Jonathan Huggins

Jonathan Huggins is an Assistant Professor at Boston University, affiliated with the Department of Mathematics & Statistics and the Faculty of Computing & Data Sciences. He holds a Ph.D. in Computer Science from MIT (2018) and a B.A. in Mathematics from Columbia University (2012). His research focuses on developing fast, trustworthy machine learning and Bayesian methods that balance computational efficiency and statistical optimality, with applications in ecological forecasting and genomic data analysis.

Education:

• Ph.D. in Computer Science, Massachusetts Institute of Technology (2018)
• B.A. in Mathematics, Columbia University (2012)

Research Interests:

• Large-scale machine learning and Bayesian computation
• Robust statistical inference
• Applications in genomics and ecological modeling
• Algorithmic development for scalable inference

Key Projects:

• Stochastic Methods for Data Science: A book on stochastic processes and algorithms
• VIABEL: A Python package for variational inference and diagnostics
• ShorTeX: A LaTeX package for mathematical writing

Recent Articles: Focus on scalable Bayesian methods, error bounds for iterative algorithms, and mutational signature discovery. His work emphasizes reproducibility and robustness in statistical inference.

Awards:

• Blackwell–Rosenbluth Award (Outstanding Junior Bayesian Researcher)

Grants & Funding: Supported by NIH, NSF, and the Department of Defense. Active in advising students across multiple BU programs.

Labs/Teams: Affiliated with the BU URBAN Program, Program in Bioinformatics, and Department of Computer Science.