Research results/news


September 12, 2018 [New team members!] I am very glad to welcome two new PhD students to our team, Fredrik Gustafsson and Niklas Gunnarsson (WASP industrial PhD student together with Elekta). We also have a visiting PhD student Antônio Ribeiro from Universidade Federal de Minas Gerais (UFMG) in Belo Horizonte, Brazil. Antônio will visit us for one year and we will be working on the intersection of machine learning and control, especially with connections to optimization.

September 5, 2018 [Paper accepted for NIPS] This work deals with decision making for dynamical systems in the presence of uncertainty. More specifically, the problem we address lies at the intersection of reinforcement learning and robust control, and can be summarized as follows: given observations from an unknown dynamical system, we seek a policy to optimize the expected cost (as in reinforcement learning), subject to certain robust stability guarantees (as in robust control).

We have for a long time been developing algorithms to learn probabilistic models of dynamical systems from measured data. This is the first work where we are trying to make use of these models to build controllers with stability guarantees. The recent series of blog posts (nicely summarized in this paper) of Ben Recht was a great source of inspiration for our work.

[C106] Jack Umenberger and Thomas B. Schön. Learning convex bounds for linear quadratic control policy synthesis. In Neural Information Processing Systems (NIPS), Montréal, Canada, December 2018. [arXiv]

September 3, 2018 [New PhD thesis to be defended] On October 12 at 10.15 Andreas Svensson will present and defend his PhD thesis in room ITC/2446 here at Uppsala University. The title of the thesis is: Machine learning with state-space models, Gaussian processes and Monte Carlo methods. The thesis is available here. The opponent is Professor Carl Rasmussen from the University of Cambridge and the grading committee consists of Docent Tatjana Pavlenko (KTH), Docent Carl Henrik Ek, (University of Bristol) and Professor Kaj Nyström (Uppsala University). Welcome!

September 1, 2018 [Paper accepted for Nuclear inst. and methods in physics research section B] In this paper we present our new results on modelling and reconstruction of strain fields, relying upon data generated from neutron Bragg-edge measurements. This is an application of our Gaussian process construction from NIPS 2017 that correctly accounts for known linear constraints. More specifically the strain field is modelled as a Gaussian process, assigned a covariance structure customised by incorporation of the so-called equilibrium constraints. The results indicate a high potential and we hope that this will inspire the concept of probabilistic modelling to be used within other tomographic applications as well.

Carl Jidling, Johannes Hendriks, Niklas Wahlström, Alexander Gregg, Thomas B. Schön, Chris Wensrich and Adrian Wills. Probabilistic modelling and reconstruction of strain. Nuclear instruments and methods in physics research section B, 2018. [arXiv]

July 4, 2018 [Paper accepted for JASA] We have introduced a new coupling construction for particle filters which we also exploit to derive a new unbiased estimator of smoothing expectations. The lack-of-bias property has methodological benets in that independent estimators can be generated in parallel and condence intervals can be constructed from the central limit theorem. To design unbiased estimators, we combine a generic debiasing technique for Markov chains and a Markov chain Monte Carlo algorithm for smoothing.

Pierre E. Jacob, Fredrik Lindsten and Thomas B. Schön. Smoothing with couplings of conditional particle filters. Journal of the American Statistical Society (JASA), 2018. [arXiv]

June 7, 2018 [New Automatica paper] We revisit the classical problem of maximum likelihood learning of linear time-invariant state space models. Our contribution is that we add model stability guarantees by combining Lagrangian relaxation with the expectation maximization (EM) algorithm to build tight bounds on the likelihood. We then derive two algorithms that can be used to optimize these bounds over convex parameterizations of all stable linear models using semidefinite programming.

[J34] Jack Umenberger, Johan Wågberg, Ian Manchester and Thomas B. Schön. Maximum likelihood identification of stable linear dynamical systems. Automatica, 2018. (Accepted for publication) [arXiv] [code]

June 1, 2018 [2 guest PhD students] Over the coming month I am very glad to welcome our two guest PhD students Timothy Rogers from the University of Sheffield in the UK and Matteo Scandella from the University of Bergamo in Italy. Both will be working on sequential Monte Carlo and Birch.

May 11, 2018 [Paper accepted for ICMLWe provide new results for the problem of predicting spatio-temporal processes with temporal patterns that vary across spatial regions, when data is obtained as a stream. More specifically, we develop a localized spatio-temporal covariance model of the process that can capture spatially varying temporal periodicities in the data. We then apply a covariance-fitting methodology to learn the model parameters which yields a predictor that can be updated sequentially with each new data point.

[C105] Muhammad Osama, Dave Zachariah and Thomas B. Schön. Learning localized spatio-temporal models from streaming data. In Proceedings of the 35th International Conference on Machine Learning (ICML)Stockholm, Sweden, July, 2018. [arXiv]

April 6, 2018 [Three conference accepted] We three papers accepted for the 18th IFAC Symposium on System Identification (SYSID) held in Stockholm this summer.

[C103] Carl Andersson, Niklas Wahlström and Thomas B. Schön. Data-driven impulse response regularization via deep learning. In Proceedings of the 18th IFAC Symposium on System Identification (SYSID), Stockholm, Sweden, July, 2018[arXiv]

[C102] Andreas Svensson, Dave Zachariah and Thomas B. Schön. How consistent is my model with the data? Information-theoretic model check. In Proceedings of the 18th IFAC Symposium on System Identification (SYSID), Stockholm, Sweden, July, 2018. [arXiv]

[C101] Andreas Svensson, Fredrik Lindsten and Thomas B. Schön. Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations. In Proceedings of the 18th IFAC Symposium on System Identification (SYSID), Stockholm, Sweden, July, 2018. [arXiv]

March 21, 2018 [First pre-release of our new probabilistic programming language Birch] Within our research project ASSEMBLE (funded by the Swedish Foundation for Strategic Research) we are developing a new probabilistic programming language, which we call Birch. Everyone is welcome to play with it. More information is available here.

March 16, 2018 [Paper accepted for IEEE Transactions on Robotics] By using Maxwell's equations, we derive a probabilistic modeling approach for interpolation and extrapolation of the magnetic field. We model the magnetic field components jointly by imposing a Gaussian process (GP) prior on the latent scalar potential of the magnetic field. By rewriting the GP model in terms of a Hilbert space representation, we circumvent the computational pitfalls associated with GP modeling and provide a computationally efficient and physically justified modeling tool for the ambient magnetic field. The model allows for sequential updating of the estimate and time-dependent changes in the magnetic field and is applied in real-world applications. This is joint work with our friends Arno Solin and Simo Särkkä at Aalto University, Finland.

Arno Solin, Manon Kok, Niklas Wahlström, Thomas B. Schön and Simo Särkkä. Modeling and interpolation of the ambient magnetic field by Gaussian processes. IEEE Transactions on Robotics, 2018. (Accepted for publication) [arXiv] [video]

February 28, 2018 [Looking for new PhD students in Machine Learning to join our team] A short informal overview of some of our research is available here and a link to the advertisment is available here.

February 27, 2018 [Our team is now part of the WASP project] WASP stands for Wallenberg Artificial  Intelligence, Autonomous Systems and Software Program. It is Sweden’s largest ever individual research program. More information about the project is available here.

February 27, 2018 [Welcome to 2018 edition of the PhD course in Probabilistic Machine Learning] More information is available from the course website, which is available here.

January 22, 2018 [Paper accepted for ACC 2018] We have developed a decision-theoretic formulation of the parametric system identification problem that bridges the gap between the classical and the more recent regularized approaches. The end result is a regularized method that is robust to small sample-sizes as well as  overparameterization. 

[C100] Johan Wågberg, Dave Zachariah and Thomas B. Schön. Regularized parametric system identification: a decision-theoretic formulation. In Proceedings of the American Control Conference (ACC), Milwaukee, WI, USA, June, 2018. [arXiv]

January 16, 2018 [NewLEADS in the news] A popular scientific article about our new research environment NewLEADS is available here.


December 22, 2017 [Paper accepted for AISTATS] This is our first paper making use of our new probabilistic programming language (Birch). The contribution is a construction that automatically makes use of Rao-Blackwellization to exploit a conditionally linear Gaussian sub-structure when it is present in the model.

[C99] Lawrence M. Murray, Daniel Lundén, Jan Kudlicka, David Broman and Thomas B. Schön. Delayed sampling and automatic Rao-Blackwellization of probabilistic programs. In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS)Lanzarote, Spain, April, 2018. [arXiv]

October 31, 2017 [New grant from the Swedish Research Council] Our new ideas on how to build flexible models for nonlinear dynamical systems were granted from the Swedish Research Council. More details about the call are available here.

October 23, 2017 [Journal paper accepted for Mechanical Systems and Signal Processing] We are concerned with the problem of learning probabilistic nonlinear state space models of dynamical systems from measured data. In this invited tutorial we provide a self-contained introduction to one of the state-of-the-art methods - the particle Metropolis-Hastings algorithm - which has proven to offer a practical approximation. It is a Monte Carlo based method, where the particle filter is used to guide a Markov chain Monte Carlo method through the parameter space.

Thomas B. Schön, Andreas Svensson, Lawrence Murray and Fredrik Lindsten. Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo. Mechanical Systems and Signal Processing (MSSP), 2017. [pdf] [arXiv] [code]

September 25, 2017 [Journal paper accepted for IFAC Journal of Systems and Control] The Gaussian Process has three traditional shortcomings: 1) it is computationally intensive, 2) it cannot effectively deal with stochastic (noisy) inputs and 3) it cannot effectively incorporate new measurements in an online fashion. We have derived an algorithm that handles all three shortcomings. The new results are illustrated by solving nonlinear system identification problems.

Hildo Bijl, Thomas B. Schön, Jan-Willem van Wingerden and Michel Verhaegen. System identification through online sparse Gaussian process regression with input noise. IFAC Journal of Systems and Control, 2017. (Accepted for publication) [pdf] [arXiv] [ScienceDirect]

September 14, 2017 [Teaching the SMC methods course in Brussels] I will teach the SMC methods course in Brussels during the time period October 3 - 6, 2017. More complete information about the August edition of the course is available here.

September 12, 2017 [Journal paper accepted for Mechanical Systems and Signal Processing] A particle filtering based algorithm used to identify a nonlinear dynamical system based on observations that are highly informative (observations where there is very little or no measurement noise present) often ends up in trouble. To counteract this we suggest an algorithm which initially assumes that there is artificial measurement noise present. The variance of this noise is sequentially decreased in an adaptive fashion such that we in the end recover the original problem or possibly a very close approximation of it. Computationally the parameters are learned using a sequential Monte Carlo (SMC) sampler. We illustrate the new algorithm on one of the benchmarks for nonlinear system identification avilable here. More specifically we learn a challenging Wiener-Hammerstein system (described here) arising from an electrical circuit.

Andreas Svensson, Thomas B. Schön and Fredrik Lindsten. Learning of state-space models with highly informative observations: a tempered Sequential Monte Carlo solution. Mechanical Systems and Signal Processing (MSSP), 2017. [arXiv]

September 4, 2017 [Paper accepted for NIPS]  In this work we present a new probabilistic model in the form of a Gaussian process that is guaranteed to fulfill linear operator constraints. These constraints can for example encode fundamental properties of physics. One of the examples involve modelling of the magnetic field, which is known to be curl-free. This basic knowledge is encoded in our model from the start and as we aquire measurements the model is adapted accordingly. This opens up for exciting opportunities, where we can start from basic scientific laws and then fill in with specific knowledge optained from measurements in the particular situation at hand, in such a way that we at all times properly accounts for uncertainty throughout the entire model.

Carl Jidling, Niklas Wahlström, Adrian Wills and Thomas B. Schön. Linearly constrained Gaussian processesAdvances in Neural Information Processing Systems (NIPS), Long Beach, CA, USA, December, 2017. [arXiv]


September 1, 2017 [SMC workshop and PhD course] The Sequential Monte Carlo workshop and the associated PhD course are now over. All the material is available from the course and workshop websites. If you are still interested in the PhD course it is offered once more in October, but that time it is given in Brussels, Belgium. More information will be available here shortly.

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August 23, 2017 [SMC PhD course starting tomorrow] Tomorrow we start the intensive PhD course on SMC methods here in Uppsala. There are 85 students registered for the course, from 13 countries representing 35  universities and companies. More information about the course is available here. Directly after the course we are hosting the international conference on SMC at Norrlands Nation here in Uppsala with roughly 100 participants, more information about this event is available here.

August 3, 2017 [Paper accepted for Foundations and Trends in Signal Processing] We have been working with inertial sensors (accelerometers and gyroscopes) for well over a decade now. Over the past years we have made a significant effort to documents what we have learnt when it comes to the signal processing that is necessary in order to bring useful information out of the raw measurements of acceleration and angular velocity. I am very glad to announce that these end result of all this work is now accepted for publication in Foundations and Trends in Signal Processing. Our aim is to provide a self-contained introduction on how to use these sensors for position and orientation computations among other things.

Manon Kok, Jeroen Hol and Thomas B. Schön. Using inertial sensors for position and orientation estimation. Foundations and Trends in Signal Processing, 2017. (Accepted for publication) [arXiv]

July 12, 2017 [Paper accepted for CDC] We have new results on the construction of probabilistic optimization algorithms (algorithms capable of solving optimization problems when we only have access to noisy versions of the cost function and its derivatives). It has recently been shown that most of the existing quasi-Newton algorithms can be formulated as learning algorithms, capable of learning local models of the cost function. We leverage this fact together with the non-parametric and probabilistic Gaussian process model in solving these stochastic optimization problems. Our new algorithm unites these approximations together with recent probabilistic line search routines to deliver a generally applicable probabilistic quasi-Newton approach.

Adrian G. Wills and Thomas B. Schön. On the construction of probabilistic Newton-type algorithms. In Proceedings of the 56th IEEE Conference on Decision and Control (CDC), Melbourne, Australia, December 2017. [arXiv]

July 5, 2017 [2 new post-docs joining the team] After the summer we have two new post-docs joining us. Juozas Vaicenavičius will be an industrial post-doc working on deep learning for autonomous driving together with our collaborators at Autoliv. Juozas is also affiliated with the center of interdisciplinary mathematics (CIM) here at Uppsala University. Jack Umenberger from the University of Sydney brings in important knowledge on optimization based methods for nonlinear system identification and machine learning and will be working in the ASSEMBLE project.

May 15, 2017 [Best paper award] Christian Naesseth received the best paper award at the 20th International Conference on Artificial Intelligence and Statistics (AISTATS). The award to given for this paper. Congratulations!!

May 8, 2017 [3 new visitors] We have just welcomed three new visitors to the team. Professor Bhushan Gopaluni from the University of British Columbia in Vancouver (Canada) is visiting for two months, Dr Koen Tiels from the Vrije Universiteit Brussel (Belgium) is staying for 2 weeks. Finally, we have Tom Jin visiting from the Department of Statistics at Warwick University in Warwick (UK), where he is a PhD student within OxWaSp. Welcome!!

April 20, 2017 [New post-doc] I am very glad to welcome Jalil Taghia to the team. Jalil is currently a post-doc at Stanford (Department of Psychiatry and Behavioral Sciences), and he will join us on July 1, 2017. He will be working on deep learning, variational inference and sequential Monte Carlo. In terms of applications he will be involved in a project that Fredrik Lindsten has together with Sectra.

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April 4, 2017 [Registration for the SMC workshop 2017 and the associated intensive PhD course has now opened!] Sequential Monte Carlo (SMC) methods, also known as particle filters or particle methods, have over the past two decades emerged as very successful tools for computational inference in statistical models, including (but not limited to) nonlinear dynamical systems. The aim of the workshop is to bring together researchers developing and using SMC methods in various scientific fields (both in academia and industry). The list of speakers should make for a very interesting event; hope to see you there!

March 16, 2017 [New online learning method for large/streaming data] We have developed a new online learning method for prediction especially well suited in settings involving large and/or streaming data sets. The predictor is implemented online with a runtime that scales linearly in the number of samples; has a constant memory requirement; avoids local minima problems; and prunes away redundant feature dimensions without relying on restrictive assumptions on the data distribution.

Dave Zachariah, Petre Stoica and Thomas B. Schön. Online learning for distribution-free prediction. Pre-print arXiv:1703.05060. [arXiv]

March 1, 2017 [New results accepted for IFAC World congress 2017] We have some new insights to share on the classic problem of smoothing in linear state space models. More specifically we consider the problem of fixed interval smoothing in linear time-varying Gaussian state space models. Interestingly all existing solutions to this problem impose restrictions on the system matrices in order for them to be applicable. We develop new forward-backward type recursions that are applicable under the mildest assumptions possible.

Li-Hui Geng, Brett Ninness, Adrian G. Wills and Thomas B. Schön. Smoothed state estimation via efficient solution of linear equationsIn Proceedings of the 20th World Congress of the International Federation of Automatic Control (IFAC), Toulouse, France, July, 2017.

February 24, 2017 [New research environment will be created] Our research environment “NewLEADS - New Directions in Learning Dynamical Systems" together with researchers at KTH has been granted funding from the Swedish research council. More information is available here.

February 7, 2017 [Looking for 2 PhD students in Machine Learning (Deep Learning)] We are looking for two PhD students in Machine Learning working closely with two of our industrial partners (Autoliv and Sectra). This is an effort to further expand our research in Deep Learning and to extend our collaborations with Autoliv and Sectra. More information is available here (jointly with Autoliv) and here (jointly with Sectra). The formal applications are submitted here.

January 25, 2017 [New paper accepted for AISTATS 2017] The backgound to this paper is that the existing uncertainty estimates for Gaussian processes employing hyperparameters that are learnt from data are actually wrong. The contribution of the paper is that we derive a correct and fundamental lower bound of the mean square error (MSE). Importantly this bound is available as a closed-form expression that is simple and cheap to compute, so whenever you need to compute the uncertainty from a Gaussian process you should use these results. The paper will be presented at the 20th International Conference on Artificial Intelligence and Statistics (AISTATS) held in Fort Lauderdale, FL, USA in April, 2017.

Johan Wågberg, Dave Zachariah, Thomas B. Schön and Petre Stoica. Prediction performance after learning in Gaussian process regression. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), Fort Lauderdale, FL, USA, April, 2017.

January 23, 2017: [One finished and two new PhD students] Manon Kok successfully defended her PhD thesis and she is now moving on to the University of Cambridge where she will do her post-doc with Carl Rasmussen in the Machine Learning group. We also have two new PhD students joining the team; Muhammad Osama (starting as soon as his residence permit is grated) and Carl Jidling (starting on February 1). Really look forward to working with you, welcome to the team!

January 10, 2017: [Two updates on the Machine Learning activities in Uppsala] 1. We (Fredrik LindstenAndreas SvenssonNiklas Wahlström and myself) will offer a course in statistical Machine Learning designed for fourth year MSc students, starting on Monday next week. More information is available from the course web site. 2. The Machine Learning activities are covered by the arena page which is available here.



December 13, 2016: [New PhD thesis] Next month on Friday January 13 Manon Kok will defend her PhD thesis entitled Probabilistic modeling for sensor fusion with inertial measurements. The thesis is available here. The faculty opponent is Eric Foxlin (founder and builder of InterSense) currently working for fitbit. The thesis committe consists of Professor Fredrik Tufvesson (Lund University), Docent Isaac Skog (KTH), Docent Edith Ngai (Uppsala University) and as fall-back committee member we have Professor Magnus Herberthsson (Linköping University).

December 11, 2016: [New Automatica paper] Our new flexible nonparametric nonlinear state space model has been accepted for publication in Automatica. The model builds upon a basis function expansion and we use a connection to Gaussian processes to develop priors on the coefficients, for tuning the model flexibility and to prevent overfitting to data, akin to a Gaussian process state space model (GP-SSM). The priors can alternatively be seen as a regularization, and helps the model in generalizing the data without sacrificing the richness offered by the basis function expansion. An  important part of the contribution is to show that it is possible to do regularization in learning nonlinear dynamical systems.

Andreas Svensson and Thomas B. Schön. A flexible state space model for learning nonlinear dynamical systems. Automatica, 2016. (Accepted for publication) [pdf] [code]

November 21, 2016: [New licentiate thesis] On December 16 Andreas Svensson will present his licentiate thesis entitled "Learning probabilistic models of dynamical phenomena using particle filters” (available here). The discussion leader will be Richard Turner from the Machine Learning group at the University of Cambridge (UK). 

October 27, 2016: [Video lecture] My WASP lectures on Machine learning are available here (video) and the slides are available here and here. This is part of a basic PhD course on Autonomous systems.

October 7, 2016: [New researcher] On Tuesday next week Lawrence Murray will join us as a researcher working on the ASSEMBLE project. Lawrence moved from the University of Oxford where he just finished his post-doc together with Arnaud Doucet. Welcome to the team!

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 CarloJournal of Computational and Graphical Statistics (JCGS), 2016[arXiv]

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), December, Las Vegas, 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.

June 10, 2016: We (Pierre JacobFredrik Lindsten and myself) have developed a new coupling construction for particle filters and conditional particle filters. The coupled particle filter improves the performance significantly for example when it comes to computing gradients and using it inside the particle Metropolis Hastings algorithm. When the coupled conditional particle filter is combined with a recent debiasing technique we obtain a new smoothing strategy with the appealing properties of 1) straightforward parallelization and 2) the construction of accurate error estimates. Neither of the above is possible with existing particle smoothers. All the details are available in this arXiv:ed paper:

Pierre E. Jacob, Fredrik Lindsten and Thomas B. Schön. Coupling of Particle Filters. Pre-print arXiv, June, 2016. [arXiv]

May 16, 2016: I gave an interview about our new ASSEMBLE project, you can find it here.

May 10, 2016: We have three new papers accepted, where we present new algorithms for the calibration of magnetometers and magnetometers and a scalable and distributed solution to inertial motion capture, respectively.

Manon Kok and Thomas B. Schön. Magnetometer calibration using inertial sensorsIEEE Sensors Journal, 2016.

Manon Kok, Sina Khoshfetrat Pakazad, Thomas B. Schön, Anders Hansson and Jeroen D. Hol. A scalable and distributed solution to the inertial motion capture problem. Submitted to the 19th International conference on information fusion, Heidelberg, Germany, June, 2016. [arXiv]

Fredrik Olsson, Manon Kok, Kjartan Halvorsen and Thomas B. Schön. Accelerometer calibration using sensor fusion with a gyroscope. In Proceedings of the IEEE Workshop on Statistical Signal Processing (SSP), Palma de Mallorca, Spain, June, 2016.

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April 12, 2016: Our project ASSEMBLE goes live! We will work on automating machine learning and bringing powerful algorithms (like Sequential Monte Carlo) to the users. The project is funded by The Swedish foundation for strategic research. The partners are David Black-Schaffer (Uppsala university), Joakim Jaldén (KTH) and David Broman (KTH). Link to SSF announcement.

April 7, 2014: I am very glad to welcome Niklas Wahlström as a post-doc in the team! Niklas will work on Deep reinforcement learning and modelling in general.

March 24, 2016: Liang Dai and Johan Dahlin will defend their PhD theses on April 29 and May 4, respectively. Liang’s thesis is entitled Identification using convexification and recursion and it is available here. Johan’s thesis is entitled Accelerating Monte Carlo methods for Bayesian inference in dynamical models and it available here. Professor Magnus Johansson (KTH) will serve as the opponent for Liang’s thesis and the grading committee consists of: Professor Maya Neytcheva (Uppsala University), Docent Martin Enqvist (Linköping University) and Professor Wolfgang Birk (Luleå Technical University). Dr Richard Everitt (University of Reading, UK) and the grading committee consists of: Professor Filip Lindskog (Stockholm University) Dr Paolo Giordani (Sveriges Riksbank) and Dr Johan Karlsson (KTH).

March 18, 2016: The slides for my tutorial on Sunday are available here: Lecture 1Lecture 2Lecture 3Lecture 4. The underlying  paper is available here (published at SYSID 2015) and the code implementing the examples is available here. There are also some exercises available here (and data to be used is available here).

January 18, 2016: The 5th edition of the Statistical Machine Learning (SML) course starts tomorrow. The theme of this year’s projects is high-energy particle physics. Some interactions between the fields are available from the dedicated workshops held at NIPS in 2015 (here) and 2014 (here).


January 15, 2016: During the 41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) to be held in Shanghai, China, I will give a 1 day (March 20) tutorial on how to use sequential Monte Carlo (e.g. particle filters) methods to learn models of nonlinear dynamical systems. The title of the tutorial is Learning nonlinear dynamical models using particle filters and a brief abstract is available here. I will make the slides available before the conference. The tutorial is built around this paper,

Thomas B. Schön, Fredrik Lindsten, Johan Dahlin, Johan Wågberg, Christian A. Naesseth, Andreas Svensson and Liang Dai. Sequential Monte Carlo methods for system identification. In Proceedings of the 17th IFAC Symposium on System Identification (SYSID), Beijing, China, October 2015. [pdf] [arXiv] [Code]



December 22, 2015: Our construction of a new Gaussian process state space model enabling Bayesian inference in nonlinear dynamical systems has been accepted for publication at the 19th International Conference on Artificial Intelligence and Statistics (AISTATS) that will be held in Cadiz (Spain) in May, 2016. 

Andreas Svensson, Arno Solin, Simo Särkkä and Thomas B. Schön. Computationally efficient Bayesian learning of Gaussian process state space models. In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS), Cadiz, Spain, May, 2016. [arXiv]

December 11, 2015: The Linear Quadratic Gaussian (LQG) controller has been around for a long time and it is a very commonly used controller. Somewhat surprisingly, the statistical properties of the resulting cost function have previously not been fully sorted out. We have derived analytic expressions for the variance (and also the mean) of the LQG cost function. Besides adding to the understanding of the properties of the controller, this also opens up for new controller synthesis. The results have been accepted for Automatica.

Hildo Bijl, Jan-Willem van Wingerden, Thomas B. Schön and Michel Verhaegen. Mean and variance of the LQG cost function. Automatica, 2016.

November 19, 2015: On December 4 two PhD students that I really enjoy working with will defend their theses. The title of Niklas Wahlström’s thesis is Modelling of magnetic fields and extended targets for localisation applications and it is available here and in DiVAProfessor Simon Maskell will act as the opponent. The title of Joel Kronander’s thesis is Physically based rendering of synthetic objects in real environments and it is available here and in DiVA. The opponent for Joel’s thesis will be Greg Ward

November 6, 2015: We have two workshop papers accepted for the NIPS workshops on Black box learning and inference and Deep reinforcement learning.

Christian A. Naesseth, Fredrik Lindsten and Thomas B. Schön. Towards Automated Sequential Monte Carlo for Probabilistic Graphical Models. In Black box learning and inference workshop at the Conference on Neural Information Processing Systems (NIPS), Montréal, Canada, December 2015.

John-Alexander M. Assael, Niklas Wahlström, Thomas B. Schön and Marc Peter Deisenroth. Data-efficient learning of feedback policies from image pixels using deep dynamical models. In Deep Reinforcement Learning Workshop at the Conference on Neural Information Processing Systems (NIPS), Montréal, Canada, December 2015.

October 26, 2015: I will give my PhD course in Statistical Machine Learning again. The course starts in mid January. For more information about the course, click here. There is no formal registration required, but send me an e-mail is case you are intertested in participating in the course. Welcome!

September 11, 2015: Two new results on how to regularize nonlinear state space models and how to marginalize the hyper-parameters in Gaussian processes have been accepted for publication at the sixth IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) that will be held in Cancun, Mexico in December 2015.

Andreas Svensson, Arno Solin, Simo Särkkä and Thomas B. Schön. Nonlinear state space model identification using a regularized basis function expansion. In Proceedings of the IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Cancun, Mexico, December 2015.
Andreas Svensson, Johan Dahlin and Thomas B. Schön. Marginalizing Gaussian process hyperparameters using sequential Monte Carlo methods.  In 
Proceedings of the IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Cancun, Mexico, December 2015. [arXiv]

September 8, 2015: Our new sampling strategy to be used within existing MCMC based rendering methods (e.g. MLT and ERPT), for rendering of scenes with heterogenous participating media (such as smoke, clouds and fire) has been accepted for SIGGRAPH Asia 2015 held in Kobe (Japan) in November. Our approach is based on the pseudo-marginal approach developed for Bayesian inference in models with intractable likelihoods. Specifically, it allows us to show that any positive and unbiased estimator of the target distribution can replace the exact quantity to simulate a Markov Chain with a stationary distribution that still has the exact target distribution as a marginal. This results in significantly shorter rendering times (an example is provided in the figure showing equal time renderings).

Joel Kronander, Thomas B. Schön and Jonas Unger.  Pseudo-marginal Metropolis light transport. In Proceedings of SIGGRAPH Asia Technical Briefs, Kobe, Japan, November 2015. [pdf]

August 21, 2015: During this week a team consisting of Johan WågbergKjerstin JohanssonJohan KarlssonMohammed Al Abassi and myself participated in the Swedish study group Mathematics in Industry at Institute Mittag-Leffler. We worked on solving the problem of energy disaggregation that Greenely is facing, see also this blog post.

July 28, 2015: In 2005 we published a paper on Rao-Blackwellized particle filters (available here). Ten years later our paper sorting out the details concerning Rao-Blackwellized particle smoothing is now accepted for publication. This opens up also for offline inference in conditionally linear Gaussian models. Our construction is such that we marginalize out a conditionally tractable subset of state variables, effectively making use of sequential Monte Carlo only for the intractable (i.e. the nonlinear/non-Gaussian) part of the model.

Fredrik Lindsten, Pete Bunch, Simo Särkkä, Thomas B. Schön and Simon J. Godsill. Rao-Blackwellized particle smoothers for conditionally linear 
Gaussian models
IEEE Journal of Selected Topics in Signal Processing, 2015. (Accepted for publication) [arXiv]

June 21, 2015: The slides for tomorrow’s lectures on Nonlinear system identification using sequential Monte Carlo methods are now finished [Lecture 1][Lecture 2][Lecture 3][Lecture 4]. These lectures form a course within the Summer school on foundations and advances in stochastic filtering (FASF 2015) held here in Barcelona, Spain during June 22-25, 2015. It is an updated version of the guest lectures given in Brussels a few weeks back and it is based on this paper.

June 7, 2015: The slides for tomorrow’s lectures on Nonlinear system identification using sequential Monte Carlo methods are now finished [Lecture 1][Lecture 2][Lecture 3][Lecture 4]. These lectures are given in connection with the the Doctoral school on nonlinear system identification held in Brussels, Belgium. They are based on our recent tutorial paper on the topic, available here. In case you find this interesting I will give another edition in Barcelona, Spain on June 22, more information about how to register can be found here.

May 31, 2015: Together with Fredrik Lindsten and Bhushan Gopaluni I am organizing an invited session on the use of Sequential Monte Carlo methods for inference in nonlinear dynamical systems. It was just accepted for the 17th IFAC Symposium on System Identification (SYSID) to be held in Beijing in China, October 2015.

May 31, 2015: We have new research results accepted for the 17th IFAC Symposium on System Identification (SYSID) to be held in Beijing in China, October 2015.

Andreas Svensson, Thomas B. Schön and Manon Kok. Nonlinear state space smoothing using the conditional particle filter. In Proceedings of the 17th IFAC Symposium on System Identification  (SYSID), Beijing, China, October 2015. [pdf] [arXiv]

Johan Dahlin, Fredrik Lindsten and Thomas B. Schön, Quasi-Newton particle Metropolis-Hastings applied to intractable likelihood models. In Proceedings of the 17th IFAC Symposium on System Identification  (SYSID), Beijing, China, October 2015. [arXiv]
Niklas Wahlström, Thomas B. Schön and Marc Peter Deisenroth. Learning deep dynamical models from image pixels. In Proceedings of the 17th IFAC Symposium on System Identification  (SYSID), Beijing, China, October 2015.
Hildo Bijl, Jan-Willem van Wingerden, Thomas B. Schön and Michel Verhaegen. 
Online sparse Gaussian process regression using FITC and PITC approximations. In Proceedings of the 17th IFAC Symposium on System Identification  (SYSID), Beijing, China, October 2015. [pdf]
Jerker Nordh, Torbjörn Wigren, Thomas B. Schön and Bo Bernhardsson. 
Particle filtering based identification for autonomous nonlinear ODE models. In Proceedings of the 17th IFAC Symposium on System Identification  (SYSID), Beijing, China, October 2015. [pdf]
Manon Kok, Johan Dahlin, Thomas B. Schön and Adrian Wills. 
Newton-based maximum likelihood estimation in nonlinear state space models. In Proceedings of the 17th IFAC Symposium on System Identification  (SYSID), Beijing, China, October 2015. [arXiv]
Johan Wågberg, Fredrik Lindsten and Thomas B. Schön. Bayesian nonparametric identification of piecewise affine ARX systems. In Proceedings of the 17th IFAC Symposium on System Identification  (SYSID), Beijing, China, October 2015.
Jack Umenberger, Johan Wågberg, Ian R. Manchester and Thomas B. Schön. 
On identification via EM with latent disturbances and Lagrangian relaxation. In Proceedings of the 17th IFAC Symposium on System Identification  (SYSID), Beijing, China, October 2015.
Thomas B. Schön, Fredrik Lindsten, Johan Dahlin, Johan Wågberg, Christian A. Naesseth, Andreas Svensson and Liang Dai. 
Sequential Monte Carlo methods for system identification. In Proceedings of the 17th IFAC Symposium on System Identification (SYSID), Beijing, China, October 2015.

May 12, 2015: Data-efficient learning in continuous state-action spaces using very high-dimensional observations remains a key challenge in developing fully autonomous systems. In some recent results that was just accepted to the deep learning workshop at ICML this summer we we consider one instance of this challenge, the so-called pixels-to-torques problem, where an agent must learn a closed-loop control policy from pixel information only. We introduce a data-efficient, model-based reinforcement learning algorithm that learns such a closed-loop policy directly from pixel information.

Niklas Wahlström, Thomas B. Schön and Marc Peter Deisenroth. From pixels to torques: policy learning with deep dynamical models. Deep Learning Workshop at the International Conference on Machine Learning (ICML), Lille, France, July 2015. (accepted for publication) [arXiv]

April 27, 2015: Our new sequential Monte Carlo (SMC) construction enabling inference in high(er) dimensional models has been accepted to the International Conference on Machine Learning (ICML), which will be held in Lille (France) in July. The new construction mimics fully adapted proposals for latent spaces and structures in high dimensional models. The key is a nested coupling of multiple SMC samplers and backward simulators.

Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön. Nested sequential Monte Carlo methods. In Proceedings of the 32nd International Conference on Machine Learning (ICML), Lille, France, July, 2015. (accepted for publication) [pdf] [arXiv]

April 10, 2015: Our tutorial paper Sequential Monte Carlo Methods for System Identification is now available as a pre-print on arXiv. In this paper we describe different approaches (both Bayesian and frequentist) to estimate parameters in general state space models, also know as hidden Markov models. The SMC approach is motivated by its ability to deal with the intractability of nonlinear and non-Gaussian state space models.

March 19, 2015: The cars project has relocated to SciFest for three days (Today - Saturday). Check it out! SciFest is a science festival here in Uppsala, click here to find out more about it.

January 29, 2015: The details concerning the one-day tutorial that I will give on nonlinear system identification using sequential Monte Carlo (SMC) methods are now available. The tutorial is given as a part of the Summer School on Foundations and advances in stochastic filtering, which will be held in Barcelona, June 22-26, 2015.

January 26, 2015: The sequential Monte Carlo (SMC) workshop will be held in Paris in August. More information is available from here, where you can also register.


January 23, 2015: The website for the Uppsala CARS (Camera-based Autonomous Racing System) project is now available here! If you are student here in Uppsala interested in working on this project, let us know since we are currently in the process of assembling a new team. Some ideas for future projects are available here, but own ideas are of course also welcome. 


December 29, 2014: Over the last 5 years we have been working on the problem of combining information (sensor fusion) from inertial sensors (accelerometers and gyroscopes) and ultrawideband (some early results are available here and here). Our new work provides a general solution based on a formulation of the problem as an optimization problem. The present application is indoor positioning. This work has now been accepted for publication in the IEEE Transactions on Vehicular technology (special section on Indoor localization, tracking, and mapping with heterogeneous technologies)

Manon Kok, Jeroen D. Hol and Thomas B. Schön. Indoor positioning using ultrawideband and inertial measurementsIEEE Transactions on Vehicular Technology (special section on Indoor localization, tracking, and mapping with heterogeneous technologies), 2015. (accepted for publication) [pdf]

December 27, 2014: Kaczmarz's algorithm is an iterative method for solving linear systems of equations published by the Polish mathematician Stefan Kaczmarz back in 1937, here is an English translation of the original paper (Angenäherte Auflösung von Systemen linearer Gleichungen). We have studied the exponentional convergence of Kaczmarz's algorithm and we provide upper bounds of its convergence rate. The analysis is inspired by proofs of stability for linear time-varying dynamical systems from the control community. The work has been provisionally accepted for publication in the IEEE Signal Processing Letters. There is still room to find tighter upper bounds.

Liang Dai and Thomas B. Schön. On the exponential convergence of the Kaczmarz algorithm. IEEE Signal Processing Letters, 2015.

December 17, 2014: We have been invited to present our optimization-based solution to the human body motion capture problem at the conference on Technically Assisted Rehabilitation (TAR 2015) held in Berlin, Germany in March 2015.

Manon Kok, Jeroen Hol and Thomas B. Schön. An optimization-based approach to human body motion capture using inertial sensors. Conference on Technically Assisted Rehabilitation (TAR), Berlin, Germany, March, 2015. (invited paper). A more complete description of this work is available here.

December 8, 2014: In june next year I will teach a module on nonlinear system identification using sequential Monte Carlo during the Summer school on stochastic filtering to be held in Barcelona, Spain.

November 12, 2014: Manon Kok will do her pre-doc together with Simo Särkkä at Aalto University in Finland during the time period January - March 2015.

September 22, 2014: Our new derivation of recursive direct weight optimization has been accepted for publication in IEEE TAC
Liang Dai and Thomas B. Schön. A new structure exploiting derivation of recursive direct weight optimizationIEEE Transactions on Automatic Control, 2014. (accepted for publication)

September 9, 2014: Our paper on inference in general probabilistic graphical models has been accepted for publication at NIPS 2014. Our method is consistent and it provides unbiased estimates of the partition function. I gave a seminar on the idea at the Isaac Newton Institute for Mathematical Sciences in Cambridge earlier this year, the talk is available here.

Christian A. Naesseth, Fredrik Lindsten and Thomas B. Schön. Sequential Monte Carlo methods for graphical modelsAdvances in Neural Information Processing Systems (NIPS) 27, Montreal, Quebec, Canada, December, 2014. [pdf] [arXiv] [code] [video]

September 8, 2014: Johan Dahlin just started his pre-doc at the University of New South Wales (UNSW) in Sydney, Australia. He hosts are Robert Kohn at the Australian School of Business and Pierre Del Moral at the School of Mathematics and Statistics.

September 4, 2014: I am very glad to announce that Tobias Rydén will join us as an Adjunct Professor. Tobias main employer is Lynx Asset Management and he has perviously held Professor positions (mathematical statistics) at Lund University and KTH.

August 29, 2014: Received the Automatica best paper award for papers published in Automatica 2011-2013, link. Awarded for the paper; Thomas B. Schön, Adrian Wills and Brett Ninness. System identification of nonlinear state-space models. Automatica, 47(1):39-49, January 2011. [pdf] The award was handed out at the 18th World Congress of the International Federation of Automatic Control (IFAC), Cape Town, South Africa, August, 2014, where we also received a honorable mention (nominated for the best application paper award) with the paper Manon Kok, Jeroen D. Hol and Thomas B. Schön. An optimization-based approach to human body motion capture using inertial sensors. [pdf]

August 28, 2014: Our new results on Bayesian parameter inference in nonlinear dynamical systems have just been accepted for publication in Statistics and Computing. We introduce two alternative versions of the Particle Metropolis Hastings (PMH) algorithm that incorporate gradient and Hessian information about the posterior into the proposal. In the paper we show how to estimate the required information using a fixed-lag particle smoother.

Johan Dahlin, Fredrik Lindsten and Thomas B. Schön. Particle Metropolis Hastings using gradient and Hessian information. Statistics and Computing, 2014. (accepted for publication) [pdf] [arXiv] [code]

August 22, 2014: Christian Andersson Naesseth won the best poster award at the Summer school on deep learning for image analysis (held in Copenhagen, Denmark). The poster is available here and the papers describing the work in detail are available here and here.


August 18, 2014: During the summer a team of students have been working on realizing the CARS project, which is an acronym for Camera-based Autonomous Racing System. You can have a look at their result in this video. It includes a camera based target tracking system, controllers and of course the practical implemention of it all.

July 30, 2014:  Recently we had two papers accepted. The first paper presents a maximum likelihood estimator for jump Markov linear models based on a combination of the particle filter and Markov chain Monte Carlo methods. In the second paper we derive a new Sequential Monte Carlo-based algorithm to estimate the capacity of two-dimensional channel models, yielding more than an order of magnitude improvement in estimation accuracy compared to existing methods. The underlying method is explained in  a more general context in this video and in this arXiv paper.

Andreas Svensson, Thomas B. Schön and Fredrik Lindsten. Identification of jump Markov linear models using particle filters. IProceedings of the 53rd IEEE Conference on Decision and Control (CDC), Los Angeles, CA, USA, December, 2014. (accepted for publication) [pdf] [code]
Christian A. Naesseth, Fredrik Lindsten and Thomas B. Schön. Capacity estimation of two-dimensional channels using Sequential Monte CarloIn Proceedings of the IEEE Information Theory Workshop (ITW), Hobart, Tasmania, Australia, November, 2014. (accepted for publication) [pdf] [arXiv]

May 28, 2014: Our recent explorations on the use of Monte Carlo methods (including sequential Monte Carlo) for real-time video based lightning has been accepted for publication at the 22nd European Signal Processing Conference (EUSIPCO) held in Lisabon, Portugal in September. This is very much introductory work for us and more will follow.

Joel Kronander, Johan Dahlin, Daniel Jönsson, Manon Kok, Thomas B. Schön and Jonas Unger. Real-time video based lighting using GPU raytracing, in Proceedings of the 22nd European Signal Processing Conference (EUSIPCO), Lisabon, Portugal, September 2014. (accepted for publication) [pdf]

May 21, 2014: We (Nicolas ChopinAdam Johansen and myself) are organizing a workshop on sequential Monte Carlo methods at ENSAE in Paris during August 26-28 2015. More information is available here. Hope to see you there!

May 9, 2014: On June 5, Manon Kok will defend her licentiate thesis entitled Probabilistic modeling for positioning applications using inertial sensors [pdf]. The discussion leader will be Dr. Gabriele Bleser from the Augmented Vision group at the German Research Center for Artificial Intelligence. The thesis contains (among other things); A new optimization-based formulation of human motion capture (finding the position and orientation of human bodies) using inertial sensors and a new algorithm to calibrate magnetometers in the presence of metallic objects. The thesis is available here.

May 5, 2014: On May 28, Johan Dahlin will defend his licentiate thesis entitled Sequential Monte Carlo for inference in nonlinear state space models [pdf]. The discussion leader will be Dr. Adam Johansen from the Department of Statistics at the University of Warwick. The thesis contains (among other things): new methods for learning parameters in nonlinear dynamical models and it also studies the input design problem for nonlinear dynamical systems with some new insights. The thesis is available here.

April 24, 2014: We have two new papers accepted for the 2014 IEEE Workshop on Statistical Signal Processing (SSP) to be held at Jupiters on the Gold Coast in Australia in July. The first result is a new marginal particle smoother that is based on running a sequential Monte Carlo sampler backward in time after an initial forward filtering pass. In the second paper we make use of mixture importance sampling ideas to derive robust and efficient particle filters.

Joel Kronander and Thomas B. Schön. Robust auxiliary particle filters using multiple importance samplingProceeding of the IEEE Statistical Signal Processing Workshop (SSP), Gold Coast, Australia, July 2014. (accepted for publication). [pdf]

Joel Kronander, Thomas B. Schön and Johan Dahlin. Backward sequential Monte Carlo for marginal smoothingProceeding of the IEEE Statistical Signal Processing Workshop (SSP), Gold Coast, Australia, July 2014. (accepted for publication). [pdf]

March 25, 2014: We have recently developed a new algorithm for inference in latent variable models, state space models (both Markovian and non-Markovian) being one important special case. The algorithm belongs to the family of particle MCMC (PMCMC) algorithms. More specifically our algorithms builds on the Particle Gibbs sampler of Christophe Andrieu, Arnaud Doucet and Roman Holenstein available here. The work has been accepted for publication in the Journal of Machine Learning Research (JMLR)

Fredrik Lindsten, Michael I. Jordan and Thomas B. Schön. Particle Gibbs with ancestor sampling. Journal of Machine Learning Research (JMLR), 2014. (accepted for publication) [pdf] [arXiv]

March 21, 2014: The work carried out by Daniel Hultqvist during his Master’s thesis at Autoliv  has been accepted for publication at the IEEE Intelligent Vehicles Symposium (IV). The contribution is a new algorithms for detection and positioning an overtaking vehicle using 1D optical flow. 

Daniel Hultqvist, Jacob Roll, Fredrik Svensson, Johan Dahlin and Thomas B. Schön. Detecting and positioning of overtaking vehicles using 1D optical flow. in Proceedings of the IEEE Intelligent Vehicles (IV) Symposium, Dearborn, MI, USA, June 2014 (accepted for publication). [pdf]

February 13, 2014: We just had some new results accepted for publication at the 19th IFAC World Congress (Cape Town, South Africa, August 2014). Very briefly, the results are; an empirical Bayes method for our previous GP-SSM construction, maximum likelihood calibration of magnetometers using inertial sensors, inertial human motion capture, the use of second-order information in PMCMC for Bayesian system identification, input design in nonlinear systems and an anomaly detection model. 

  • Roger Frigola, Fredrik Lindsten, Thomas B. Schön and Carl E. Rasmussen. Identification of Gaussian Process state-space models with particle stochastic approximation EM. [pdf]
  • Manon Kok and Thomas B. Schön. Maximum likelihood calibration of a magnetometer using inertial sensors. [pdf]
  • Manon Kok, Jeroen D. Hol and Thomas B. Schön. An optimization-based approach to human body motion capture using inertial sensors. [pdf]
  • Johan Dahlin, Fredrik Lindsten and Thomas B. Schön. Second-order Particle MCMC for Bayesian parameter inference. [pdf]
  • Patricio E. Valenzuela, Johan Dahlin, Cristian R. Rojas and Thomas B. Schön. A graph/particle-based method for experiment design in nonlinear systems. [pdf]
  • André Carvalho Bittencourt and Thomas B. Schön. Data-driven anomaly detection based on a bias chenge model. [pdf]

January 6, 2014: We have just finished the full paper on the ancestor sampling version of Particle Gibbs. It contains significant original developments compared to the NIPS paper and the presentation is hopefully a bit more pedagogical.

Fredrik Lindsten, Michael I. Jordan and Thomas B. Schön. Particle Gibbs with ancestor sampling. Preprint at arXiv:1401.0604, January, 2014. [pdf]



December 6, 2013: If you are interested in a brief exposition of the use of sequential Monte Carlo methods (e.g. particle filters and particle smoothers) for nonlinear system identification you can have a look at this book chapter,

Thomas B. Schön. Nonlinear system identification using particle filters. Encyclopedia of Systems and Control (Eds. T. Samad and J. Baillieul) Springer, 2014. [pdf] [Springer]


November 21, 2013: Last Friday I was formally installed as Professor at Uppsala University. During the installation I delivered a popular scientific lecture on my research, available here (in Swedish).

November 8, 2013: After Christmas I will offer two PhD courses. In January I will give the course Computational Learning in dynamical systems at Universidad Tecnica Federico Santa Maria (UTFSM) in Valparaiso, Chile, the course flyer is available here. Back in Uppsala I will offer a course on Machine Learning, starting in February. Send me an e-mail if you are interested in participating in any of these courses. Welcome!

November 5, 2013: I have been awarded a grant from the Swedish Research council (VR). The project name is Probabilistic modeling of dynamical systems. It is a 4 year project worth SEK 3 880 000.

September 5, 2013: We have been able to construct and learn a Gaussian process (GP) state space model while keeping alive the entire nonparametric richness of this kind of model. We believe that it is the first time that this has been achieved. The key allowing us to do this is a new PMCMC method known as Particle Gibbs with Ancestor Sampling (PG-AS). We have a feeling that these inference methods are useful also for other Bayesian nonparametric models. The new results will be published at NIPS in December.

Roger Frigola, Fredrik Lindsten, Thomas B. Schön and Carl E. Rasmussen. Bayesian inference and learning in Gaussian process state-space models with particle MCMCAdvances in Neural Information Processing Systems (NIPS) 26, Lake Tahoe, NV, USA, December, 2013 (accepted for publication). [pdf] [supplemental]

September 2, 2013: Starting my new position as Professor of the Chair of Automatic Control at Uppsala University. I will continue to be affiliated with Linköping University.

August 11, 2013: On October 25, 2013, 10.15, Fredrik Lindsten will defend his PhD thesis. Arnaud Doucet (Professor of Statistics at Oxford University, UK) will serve as faculty opponent. The grading committee consists of Prof. Tobias Rydén (Lynx Asset Management), Doc.  Joakim Jaldén (KTH) and Prof. Mattias Villani (Linköping University). The thesis is entitled Particle filters and Markov chains for learning of dynamical systems [pdf].

June 17, 2013: Our new tutorial on particle smoothing methods is now accepted for publication in Foundations and Trends in Machine Learning,


[J16] Fredrik Lindsten and Thomas B. Schön. Backward simulation methods for Monte Carlo statistical inferenceFoundations and Trends in Machine Learning, 2013. [pdf] [now]

This paper is written with the intent to be a self-contained introduction to particle smoothing in a general setting, not just confined to standard nonlinear state space models. More precisely, we discuss the use of forward filtering/backward simulation techniques, addressing both smoothing and parameter inference problems.

May 28, 2013: Johan Kihlberg and Simon Tegelid have been awarded the best Master's thesis award 2012 by Dataföreningen - öst. The thesis is entitled "Map Aided Indoor Positioning" and it is available for download by clicking here.

May 22, 2013: I have been awarded the Best PhD thesis award 2013 by The European Association for Signal Processing (EURASIP). "The selection process is based on the evaluation of the impact of the theses, their subsequent journal and conference publications and related citations received, and on the review reports of three independent reviewers as well as on the download statistics." The thesis entitled "Estimation of Nonlinear Dynamic Systems: Theory and Applications" is available here and the announcement is available here.

April 22, 2013: I am very glad to welcome Roger Frigola, who is visiting our team for a couple of months. Roger is a PhD student with the Machine Learning group at the University of Cambridge (UK).

March 7, 2013: Our new method for Wiener system identification has been accepted for publication in Automatica. The method avoids making strong assumptions, such as monotonicity, on the nonlinear mapping. Stochastic disturbances, entering both as measurement noise and as process noise, are also straightforwardly handled. The inference algorithm is based on an efficient particle Markov chain Monte Carlo (PMCMC) method, referred to as particle Gibbs with ancestor sampling (PG-AS).

[J15] Fredrik Lindsten, Thomas B. Schön and Michael I. Jordan. Bayesian semiparametric Wiener system identificationAutomatica 2013. [pdf]

March 1, 2013: We have some new results on the following topics; particle smoothing, PMCMC, modeling of magnetic disturbances and localization using magnetic maps. The underlying papers have been accepted for publication at the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP) to be held in Vancouver, Canada during May 26-31, 2013.

[C64] Fredrik Lindsten, Pete Bunch, Simon J. Godsill and Thomas B. Schön. Rao-Blackwellized particle smoothers for mixed linear/nonlinear state-space models, in Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, May 2013. [pdf]

[C63] Manon Kok, Niklas Wahlström, Thomas B. Schön and Fredrik Gustafsson. MEMS-based inertial navigation based on a magnetic field map, in Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, May 2013. [pdf]

[C62] Niklas Wahlström, Manon Kok, Thomas B. Schön and Fredrik Gustafsson. Modeling magnetic fields using Gaussian processes, in Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, May 2013. [pdf]

[C61] Johan Dahlin, Fredrik Lindsten and Thomas B. Schön. Particle Metropolis Hastings using Langevin dynamics, in Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, May 2013. [pdf] [code]

[C60] Ehsan Taghavi, Fredrik Lindsten, Lennart Svensson and Thomas B. Schön. Adaptive stopping for fast particle smoothing, in Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, May 2013. [pdf]

January 22, 2013: In an attempt to provide a quick and easily accessible introduction to my research I have written an overview of my research activities, which is available here.


November 28, 2012: During the period January - March 2013 I am offering my PhD course in Machine Learning. More information about the course is available here. Let me know if you are interested in participating. As usual participants from industry are also welcome.

October 8, 2012: I am offering two editions of my new PhD course Computational Inference in Dynamical Systems. During October 17-18 it will be given at the University of Sydney and during November 19-23 it will be give at the Royal Institute of Technology. If you are interested in following the course you are most welcome!

September 28, 2012: On November 9, 2012, 10.15, Karl Granström will defend his PhD thesis. Prof. Ba-Ngu Vo will serve as faculty opponent. The thesis is entitled Extended target tracking using PHD filters [pdf].

September 4, 2012: We have developed a new particle MCMC method, that we refer to as particle Gibbs with ancestor sampling (PG-AS). Similarly to the existing PG with backward simulation (PG-BS) procedure, we use backward sampling to (considerably) improve the mixing of the PG kernel. Instead of using separate forward and backward sweeps as in PG-BS, we achieve the same effect in a single forward sweep. This result has been accepted for publication at NIPS this year,

[C59] Fredrik Lindsten, Michael I. Jordan and Thomas B. Schön, Ancestor sampling for particle Gibbs, in Advances in Neural Information Processing Systems (NIPS) 25, 2012. [pdf] [supplemental] [NIPS]

August 14, 2012: The final program for our workshop on Moving Horizon Estimation and System Identification to be held in Leuven, Belgium, August 29-30 is now available. Click here for details.

July 8, 2012: Our new maximum likelihood based method for identification of Hammerstein-Wiener model structures is capable of handling a very general situation, wherein multivariable data, non-invertible Hammerstein and Wiener nonlinearities, and colored stochastic disturbances both before and after the Wiener nonlinearity are all catered for. This new result has been accepted for publication in Automatica.

[J14] Adrian Wills, Thomas B. Schön, Lennart Ljung and Brett Ninness. Identification of Hammerstein-Wiener modelsAutomatica, 49(1): 70-81, January 2013. [pdf] [Automatica]

Older news are available here.

 © Thomas Schön 2018