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, April 16, seminar in Stanford University
- 2019, February 27-28, keynote speaker at the Computation and Econometrics Workshop, GRIPS (Tokyo, Japan).
- 2019, January 9, seminar in Università deli Studi di Padova, Italy
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
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