Our aim is to automate the extraction of knowledge and understanding from data. Allowing machines (and humans) to understand what is happening and to acquire new skills and learn new things. We achieve this by developing new probabilistic models and deriving algorithms capable of learnings these models from data. The systematic use of probability in representing and manipulating these models is key. It allows us to represent not only what we know, but to some extent also what we do not know. We take a particular interest in dynamical phenomena evolving over time.
Our research is multi-disciplinary and it sits somewhere on the intersection of areas of Machine learning and statistics, signal processing, automatic control and computer vision. We pursue both basic and applied research, which explains our tight collaboration with various companies. A slightly more detailed overview of our research is available here.
Recent research results/news
September 5, 2016: Our project Machine learning for diagnostic support in radiology together with Sectra was granted by Vinnova. This allows us to start up a joint industrial post-doc project on this topic.
September 1, 2016: Today we have two new PhD students starting, Anna Wigren and Carl Andersson. They will be working on large scale sequential Monte Carlo and deep learning. I very much look forward to the journey that lies ahead, welcome to the team!!
August 22, 2016: Over the coming month Jack Umenberger is visiting us from Ian Manchester’s group at the University of Sydney for a short pre-doc. Together with Johan Wågberg we will work on designing EM-type algorithms for nonlinear system identification using recent Lagrangian relaxations and new particle smoothing solutions. Some early proof-of-concept work for linear systems is available here.
August 19, 2016: I am very glad to announce that Christian Andersson Naesseth will spend this academic year doing his pre-doc at Professor David Blei’s lab at Columbia University in New York. The work will be focused around the use of variational approximations in machine learning. Christian was awarded a Fulbright grant to finance his pre-doc.
August 18, 2016: We have developed a new class of sequential Monte Carlo (SMC) algorithms that is especially well suited for inference in probabilistic graphical models (including models with loops). By making use of an auxiliary tree-structured decomposition of the model we turn the original problem into a a collection of recursively solved sub-problems. This divide-and-conquer strategy has also given the name Divide-and-Conquer Sequential Monte Carlo (D&C-SMC) to the new class of algorithms. We illustrate the performance on a Markov Random Field (MRF) and on a hierarchical logistic regression problem. This work has now been accepted for publication in the Journal of Computational and Graphical Statistics (JCGS)
Fredrik Lindsten, Adam M. Johansen, Christian A. Naesseth, Bonnie Kirkpatrick, Thomas B. Schön, John Aston and Alexandre Bouchard-Côté. Divide-and-Conquer with Sequential Monte Carlo. Journal of Computational and Graphical Statistics (JCGS), 2016. [arXiv]
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