My main research interest is nonlinear inference, especially within the context of dynamical systems, solved using probabilistic models and algorithms. In terms of scientific fields, my research is situated somewhere on the intersection between the fields of machine learning, signal processing and automatic control. My aim is to pursue both basic and applied research, where the latter is typically carried out in close collaboration with industry. A brief overview of my research is available here and my publications are available here.
Recent research results/news
July 24, 2016: We have developed a Bayesian input design method for nonlinear state-space models. The key ingredients are Gaussian process optimization and the particle filter. This work will be presented at the 55th IEEE Conference on Decision and Control (CDC) that is held in Las Vegas in December.
Patricio E. Valenzuela, Johan Dahlin, Cristian R. Rojas and Thomas B. Schön. Particle-based Gaussian process optimization for input design in nonlinear dynamical models. In Proceedings of the 55th IEEE Conference on Decision and Control (CDC), Las Vegas, NV, USA, December, 2016. (accepted for publication) [arXiv]
July 5, 2016: Next week Pierre Jacob will present our coupling construction for the particle filter and the conditional particle filter at the World Congress in Probability and Statistics held in Toronto (Canada). All the details on the developments so far are available on arXiv.
July 4, 2016: In September Jeroen Hol (Xsens) will present new developments of our optimization based approach to human body motion caption. Early results in this direction were presented at the 19th IFAC World Congress (Cape Town, South Africa) in August 2014, where it was nominated for the best application paper prize. That paper is available here and an abstract outlining the new developments that will be presented at The 4th European Conference on Computational Optimization (EUCCO) held in Leuven (Belgium) is available here.
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