Scholar
Jonathan Huggins
Google Scholar ID: x1mbRloAAAAJ
Assistant Professor of Statistics, Boston University
Citations & Impact
Citations
1,019
H-index
17
i10-index
20
Publications
20
Co-authors
41
list available
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
- Preprints & Working Papers:
- - Quantitative Error Bounds for Scaling Limits of Stochastic Iterative Algorithms
- - Robust discovery of mutational signatures using power posteriors
- - Structurally Aware Robust Model Selection for Mixtures
- - Tuning Stochastic Gradient Algorithms for Statistical Inference via Large-Sample Asymptotics
- - Calibrated Model Criticism Using Split Predictive Checks
Research Experience
- 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.
Education
- Ph.D. in Computer Science, 2018, Massachusetts Institute of Technology; B.A. in Mathematics, 2012, Columbia University.
Background
- Assistant Professor in the Department of Mathematics & Statistics and the Faculty of Computing & Data Sciences at Boston University. His research focuses on developing fast, trustworthy learning and inference methods that balance computational efficiency and statistical optimality with the imperfections from real-world problems, large datasets, and complex models.
Miscellany
- Interests include large-scale learning, Bayesian computation, robust inference, and statistical genetics.
