Alexandre Bouchard-Côté

Associate Professor of Statistics

  University of British Columbia

  2016-2017: Academic visitor, Oxford Statistics


  24-29 St Giles', Oxford, OX1 3LB, room 1.05


Research highlights

Non-reversible Monte Carlo methods

Markov chain Monte Carlo (MCMC) is notoriously difficult to scale to problems having high-dimensional latent variables ("big models"), which arise in many scientific and engineering applications. We are working on an alternative to MCMC that we call the "Bouncy Particle Sampler" (BPS), which imports ideas from the field of molecular simulation to address this challenge.

  Preprint of JASA paper

  More information

Bayesian phylogenetic inference

As a result of advances in sequencing technologies, the fields of computational and statistical phylogenetics, which are concerned with the modelling and inference of evolutionary relationships, have been growing rapidly in recent years. I am particularly interested in computationally-intensive Bayesian methods and inference of complex evolutionary models.

  Novel sampling method based on Hamiltonian Monte Carlo for parameter-rich evolutionary models.

  More papers

Probabilistic modelling of the evolutionary dynamics and phylogeny of cancer

Proliferating cancer cells, in which DNA repair mechanisms are disrupted, accumulate mutations at a much faster rate than healthy cells do. This leads to the emergence of an evolutionary process inside the tumour. A current research frontier is the characterization of the evolutionary dynamics and phylogenies within individual cancer tumours, where multiple sub-populations of cancer cells acquire differentiating sets of mutations.

  Latest Nature Methods paper on the analysis of single cell data

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Computational historical linguistics

Phylogenetic trees (or networks, forests, etc) also play an important in linguistics, to describe how language change and splits in ancestral speaker populations gave rise to today's linguistic diversity. Computational methods are also starting to play an important role in this field.

  PNAS paper on automated ancient language reconstruction

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My main field of research is in computational statistics/statistical machine learning. I am interested in the mathematical side of the subject as well as in applications in linguistics and biology.

On the methodology side, I am interested in Monte Carlo methods such as SMC and MCMC, graphical models, non-parametric Bayesian statistics, randomized algorithms, and variational inference.

My favoriate applications, both in linguistics and biology, are related to phylogenetics in one way or another. Some examples of things I have currently/recently been working on: automated reconstruction of proto-languages; cancer phylogenetics; population genetics; pedigrees, tree and alignment inference.

In the past, I also did some work on machine translation, on logical characterization and approximation of labeled Markov processes, and on reinforcement learning.

Academic background