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Dr Sergio Bacallado

Sergio Bacallado is a lecturer in the Department of Pure Mathematics and Mathematical Statistics and a Fellow in Mathematics at Magdalene.

He completed undergraduate degrees in Mathematics and Chemistry from the Massachusetts Institute of Technology and a PhD in Structural Biology from Stanford University, where he was also Stein Fellow in the Department of Statistics. His research deals with Bayesian methodology, with an emphasis on nonparametric inference, and its application to biological data, such as molecular dynamics simulations and genomic data from human microbiome studies.


Research Interests

  • Bayesian nonparametrics and Monte Carlo methods
  • Inductive models for species sampling
  • Reversible Markov chains and random walks with reinforcement
  • Human microbiome studies
  • Molecular dynamics and drug development
  • Design of clinical trials and multi-arm bandit problems


SB, SB (MIT), PhD (Stanford)

Selected Publications

Boyu Ren, Sergio Bacallado, Stefano Favaro, Susan Holmes, and Lorenzo Trippa. Bayesian Nonparametric Ordination for the Analysis of Microbial Communities. Journal of the American Statistical Association, to appear, 2017.

Sergio Bacallado, Vijay Pande, Stefano Favaro, Lorenzo Trippa. Bayesian regularization of the length of memory in reversible sequences. Journal of the Royal Statistical Society B, 78 (4), pp. 933-946, 2016.

Sergio Bacallado, Persi Diaconis, Susan Holmes. de Finetti priors using Markov chain Monte Carlo computations. Journal of Statistics and Computing, (25), 797- 808, 2015.

Sergio Bacallado, Stefano Favaro, Lorenzo Trippa. Bayesian nonparametric analysis of reversible Markov chains. The Annals of Statistics, (41), 2, pp. 870- 896, 2013.

Sergio Bacallado. Bayesian analysis of variable-order, reversible Markov chains. The Annals of Statistics, (39), 2, pp. 838-864, 2011.