Christian Andersson Naesseth
I am a PhD Student at the Division of Automatic Control, Department of Electrical Engineering, Linköping University since August 2013.
My current research interests are focused on computational statistics methods, such as Monte Carlo and variational Bayes, for Bayesian inference and learning in complex, high-dimensional statistical models. Applications for our methods can be found in such diverse fields as information theory, statistical mechanics, machine learning, system identification, spatial statistics and many, many more. My research is supervised by Dr. Fredrik Lindsten and Prof. Thomas Schön (both currently at Uppsala University, Sweden). During October 2015 I visited Dr. Frank Wood's lab at the University of Oxford, United Kingdom, to work on Monte Carlo methods for probabilistic programming.
For supplementary material, source code from my published work and more information about my research interests please see the research pages (menu to the left).
Jun 17, 2016: Next week I will be attending ICML in New York! We will be presenting our new algorithm Interacting Particle Markov Chain Monte Carlo, especially useful for probabilistic programming. The method is implemented on the development branch of the probabilistic programming system Anglican.
Apr 25, 2016: The new particle MCMC method we have developed, the Interacting Particle Markov Chain Monte Carlo, which efficiently makes use of distributed and multi-core computing architectures has been accepted to ICML 2016. You can find a pre-print on arXiv.
Here are the archived news.
PhD Student in Automatic Control
(Swedish: Doktorand i reglerteknik)
- +46 13 281087
- Dept. of Electrical Engineering
- Linköping University
- SE-581 83 Linköping
- Visiting Address:
- Campus Valla
- Building B
- Room 2A:522 (in the A corridor on the ground floor between entrance 25 and 27)
Page responsible: Christian Andersson Naesseth
Last updated: 2016-07-06