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 Prof. Thomas Schön (currently at Uppsala University, Sweden) and co-supervisor Dr. Fredrik Lindsten (currently at Uppsala University, Sweden).
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).
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.
Dec 1, 2015: Next week I will be attending NIPS in Montreal, Canada. I will be presenting two posters at the "Black Box Inference and Learning" and "Scalable Monte Carlo Methods" workshops. One is about Hamiltonian dynamics and its use in importance sampling-based algorithms, and the second one is work towards automating SMC inference for probabilistic programs and graphical models. They can both be found here.
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: 2016-04-26