I am an Assistant Professor of Statistics at Harvard University. I develop scalable and principled methods for statistical inference in complex scientific models. My research proposes new ways of parallelizing Monte Carlo methods, of comparing statistical models based on predictive performance, of retrieving latent processes given observations, of performing inference in non-Markovian and nonlinear models, and of learning with purely generative models with intractable likelihoods.

In my recent articles, I look into:
  • Monte Carlo algorithms with parallel computing in mind;
  • nonlinear dynamical systems, state space models;
  • sequential inference (i.e. updating inference as data points arrive);
  • purely generative models and inference with intractable likelihoods using ideas from optimal transport;
  • Bayesian inference in models made of modules, model misspecification.
Recent talks:
  • 2018, November 1, seminar in the Stats department, University of Florida
  • 2018, April 6, seminar in the Maths department, University of Mississippi
  • 2018, January 9, 10:30am, séminaire d’Analyse-Probabilités, Université Paris-Dauphine, on unbiased MCMC.
  • 2018, January 11, 10:30am, CREST-ENSAE, Paris-Saclay, on Statistical learning in models made of modules.
  • 2018, April 6, Department of Mathematics, University of Mississipi, on unbiased MCMC.

I am not taking any interns over the summer. If you're interested in a PhD, look for info on the Department's website.

Email: pjacob at fas.harvard.edu.
Phone: (617) 496-9259
Office: Science Center 712, 1 Oxford Street, Cambridge, MA 02138