Harvard health shield
Jeff Miller
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

The goal of my research is to develop statistical methods to help understand diseases of aging at the cellular/molecular level. I am interested in using high-throughput genomic data to develop mathematical models of the key biological processes and molecular mechanisms that underlie age-related decline in general, and cancer in particular. In addition to advancing our scientific understanding, this will enable the development of accurate prognostic and diagnostic tools for precision medicine.

My methodological research focuses on:

  • robustness to model misspecification,
  • nonparametric Bayesian models,
  • frequentist analysis of Bayesian methods, and
  • efficient algorithms for inference in complex models.
Currently, I'm working on the following applications:
  • models for de-biasing high-throughput sequencing data,
  • inferring cancer tumor phylogenetic trees (clonal evolution),
  • biostatistical analysis of X-linked Dystonia Parkinsonism (XDP), and
  • models for tuberculosis risk assessment.


Inference in generalized bilinear models, J. W. Miller and S. L. Carter, 2020. (pdf) (arXiv)

Robust and reproducible model selection using bagged posteriors, J. H. Huggins and J. W. Miller, 2020. (pdf) (arXiv)

Using bagged posteriors for robust inference and model criticism, J. H. Huggins and J. W. Miller, 2020. (pdf) (arXiv)

Identifying longevity associated genes by integrating gene expression and curated annotations, F. W. Townes, K. Carr, and J. W. Miller, PLOS Computational Biology, 16(11): e1008429, 2020. (pub) (pdf) (bioaRxiv)

Asymptotic normality, concentration, and coverage of generalized posteriors, J. W. Miller, 2019. (pdf) (arXiv)

Real-time genomic characterization of advanced pancreatic cancer to enable precision medicine, A. J. Aguirre, J. A. Nowak, N. D. Camarda, R. A. Moffitt, and 57 others including J. W. Miller, Cancer Discovery, CD-18-0275, 2018. (pub)

Fast and accurate approximation of the full conditional for gamma shape parameters, J. W. Miller, Journal of Computational and Graphical Statistics (JCGS), Vol. 28, 2019, pp. 476-480. (pub) (pdf) (arXiv) (source code)

An elementary derivation of the Chinese restaurant process from Sethuraman's stick-breaking process, J. W. Miller, Statistics & Probability Letters, Vol. 146, 2019, pp. 112-117. (pub) (pdf) (arXiv)

A detailed treatment of Doob's theorem, 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) , Vol. 114, 2019, pp. 1113-1125. (pub) (extended version) (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. 113, 2018, pp. 340-356. (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, pp. 199-206. (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.