Jeff Miller

Jeff Miller PhotoJeff Miller
Assistant Professor of Biostastics
Harvard T.H. Chan School of Public Health

Jeff Miller holds a Bachelor of Science in Mechanical Engineering from the Georgia Institute of Technology, a Master of Science in Mechanical Engineering from Stanford University, and a PhD in Applied Mathematics from Brown University. He worked as a Postdoctoral Associate in the Department of Statistical Science at Duke University, and he has also served in the United States Air Force as a Project Manager for the Robotics Research Group. 

Miller's research focuses statistical methods for finding structure in complex systems, with applications in genomics and diseases of aging.  He has developed novel approaches for robustness to model misspecification, for inference in nonparametric Bayesian models, and for efficient sampling in constrained spaces. His future research will focus on developing methods for the analysis of high-throughput data, to help understand and combat diseases of aging such as Alzheimer's and cancer. He is currently developing a Bayesian method to infer the phylogenetic history of tumor cell populations in cancer patients.

He is the recipient of many distinguished awards, grants, and fellowships. At Brown University, he received the Outstanding Dissertation Award in the Physical Sciences, the Sigma Xi Outstanding Graduate Student Award, and the Presidential Award for Excellence in Teaching. He received a National Defense Science and Engineering Graduate (NDSEG) Fellowship, has been supported by NSF, NIH, and DARPA grants, and has received a number of travel grants and conference awards, including an ISBA New Researchers Travel Award in 2016.

Selected preprints and publications:

Robust Bayesian inference via coarsening, J. W. Miller and D. B. Dunson, arXiv:1506.06101, 2015.

Mixture models with a prior on the number of components, J. W. Miller and M. T. Harrison, arXiv:1502.06241, 2015.

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.

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.

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

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

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