Alexandre Bouchard-Côté

Associate Professor of Statistics

  University of British Columbia

  ESB building, room 3124


Job opening: we (a team including Leonid Chindelevitch, myself and others) are looking for at least one post-doc in the areas of pathogenic genome-wide association studies, phylodynamics, Approximate Bayesian Computation or Sequential Monte Carlo, and molecular epidemiology. More information here.

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.

  JASA paper

  Follow-up Annals of Statistics paper, on the geometric ergodicity of BPS

  Preprint of follow-up work, on non-linear trajectories and discrete piecewise deterministic Markov processes

  More information

Tools for Bayesian data science

We are developing a language and software development kit for doing Bayesian analysis. The design philosophy is centered around the day-to-day requirements of real world (Bayesian) data science. The inference engines brings to bear several recent advances such as non-reversible methods.

  Project site

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.

  Prepring on a change-of-measure based phylogenetic SMC algorithm.

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

  Long indel model based on the Poisson Indel Process.

  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.

  Nature Methods paper on the analysis of single cell data

  More papers

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

  More papers

See also: my arXiv pre-prints


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 favorite 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