I'm a PhD Student in Statistics at the Center for research in economics and statistics (CREST), Paris and currently in the last year of my studies.
My supervisor is Nicolas Chopin.
From October 2017 to April 2018 I visited Pierre E. Jacob at the Department of Statistics at Harvard University.
After graduate studies in economics, mathematics and statistics under the double degree program of
Humboldt University Berlin and ENSAE ParisTech,
I am currently writing a PhD thesis in applied mathematics at the University Paris Saclay.
The topic is on high dimensional Bayesian computation, with a focus on improving Monte Carlo simulations for models with numerous parameters to be inferred. My research has applications in statistics and machine learning.
Below you can find a list of past and current projects.
My research interest lies in Monte Carlo methods and approximate inference in general and in particular Hamiltonian Monte Carlo, sequential Monte Carlo, quasi Monte Carlo, approximate Bayesian computation and variational inference.
I've had the pleasure of assisting in teaching several courses, here are some.
- Mathematical Statistics - ENSAE ParisTech in Fall 2015, 2016 and 2017 with Nicolas Chopin
- Probability Theory - ENSAE ParisTech in Fall 2017 with Cristina Butucea
- Monte Carlo methods - ENSAE ParisTech in Spring 2016 and 2017 with Nicolas Chopin
- Introduction to statistics and econometrics - ENSAE ParisTech in Spring 2017 with Marco Cuturi
- Numerical Analysis - ENSAE ParisTech in Spring 2016 with Jérémie Jakubowicz
- Econometrics I - ENSAE ParisTech in Fall 2015 with Michael Visser
- Econometrics I - Humboldt University Berlin in Spring 2015 with Bernd Droge
- Ph.D. candidate, Applied Mathematics, ENSAE and University Paris Saclay [2015-2018 (expected)]
- M.Sc. Statistics, Humboldt University and Technical University, Berlin [2013-2015]
- Diploma Statistics and Economics, ENSAE, Paris [2012-2015]
- B.Sc. Economics, Humboldt University, Berlin [2010-2013]
- Buchholz A, Jacob PE, Chopin N (2018). Adaptive Tuning Of Hamiltonian Monte Carlo Within Sequential
- Buchholz A, Chopin N, Mandt S, Kloft M, Opper M, (2016). Hamiltonian Monte Carlo for Wishart distributions.
- Improving approximate Bayesian computation via quasi Monte Carlo, Rencontre des jeunes statisticiens, Porquerolles, France, 4/5/2017
- Improving approximate Bayesian computation via quasi Monte Carlo, Young Statistician Meeting, UCL, London UK, 8/18/2016
- Improving approximate Bayesian computation via quasi Monte Carlo, Machine learning summer school, Tuebingen Germany, 6/26/2017
- Improving approximate Bayesian computation via quasi Monte Carlo, ABCruise, Helsinki Finland, 5/17/2016
- Hamiltonian Monte Carlo for Wishart distributions, International conference on Monte Carlo techniques, Paris France, 7/6/2016