Assistant Professor of Biostatistics

Harvard T.H. Chan School of Public Health

Department of Biostatistics

655 Huntington Avenue, Building 1, Room 419

Boston, Massachusetts 02115

I'm interested in using statistics to understand the molecular mechanisms of diseases of aging. My methodological research has focused on:

- robustness to model misspecification,
- nonparametric Bayesian models,
- frequentist analysis of Bayesian methods, and
- efficient algorithms for inference in complex models.

- inferring cancer tumor phylogenetic trees (clonal evolution),
- using RNA-seq data to study the molecular mechanisms of dietary restriction, and
- studying Alzheimer's disease using whole-genome sequences.

Fast and accurate approximation of the full conditional for gamma shape parameters, J. W. Miller, 2018. (pdf) (arXiv) (source code)

A detailed treatment of Doob's theorem, J. W. Miller, 2018. (pdf) (arXiv)

An elementary derivation of the Chinese restaurant process from Sethuraman's stick-breaking process, J. W. Miller, 2018. (pdf) (arXiv)

Robust Bayesian inference via coarsening, J. W. Miller and D. B. Dunson, Journal of the American Statistical Association (JASA) (In press). (updated version after major revisions) (older version on arXiv) (code and data)

Mixture models with a prior on the number of components, J. W. Miller and M. T. Harrison, Journal of the American Statistical Association (JASA), Vol. 0, 2017, pp. 1-17. (pub) (pdf) (arXiv) (code)

Flexible models for microclustering with application to entity resolution, B. Betancourt, G. Zanella, J. W. Miller, H. Wallach, A. Zaidi, and B. Steorts, Advances in Neural Information Processing Systems (NIPS), Vol. 29, 2016, pp. 1417-1425. (pub) (pdf) (arXiv)

Microclustering: When the cluster sizes grow sublinearly with the size of the data set, J. W. Miller, B. Betancourt, A. Zaidi, H. Wallach, and R. C. Steorts, Bayesian Nonparametrics: The Next Generation workshop, NIPS 2015. (pdf) (arXiv)

Inconsistency of Pitman-Yor process mixtures for the number of components, J. W. Miller and M. T. Harrison, Journal of Machine Learning Research, Vol. 15, 2014, pp. 3333-3370. (pub) (pdf) (arXiv)

A simple example of Dirichlet process mixture inconsistency for the number of components, J. W. Miller and M. T. Harrison, Advances in Neural Information Processing Systems (NIPS), Vol. 26, 2013. (pub) (pdf) (arXiv)

Importance sampling for weighted binary random matrices with specified margins, M. T. Harrison and J. W. Miller. (pdf) (arXiv)

Exact sampling and counting for fixed-margin matrices, J. W. Miller and M. T. Harrison, The Annals of Statistics, Vol. 41, No. 3, 2013, pp. 1569-1592. (pub) (pdf) (arXiv)

Reduced criteria for degree sequences, J. W. Miller, Discrete Mathematics, Vol. 313, Issue 4, 2013, pp. 550-562. (pub) (pdf) (arXiv)

Nonparametric and Variable-Dimension Bayesian Mixture Models: Analysis, Comparison, and New Methods,
J. W. Miller, Brown University, Division of Applied Mathematics, 2014.
(pdf)

(Received the Brown University Outstanding Dissertation Award in the Physical Sciences, generously sponsored by the Joukowsky Family Foundation.)

Exact enumeration and sampling of matrices with specified margins, J. W. Miller and M. T. Harrison, Unpublished report (2011). (pdf) (arXiv)

A practical algorithm for exact inference on tables, J. W. Miller and M. T. Harrison, Proceedings of the Joint Statistical Meetings 2010, Statistical Computing Section.