I am an Associate Professor of Statistics at Harvard University. I develop computational methods for statistical inference, e.g. ways of parallelizing Monte Carlo methods, of comparing statistical models, of estimating latent variables given noisy measurements of them, and of dealing with intractable likelihood functions. At Harvard I mainly teach an undergraduate course on time series and a graduate course on statistical inference; I also organize PhD level courses on various topics.
In my recent articles, I look more specifically into:
- the methodological use of probability couplings;
- nonlinear dynamical systems and state space models;
- statistical inference using ideas from optimal transport;
- Bayesian inference in models made of modules, and model misspecification.
- 2020, April 12-18, workshop on Data Assimilation at Oberwolfach, Germany
- 2019, December 14-16, CMStatistics 2019, London, UK
- 2019, September 16-19, workshop at the University of Lancaster, UK
2019, April 16, seminar in Stanford University 2019, February 27-28, keynote speaker at the Computation and Econometrics Workshop, GRIPS (Tokyo, Japan).
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