Machine Learning (ICML), Long Beach, California. PMLR: Volume 97, 2019.

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Thesis

Scaling Bayesian inference: theoretical foundations and practical methods

Ph.D. thesis, Massachusetts Institute of Technology, 2018.

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Miscellanea

The feasibility of targeted test-trace-isolate for the control of SARS-CoV-2 variants

F1000Research 10(291), 2021.

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Reconstructing probabilistic trees of cellular differentiation from single-cell RNA-seq data

arXiv:1811.11790 [q-bio.QM], 2018.

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Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach

arXiv:1809.09505 [stat.TH], 2018.

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Detailed Derivations of Small-variance Asymptotics for some Hierarchical Bayesian Nonparametric Models

arXiv:1501.00052 [stat.ML], 2014.

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Infinite Structured Hidden Semi-Markov Models

arXiv:1407.0044 [stat.ME], 2014.

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Recent & Upcoming Talks

More Talks

Robust, structurally-aware inference for mixture models
May 18, 2023
Trustworthy variational inference
Oct 21, 2022
Algorithmically robust, general-purpose variational inference
Apr 13, 2022
Statistically robust inference with stochastic gradient algorithms
Dec 14, 2021

Short Bio

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. 

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