I am an Assistant Professor of Statistics at Harvard University. I develop scalable and principled methods for statistical inference. That includes new ways of parallelizing Monte Carlo methods, of comparing statistical models, of retrieving latent processes given observations, and of learning when the likelihood is intractable.
In my recent articles, I look more specifically into:
- Monte Carlo algorithms based on couplings;
- nonlinear dynamical systems, state space models;
- sequential inference (i.e. updating inference as data points arrive);
- inference using ideas from optimal transport;
- Bayesian inference in models made of modules and model misspecification.
- 2019, February 27-28, keynote speaker at the Computation and Econometrics Workshop, GRIPS (Tokyo, Japan).
- 2018, December 5, Duke Machine Learning Seminar
- 2018, November 1, seminar in the Stats department, University of Florida
- 2018, October 4, Econometrics workshop, University of Chicago
2018, June 11, workshop on parallel Monte Carlo, University of Bristol 2018, April 6, seminar in the Maths department, University of Mississippi
You can find my CV here.
I am not taking any interns over the summer. If you're interested in pursuing a PhD program, 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