Distinguished Postdoctoral Fellow

Columbia University

I am a Distinguished Postdoctoral Fellow in the Department of Statistics at Columbia University. Before joining Columbia, I was a graduate student at Harvard University, where my advisors were Pierre E. Jacob and Neil Shephard. In the 2017 fall semester, I was a visiting student at CREST - ENSAE ParisTech, hosted by Marco Cuturi and Nicolas Chopin, and supported by a Chateaubriand Fellowship. Before joining Harvard, I completed my undergraduate degree in mathematics at Imperial College London.

- PhD in Statistics, Harvard University (2019)
- MA in Statistics, Harvard University (2017)
- BSc in Mathematics, Imperial College London (2014)

**Optimal transport**and its applications in statistics and machine learning**Monte Carlo methods**and their connections to**optimization****Optimal control**, in particular the SchrÃ¶dinger bridge problem- Likelihood-free inference in
**generative models**

- Bernton, E., Jacob, P. E., Gerber, M., and Robert, C. P. (2019).
Approximate Bayesian computation with the Wasserstein distance.
*Journal of the Royal Statistical Society: Series B,*81(2):235-269. Supplementary materials. R-package. - Bernton, E., Jacob, P. E., Gerber, M., and Robert, C. P. (2019).
On parameter estimation with the Wasserstein distance.
*To appear, Information and Inference: A Journal of the IMA.* - Bernton, E. (2018). Langevin Monte Carlo and JKO splitting.
*Proceedings of Machine Learning Research*, 75:1777-1798. Presented at COLT'18. - Yang, S., Chen, Y., Bernton, E., and Liu, J. S. (2018). On parallelizable Markov chain Monte Carlo algorithms with waste-recycling.
*Statistics and Computing*28(5):1073-1081.

- Chateaubriand Fellowship (2017-2018). Grant supporting students at American universities who conduct research in France during their PhDs.
- Dempster Award (2017). Awarded yearly to one or two graduate students within the Department of Statistics at Harvard University, in particular those who have made contributions to theoretical or foundational research in statistics.
- Certifcate of Distinction in Teaching at Harvard University. Awarded to teaching assistants who receive student evaluations higher than 4.5 out of 5 points. Won four times, in the fall 2015, spring 2016, fall 2016 and spring 2018 semesters.

**SIAM CSE19 Symposium on statistical applications of measure transport.**February 2019. Title: Langevin Monte Carlo and JKO splitting.**Conference on Learning Theory.**Stockholm, July 2018. Title: Langevin Monte Carlo and JKO splitting.**Bayes in Paris Seminar Series.**Paris, October 2017. Title: Approximate Bayesian computation with the Wasserstein distance.**Optimal Transport meets Probability, Statistics and Machine Learning.**BIRS-CMO, Oaxaca, May 2017. Title: Inference in generative models using the Wasserstein distance.**Colloquium Series, Department of Statistics, Harvard University.**Cambridge, MA, April 2017. Title: Inference in generative models using the Wasserstein distance.**Harvard NLP, SEAS, Harvard University.**Cambridge, MA, March 2017. Title: Inference in generative models using the Wasserstein distance.

**Introduction to Statistics for Life Sciences (undergraduate level).**Teaching Assistant, Statistics 102, Harvard University in Spring 2017 and Spring 2018 with Profs. Dave Harrington and Kevin Rader respectively.**Statistical Inference I (graduate level).**Teaching Assistant, Statistics 211, Harvard University in Spring 2016 with Prof. Tirthankar Dasgupta.**Probability I (graduate level)**Teaching Assistant, Statistics 210, Harvard University in Fall 2015 and Fall 2016 with Prof. Joe Blitzstein.