Joris Bierkens

Delft Institute of Applied Mathematics
TU Delft
Van Mourik Broekmanweg 6
2628 XE Delft
E-mail: Joris.Bierkens@tudelft.nl

Assistant professor
dr. ir. J. Bierkens

My interest lies in the computational challenges arising in Bayesian statistics and statistical physics. This means that I am mostly developing and analyzing new Markov Chain Monte Carlo (MCMC) algorithms.

At the moment my research has a strong focus on the use of Piecewise Deterministic Markov Processes for Monte Carlo purposes. This discovery opens up an entirely new family of elegant algorithms which can be extremely efficient in challenging settings. Naturally, this discovery also leads to many further open questions. See the project page for an overview.

News

2-8-2018: The first vacancy for a PhD student as part of my Vidi project has appeared online. I am looking for a student with a mathematics or statistics background with a keen interest in the theory of stochastic processes.

31-7-2018: Joint work with Kengo Kamatani and Gareth Roberts on High-dimensional scaling limits of piecewise deterministic sampling algorithms has appeared on arXiv.

29-5-2018: I have obtained a NWO Vidi grant. This means that over the coming years I will be able to appoint two PhD students and a postdoc on Zig-Zag related topics. Contact me if you are interested in carrying out research in this direction.

20-4-2018: Our paper The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis of Big Data (in collaboration with Paul Fearnhead and Gareth Roberts) has been accepted for publication in Annals of Statistics.

Projects for students

Feel free to contact me by e-mail if you would like to carry out a research project (BSc/MSc) in the direction of Markov Chain Monte Carlo, stochastic processes, or simulation in physics and/or statistics.

Alternatively, MSc projects with industrial partners are listed at this website.

Information for companies

R&D professionals with research questions concerning Bayesian statistics and in particular Markov Chain Monte Carlo methods are welcome to contact me.

Links

Statistics at TU Delft