Simo Särkkä

Associate Professor in Sensor informatics and medical technology at Department of Electrical Engineering and Automation (EEA), Aalto University
Docent, Adjunct Professor (both at TUT and LUT)
Coordinator of AI for Health in Finnish Center for Artificial Intelligence (FCAI)
Associate Editor of IEEE Signal Processing Letters

Postal Address:

P.O.Box 12200
FIN-00076 AALTO
FINLAND

Street Address:

Room F305, F-Talo, 3rd Floor
Rakentajanaukio 2c
Espoo, Finland

Contact:

Mobile: +358 50 512 4393
Email: simo.sarkka@aalto.fi
Skype: simosarkka
Web: users.aalto.fi/~ssarkka
Twitter: @simosarkka

Current post-docs

Dr. Ali Bahrami Rad (post-doc)
Dr. Matti Raitoharju (part-time post-doc)
Dr. Zenith Purisha (post-doc)
Dr. Muhammad Fuady Emzir

Current doctoral (PhD) students / visitors

Juha Sarmavuori (Nokia Ltd.)
Toni Karvonen (Aalto University)
Filip Tronarp (Aalto University)
Zheng Zhao (Aalto University)
Rui Gao (Aalto University)
Joel Jaskari (Aalto University)
Kimmo Suotsalo (RemoteA)
Harshit Agrawal (Planmeca)

Former doctoral students (Drs. now)

Ola Rinta-Koski (Aalto University)
Arno Solin (IndoorAtlas Ltd.)
Juho Kokkala (Kone Oy)
Isambi S. Mbalawata (University of Dar Es Salaam, Tanzania)
Jouni Hartikainen (Rocsole Ltd.)

Biography

Current/previous positions:

  • 2015- Associate Professor, EEA, Aalto University
  • 2013-2018 Academy Research Fellow, NBE/BECS & EEA, Aalto University
  • 2013- Technical advisor of IndoorAtlas Ltd.
  • 2012- Docent (Adj.Prof.), Lappeenranta University of Technology
  • 2011- Docent (Adj.Prof.), Tampere University of Technology
  • 2007- Independent consultant
  • 2014 (May) Visiting Scholar, Chalmers University of Technology
  • 2013 (Oct-Dec) Visiting Professor, University of Oxford
  • 2011 (Apr-Dec) Visiting Scholar, University of Cambridge
  • 2010-2013 Senior Researcher, Aalto University
  • 2007-2009 Staff Scientist, Nalco / Nalco-Mobotec
  • 2002-2007 R&D Manager, Indagon Ltd.
  • 2000-2002 Research Engineer, Nokia Ltd.

Simo Särkkä received his Master of Science (Tech.) degree in engineering physics and mathematics, and Doctor of Science (Tech.) degree in electrical and communications engineering from Helsinki University of Technology, Espoo, Finland, in 2000 and 2006, respectively. Currently, he is an Associate Professor with Aalto University, Technical Advisor of IndoorAtlas Ltd., and an Adjunct Professor with Tampere University and LUT University. He is also a Fellow of European Laboratory for Learning and Intelligent Systems (ELLIS) as well as visiting researcher with Alan Turing Institute (ATI), and he is affiliated with Finnish Center for Artificial Intelligence (FCAI). From 2000 to 2010 he worked with Nokia Ltd., Indagon Ltd., and Nalco Company in various industrial positions related to telecommunications, positioning systems, and industrial process control. From 2010 to 2013 he worked as a Senior Researcher with the Department of Biomedical Engineering and Computational Science (BECS) at Aalto University, Finland, and also held the position of Academy Research Fellow for 2013-2018.

His and his group's research interests are in multi-sensor data processing systems with applications in location sensing, health and medical technology, machine learning, inverse problems, and brain imaging. He has authored or coauthored over 100 peer-reviewed scientific articles and his books "Bayesian Filtering and Smoothing" and "Applied Stochastic Differential Equations" along with the Chinese translation of the former were recently published via the Cambridge University Press. He is a Senior Member of IEEE and serving as a Senior Area Editor of IEEE Signal Processing Letters.


Research Activities

  • Applications

    • Embedded systems in health technology
    • Signal processing and state estimation in brain imaging (fMRI/MEG/EEG/DOT)
    • Spatio-temporal modeling in machine learning, inverse problems, and Kriging.
    • Location sensing, target tracking, audio signal processing.
    • Applications in medicine, biology, RF/RFID systems, telematics, optical/video tracking, inertial navigation, robotics, audio systems, etc.
  • Bayesian Inference Methods for Stochastic Dynamic Systems

    • Non-Linear Kalman/Bayesian filtering and smoothing
    • Continuous-time stochastic models and stochastic differential equations (SDEs)
    • Particle filtering and sequential Monte Carlo methods
  • Bayesian Inference Methods for Spatial and Spatio-Temporal Systems

    • State-space, sparse, and reduced rank methods in Gaussian process regression.
    • Stochastic partial/pseudo differential equations (SPDE).
    • Infinite-dimensional/distributed-parameter Kalman filtering and smoothing.
  • Theoretical Analysis and Other Methodology

    • Convergence and stability analysis of approximate Bayesian filters and smoothers
    • Theoretical analysis of Gaussian process regressors
    • Advanced Markov chain Monte Carlo methods

Publications

My Google Scholar profile: http://scholar.google.com/citations?user=QVhmc9cAAAAJ

See also Aalto People page

The PDF preprints below are draft versions of the articles and they are here to give people an opportunity to check the relevance of the articles before purchasing the final articles from the publisher. Please send me an email if you want the latest preprints of the submitted articles.

Books

  1. Simo Särkkä and Arno Solin (2019). Applied Stochastic Differential Equations. Cambridge University Press. Available from Cambridge University Press. The associated MATLAB/Octave codes are available for download as well as in GitHub although they are also available in the Resources tab on the CUP book web page.

    With permission from the publisher, we are providing a PDF version of the book here:

    This PDF version is made available for personal use. The copyright in all material rests with the authors (Simo Särkkä and Arno Solin). Commercial reproduction is prohibited, except as authorised by the author and publisher.

  2. Simo Särkkä (2013). Bayesian Filtering and Smoothing. Cambridge University Press. Available from Cambridge University Press at http://www.cambridge.org/sarkka, from this CUP-link, or from, e.g., Amazon UK or Amazon USA.

    Although the more convenient (and quite affordable) printed version of the book can be purchased from the above sources, with permission from the publisher, I am providing a PDF version of the book here:

    This PDF version is made available for personal use. The copyright in all material rests with the author (Simo Särkkä). Commercial reproduction is prohibited, except as authorised by the author and publisher.

  3. 希莫•萨日伽 (2015). 贝叶斯滤波与平滑. 国防工业出版社. Chinese translation of "Bayesian filtering and smoothing". Available, e.g., from Amazon China.

Journal Articles

  1. Toni Karvonen, Motonobu Kanagawa, Simo Särkkä. On the positivity and magnitudes of Bayesian quadrature weights. Accepted for publication in Statistics and Computing. (arXiv)
  2. Rui Gao, Filip Tronarp, and Simo Särkkä. Iterated Extended Kalman Smoother-based Variable Splitting for L1-Regularized State Estimation. Accepted for publication in IEEE Transactions on Signal Processing. (arXiv)
  3. A. Solin and S. Särkkä. Hilbert Space Methods for Reduced-Rank Gaussian Process Regression. Accepted for publication in Statistics and Computing. (arXiv)
  4. Zenith Purisha, Carl Jidling, Niklas Wahlström, Simo Särkkä, Thomas B. Schön. Probabilistic approach to limited-data computed tomography reconstruction. Accepted for publication in Inverse Problems. (arXiv)
  5. Toni Karvonen and Simo Särkkä. Gaussian kernel quadrature at scaled Gauss–Hermite nodes. Accepted for publication in BIT Numerical Mathematics (arXiv)
  6. Filip Tronarp, Hans Kersting, Simo Särkkä, Philipp Hennig. Probabilistic Solutions To Ordinary Differential Equations As Non-Linear Bayesian Filtering: A New Perspective. Accepted for publication in Statistics and Computing. (arXiv)
  7. Ángel F. García-Fernández, Filip Tronarp, Simo Särkkä. Gaussian target tracking with direction-of-arrival von Mises-Fisher measurements. Accepted for publication in IEEE Transactions on Signal Processing.
  8. Ángel F. García-Fernández, Filip Tronarp, and Simo Särkkä. Gaussian process classification using posterior linearisation. Accepted for publication in IEEE Signal Processing Letters. (arXiv)
  9. Ángel F. García-Fernández, Roland Hostettler, Simo Särkkä. Rao-Blackwellised posterior linearisation backward SLAM. Accepted for publication in IEEE Transactions on Vehicular Technology. (Preprint)
  10. Hüseyin Yiğitler, Ossi Kaltiokallio, Roland Hostettler, Riku Jäntti, Neal Patwari, Simo Särkkä. RSS Models for Respiration Rate Monitoring. Accepted for publication in IEEE Transactions on Mobile Computing. (arXiv)
  11. Toni Karvonen, Simo Särkkä and Chris J. Oates. Symmetry exploits for Bayesian cubature methods. Accepted for publication in Statistics and Computing. (arXiv)
  12. Juha Sarmavuori and Simo Särkkä. Numerical Integration as a Finite Matrix Approximation to Multiplication Operator. Accepted for publication in Journal of Computational and Applied Mathematics. (arXiv)
  13. Filip Tronarp, Toni Karvonen, and Simo Särkkä. Student’s t-Filters for Noise Scale Estimation Accepted for publication in IEEE Signal Processing Letters. (Preprint)
  14. Filip Tronarp and Simo Särkkä (2019). Iterative Statistical Linear Regression for Gaussian Smoothing in Continuous-Time Non-linear Stochastic Dynamic Systems. Signal Processing, Volume 159, Pages 1-12. (DOI, arXiv)
  15. Simo Särkkä, Mauricio A. Álvarez, Neil D. Lawrence (2019). Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems. IEEE Transactions on Automatic Control, Volume 64, Issue 7, Pages 2953-2960. (arXiv, DOI)
  16. Roland Hostettler and Simo Särkkä (2019). Rao–Blackwellized Gaussian Smoothing. IEEE Transactions on Automatic Control, Volume 64, Issue 1, Pages 305-312. (DOI, Preprint)
  17. Michael Schober, Simo Särkkä, and Philipp Hennig (2019). A probabilistic model for the numerical solution of initial value problems. Statistics and Computing, Volume 29, Issue 1, Pages 99-122. (Open Access Link, arXiv)
  18. Arno Solin, Manon Kok, Niklas Wahlström, Thomas B. Schön, Simo Särkkä (2018). Modeling and interpolation of the ambient magnetic field by Gaussian processes. IEEE Transactions on Robotics, Volume 34, Issue 4, Pages 1112-1127 (DOI, arXiv)
  19. Filip Tronarp, Ángel F. García-Fernández, and Simo Särkkä (2018). Iterative Filtering and Smoothing In Non-Linear and Non-Gaussian Systems Using Conditional Moments. IEEE Signal Processing Letters, Volume 25, Issue 3, Pages 408-412 (DOI, Preprint).
  20. Toni Karvonen and Simo Särkkä (2018). Fully symmetric kernel quadrature. SIAM Journal on Scientific Computing, 40(2), A697–A720. (DOI, arXiv)
  21. Soheil Sadat Hosseini, Mohsin M. Jamali, and Simo Särkkä (2018). Variational Bayesian adaptation of noise covariances in multiple target tracking problems. Measurement, Volume 122, Pages 14-19. (DOI))
  22. Olli-Pekka Rinta-Koski, Simo Särkkä, Jaakko Hollmén, Markus Leskinen, Sture Andersson (2018). Gaussian process classification for prediction of in-hospital mortality among preterm infants. Neurocomputing, Volume 298, Pages 134-141. (DOI, Preprint)
  23. Ángel F. García-Fernández, Lennart Svensson, Simo Särkkä (2018). Cooperative localisation using posterior linearisation belief propagation. IEEE Transactions on Vehicular Technology, Volume 67, Number 1, Pages 832-836. (DOI)
  24. Lassi Roininen, Sari Lasanen, Mikko Orispää,and Simo Särkkä (2018). Sparse Approximations of Fractional Matern Fields. Scandinavian Journal of Statistics., Volume 45, Issue 1, Pages 194-216 (DOI, arXiv)
  25. Ángel F. García-Fernández, Lennart Svensson, Simo Särkkä (2017). Iterated posterior linearisation smoother. IEEE Transactions on Automatic Control, Volume 62, Issue 4. (DOI, Preprint).
  26. Patrick R. Conrad, Mark Girolami, Simo Särkkä, Andrew Stuart, Konstantinos Zygalakis (2017). Statistical analysis of differential equations: introducing probability measures on numerical solutions. Statistics and Computing, Volume 27, Issue 4, pages 1065–1082. (DOI)
  27. Juho Kokkala, Arno Solin, Simo Särkkä (2016). Sigma-Point Filtering and Smoothing Based Parameter Estimation in Nonlinear Dynamic Systems. Journal of Advances in Information Fusion, 11(1), 15-30. (arXiv)
  28. Simo Särkkä, Jouni Hartikainen, Lennart Svensson, Fredrik Sandblom (2016). On the relation between Gaussian process quadratures and sigma-point methods. Journal of Advances in Information Fusion, 11(1), 31-46. (arXiv)
  29. Fredrik Lindsten, Pete Bunch, Simo Särkkä, Thomas Schön, Simon Godsill (2016). Rao-Blackwellized particle smoothers for conditionally linear Gaussian models. IEEE Journal of Selected Topics in Signal Processing, Volume 10, Number 2, Pages 353-365. (arXiv)
  30. I. S. Mbalawata and S. Särkkä. (2016) Moment Conditions for Convergence of Particle Filters with Unbounded Importance Weights. Signal Processing, Volume 116, Pages 133-138. (arXiv)
  31. Ángel F. García-Fernández, Lennart Svensson, Mark R. Morelande, Simo Särkkä (2015). Posterior linearisation filter: principles and implementation using sigma points. IEEE Transactions on Signal Processing, Volume 63, Number 20, Pages 5561-5573 (DOI, Preprint)
  32. Xi Chen, Simo Särkkä, Simon Godsill (2015). A Bayesian Particle Filtering Method For Brain Source Localisation. Digital Signal Processing, Volume 47, Pages 192-204. (arXiv)
  33. Sean Anderson, Timothy D. Barfoot, Chi Hay Tong, and Simo Särkkä (2015). Batch Nonlinear Continuous-Time Trajectory Estimation as Exactly Sparse Gaussian Process Regression. Autonomous Robots, Volume 39, Issue 3, Page 221-238. (arXiv)
  34. Juho Kokkala and Simo Särkkä (2015). Combining Particle MCMC with Rao-Blackwellized Monte Carlo Data Association for Parameter Estimation in Multiple Target Tracking. Digital Signal Processing, Volume 47, Pages 84-95, (arXiv)
  35. J. Ala-Luhtala, S. Särkkä, and R. Piché (2015). Gaussian filtering and variational approximations for Bayesian smoothing in continuous-discrete stochastic dynamic systems. Signal Processing, Volume 111, Pages 124-136. (DOI, arXiv)
  36. S. Särkkä, J. Hartikainen, I. S. Mbalawata, H. Haario (2015). Posterior Inference on Parameters of Stochastic Differential Equations via Non-Linear Gaussian Filtering and Adaptive MCMC. Statistics and Computing, Volume 25, Issue 2, Pages 427-437. (DOI, Preprint)
  37. I. S. Mbalawata, S. Särkkä, M. Vihola, H. Haario (2015). Adaptive Metropolis Algorithm Using Variational Bayesian Adaptive Kalman Filter. In Computational Statistics and Data Analysis, Volume 83, Pages 101-115. (arXiv, DOI)
  38. S.M.J. Lyons, S. Särkkä, and A.J. Storkey (2014). Series Expansion Approximations of Brownian Motion for Non-Linear Kalman Filtering of Diffusion Processes. IEEE Transactions on Signal Processing, Volume 62, Issue 6, Pages 1514-1524. (DOI, arXiv)
  39. A. Solin and S. Särkkä (2013). Infinite-Dimensional Bayesian Filtering for Detection of Quasi-Periodic Phenomena in Spatio-Temporal Data. Physical Review E, Volume 88, Issue 5, 052909. (arXiv, DOI)
  40. S. Särkkä, A. Solin, and J. Hartikainen (2013). Spatio-Temporal Learning via Infinite-Dimensional Bayesian Filtering and Smoothing. IEEE Signal Processing Magazine, Volume 30, Issue 4, Pages 51-61. (Preprint, DOI)
  41. S. Särkkä and J. Sarmavuori (2013). Gaussian Filtering and Smoothing for Continuous-Discrete Dynamic Systems. Signal Processing, Volume 93. Issue 2, Pages 500-510. (Preprint, DOI, Matlab toolbox)
  42. I. S. Mbalawata, S. Särkkä, and H. Haario (2013). Parameter Estimation in Stochastic Differential Equations with Markov Chain Monte Carlo and Non-Linear Kalman Filtering. Computational Statistics, Volume 28, Issue 3, Pages 1195-1223 (DOI)
  43. S. Särkkä, A. Solin, A. Nummenmaa, A. Vehtari, T. Auranen, S. Vanni, F.-H. Lin (2012). Dynamic Retrospective Filtering of Physiological Noise in BOLD fMRI: DRIFTER. NeuroImage, Volume 60, Issue 2, Pages 1517-1527. (DOI, Preprint, Matlab toolbox)
  44. J. Sarmavuori and S. Särkkä (2012). Fourier-Hermite Kalman Filter. IEEE Transactions on Automatic Control, Volume 57, Issue 6, Pages 1511-1515. (DOI, Preprint)
  45. S. Särkkä, V. Viikari, M. Huusko, and K. Jaakkola (2012). Phase-Based UHF RFID Tracking with Non-Linear Kalman Filtering and Smoothing. IEEE Sensors Journal, Volume 12, Issue 5, Pages 904-910. (DOI, Preprint)
  46. S. Särkkä and A. Huovilainen (2011). Accurate Discretization of Analog Audio Filters with Application to Parametric Equalizer Design. IEEE Transactions on Audio, Speech, and Language Processing, Volume 19, Issue 8, Pages 2486-2493. (DOI , Preprint, Matlab code, C++ code, VST Effect for OS X)
  47. P. Hiltunen, S. Särkkä, I. Nissilä, A. Lajunen and J. Lampinen (2011). State space regularization in the nonstationary inverse problem for diffuse optical tomography. Inverse Problems, Volume 27, Number 2. (DOI)
  48. S. Särkkä and J. Hartikainen (2010). On Gaussian Optimal Smoothing of Non-Linear State Space Models. IEEE Transactions on Automatic Control, Volume 55, Issue 8, Pages 1938-1941. (DOI, Preprint, Matlab toolbox). See also errata DOI or Preprint.
  49. S. Särkkä (2010). Continuous-Time and Continuous-Discrete-Time Unscented Rauch-Tung-Striebel Smoothers. Signal Processing, Volume 90, Issue 1, Pages 225-235. (DOI, Preprint)
  50. S. Särkkä and A. Nummenmaa (2009). Recursive Noise Adaptive Kalman Filtering by Variational Bayesian Approximations. IEEE Transactions on Automatic Control, Volume 54, Issue 3, Pages 596-600. (DOI, Preprint, Matlab code)
  51. S. Särkkä and T. Sottinen (2008). Application of Girsanov Theorem to Particle Filtering of Discretely Observed Continuous-Time Non-Linear Systems. Bayesian Analysis, Volume 3, Number 03, Pages 555-584. (DOI)
  52. S. Särkkä (2008). Unscented Rauch-Tung-Striebel Smoother. IEEE Transactions on Automatic Control, Volume 53, Issue 3, Pages 845-849. (DOI, Preprint, Matlab toolbox)
  53. S. Särkkä, A. Vehtari, and J. Lampinen (2007). Rao-Blackwellized Particle Filter for Multiple Target Tracking. Information Fusion Journal, Volume 8, Issue 1, Pages 2-15. (DOI, Preprint, Matlab toolbox)
  54. S. Särkkä, A. Vehtari, and J. Lampinen (2007). CATS Benchmark Time Series Prediction by Kalman Smoother with Cross-Validated Noise Density. Neurocomputing, Volume 70, Issues 13-15, Pages 2331-2341. (DOI Preprint)
  55. S. Särkkä (2007). On Unscented Kalman Filtering for State Estimation of Continuous-Time Nonlinear Systems. IEEE Transactions on Automatic Control, Volume 52, Issue 9, Pages 1631-1641. (DOI, Preprint)

Conference Articles (peer-reviewed)

  1. Muhammad Emzir, Sari Lasanen, Zenith Purisha, Simo Särkkä (2019). Hilbert-Space Reduced-rank Methods for Deep Gaussian Processes. to appear in Proceedings IEEE International Workshop on Machine Learning for Signal Processing (MLSP).
  2. Toni Karvonen, Filip Tronarp Simo Särkkä (2019). Asymptotics of maximum likelihood parameter estimates for Gaussian processes: the Ornstein-Uhlenbeck prior. to appear in Proceedings IEEE International Workshop on Machine Learning for Signal Processing (MLSP).
  3. Rui Gao, Filip Tronarp, Zheng Zhao, Simo Särkkä (2019). Regularized state estimation and parameter learning via augmented Lagrangian Kalman smoother method. to appear in Proceedings IEEE International Workshop on Machine Learning for Signal Processing (MLSP).
  4. Roland Hostettler, Simo Särkkä (2019), Rejection-Sampling-Based Ancestor Sampling for Particle Gibbs. to appear in Proceedings IEEE International Workshop on Machine Learning for Signal Processing (MLSP).
  5. Roland Hostettler, Ángel García-Fernandez, Filip Tronarp, and Simo Särkkä (2019). Joint Calibration of Inertial Sensors and Magnetometers Using Von Mises-Fisher Filtering and Expectation Maximization. In Proceedings of 22nd International Conference on Information Fusion (FUSION).
  6. Matti Raitoharju, Ángel García-Fernandez, and Simo Särkkä (2019). Partitioned Update Binomial Gaussian Mixture Filter. In Proceedings of 22nd International Conference on Information Fusion (FUSION).
  7. Filip Tronarp and Simo Särkkä (2019). Updates in Bayesian filtering by continuous projections on a manifold of densities. In Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP). (Preprint)
  8. Janne Mustaniemi, Juho Kannala, Simo Särkkä, Jiri Matas and Janne Heikkilä (2019). Gyroscope-Aided Motion Deblurring with Deep Networks In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). (arXiv)
  9. Toni Karvonen, Chris J. Oates and Simo Särkkä (2018). A Bayes–Sard cubature method. Advances in Neural Information Processing Systems 32 (NIPS 2018). (arXiv)
  10. Toni Karvonen, Silvere Bonnabel, Eric Moulines, and Simo Särkkä (2018). Bounds on the covariance matrix of a class of Kalman-Bucy filters for systems with non-linear dynamics. In Proceedings of Conference on Decision and Control (CDC). (PDF)
  11. Zheng Zhao, Simo Särkkä, and Ali Bahrami Rad (2018). Spectro-Temporal ECG Analysis For Atrial Fibrillation Detection. In Proceedings of IEEE International Workshop on Machine Learning for Signal Processing (MLSP). (PDF)
  12. Kimmo Suotsalo, Simo Särkkä (2018). On-Line Bayesian Parameter Estimation In Electrocardiogram State Space Models. In Proceedings of IEEE International Workshop on Machine Learning for Signal Processing (MLSP).
  13. Filip Tronarp, Toni Karvonen, and Simo Särkkä (2018). Mixture representation of the Matérn class with applications in state space approximations and Bayesian quadrature. In Proceedings of IEEE International Workshop on Machine Learning for Signal Processing (MLSP). (Preprint)
  14. Rui Gao, Filip Tronarp, Simo Särkkä (2018). Combined Analysis-l1 and Total Variation ADMM with Applications to MEG Brain Imaging and Signal Reconstruction. In Proceedings of European Signal Processing Conference (EUSIPCO).
  15. Janne Mustaniemi, Juho Kannala, Simo Särkkä, Jiri Matas, Janne Heikkilä (2018). Fast Motion Deblurring for Feature Detection and Matching Using Inertial Measurements. In Proceedings of International Conference on Pattern Recognition (ICPR). (arXiv)
  16. Filip Tronarp and Simo Särkkä (2018). Non-Linear Continuous-Discrete Smoothing by Basis Function Expansions of Brownian Motion. In Proceedings of International Conference on Information Fusion (FUSION). (Preprint)
  17. Roland Hostettler, Tuomas Lumikari, Lauri Palva, Tuomo Nieminen, Simo Särkkä (2018). Motion Artifact Reduction in Ambulatory Electrocardiography Using Inertial Measurement Units and Kalman Filtering. In Proceedings of International Conference on Information Fusion (FUSION).
  18. Filip Tronarp, Roland Hostettler, Simo Särkkä (2018). Continuous-Discrete Von Mises-Fisher Filtering on S^2 for Reference Vector Tracking. In Proceedings of International Conference on Information Fusion (FUSION). (Preprint)
  19. R. Hostettler, F. Tronarp, and S. Särkkä. Modeling the drift function in stochastic differential equations using reduced rank Gaussian processes. In Proceedings of 18th IFAC Symposium on System Identification (SYSID), Stockholm, Sweden, July 2018.
  20. Filip Tronarp, Narayan Puthanmadam Subramaniyam, Simo Särkkä, Lauri Parkkonen (2018). Tracking of dynamic functional connectivity from MEG data with Kalman filtering. In Proceedings of 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. (Preprint)
  21. Olli-Pekka Rinta-Koski, Simo Särkkä, Jaakko Hollmén, Markus Leskinen, Krista Rantakari and Sture Andersson (2017). Prediction of major complications affecting very low birth weight infants . In Proceedings of IEEE Life Sciences Conference (LSC).
  22. Mustaniemi J, Kannala J, Särkkä S, Matas J & Heikkilä J (2017). Inertial-based scale estimation for structure from motion on mobile devices. In Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017). (arXiv)
  23. R. Hostettler, S. Särkkä, and S. J. Godsill (2017).Rao–Blackwellized particle MCMC for parameter estimation in spatio-temporal Gaussian processes. In Proceedings of 27th IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Tokyo, Japan, September 2017.
  24. Toni Karvonen and Simo Särkkä (2017). Classical quadrature rules via Gaussian processes. In 27th IEEE International Workshop on Machine Learning for Signal Processing (MLSP). (Preprint)
  25. Alexander Grigorievskiy, Neil Lawrence, Simo Särkkä (2017). Parallelizable sparse inverse formulation Gaussian processes (SpInGP) . In Proceedings of IEEE International Workshop on Machine Learning for Signal Processing (MLSP). (arXiv)
  26. K. Suotsalo and S. Särkkä (2017). A linear stochastic state space model for electrocardiograms. In Proceedings of 27th IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2017. (Preprint)
  27. K. Suotsalo and S. Särkkä (2017). Detecting Malignant Ventricular Arrhythmias in Electrocardiograms by Gaussian Process Classification. In Proceedings of 27th IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2017. (Preprint)
  28. R. Hostettler, O. Kaltiokallio, H. Yiğitler, S. Särkkä, and R. Jäntti (2017). RSS-based respiratory rate monitoring using periodic Gaussian processes and Kalman filtering In Proceedings of the 25th European Signal Processing Conference (EUSIPCO), Kos, Greece, August 2017.
  29. Jakub Prüher, Filip Tronarp, Toni Karvonen, Simo Särkkä and Ondřej Straka (2017). Student-t process quadratures for filtering of non-linear systems with heavy-tailed noise. In Proceedings of 20th International Conference on Information Fusion (FUSION). (arXiv)
  30. Olli-Pekka Rinta-Koski, Simo Särkkä, Jaakko Hollmén, and Sture Andersson (2017). Prediction of preterm infant mortality with Gaussian process classification.In Proceedings of 25th European Symposium on Articial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2017).
  31. Narayan Puthanmadam Subramaniyam, Filip Tronarp, Simo Särkkä and Lauri Parkkonen (2017). Expectation–maximization algorithm with a nonlinear Kalman smoother for MEG/EEG connectivity estimation. In Proceedings of EMBEC'17.
  32. Roland Hostettler and Simo Särkkä (2016). IMU and Magnetometer Modeling for Smartphone-based PDR. In Proceedings of IPIN.
  33. Toni Karvonen and Simo Särkkä (2016). Approximate state-space Gaussian processes via spectral transformation. In Proceedings of IEEE International Workshop on Machine Learning for Signal Processing (MLSP).
  34. Jakub Prüher and Simo Särkkä (2016). On The Use Of Gradient Information In Gaussian Process Quadratures. In Proceedings of IEEE International Workshop on Machine Learning for Signal Processing (MLSP). The best student paper award winner.
  35. Toni Karvonen and Simo Särkkä (2016). Fourier-Hermite Series for Stochastic Stability Analysis of Non-Linear Kalman Filters. In Proceedings of International Conference on Information Fusion (FUSION).
  36. Filip Tronarp, Roland Hostettler, and Simo Särkkä (2016). Sigma-Point Filtering for Nonlinear Systems with Non-Additive Heavy-Tailed Noise. In Proceedings of International Conference on Information Fusion (FUSION). The first runner-up student paper award winner. (Preprint)
  37. Arno Solin, Simo Särkkä, Juho Kannala, and Esa Rahtu (2016). Terrain Navigation in the Magnetic Landscape: Particle Filtering for Indoor Positioning. In Proceedings of the European Navigation Conference (ENC). (PDF)
  38. Andreas Svensson, Arno Solin, Simo Särkkä, Thomas Schön (2016). Computationally Efficient Bayesian Learning of Gaussian Process State Space Models. In Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS), Pages 213-221. (arXiv)
  39. Simo Särkkä, Eric Moulines (2016). On The L_p-Convergence of a Girsanov Theorem Based Particle Filter. In Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP). (PDF)
  40. Andreas Svensson, Thomas B. Schön, Arno Solin, Simo Särkkä (2015). Nonlinear State Space Model Identification Using a Regularized Basis Function Expansion. In Proceedings of the 6th IEEE international workshop on computational advances in multi-sensor adaptive processing (CAMSAP). (arXiv)
  41. Juho Kokkala and Simo Särkkä (2015). On the (Non-)convergence of Particle Filters with Gaussian Importance Distributions. In Proceedings of SYSID 2015. (DOI)
  42. Simo Särkkä, Ville Tolvanen, Juho Kannala, and Esa Rahtu (2015). Adaptive Kalman Filtering and Smoothing for Gravitation Tracking in Mobile Systems. In Proceedings of International Conference on Indoor Positioning and Indoor Navigation (IPIN) 2015. (PDF)
  43. Juho Kokkala and Simo Särkkä (2015). Split-Gaussian Particle Filter. In Proceedings of European Signal Processing Conference (EUSIPCO). (PDF)
  44. Arno Solin and Simo Särkkä (2015). State Space Methods for Efficient Inference in Student-t Process Regression. In Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS). (PDF)
  45. Jayaprasad Bojja, Jussi Collin, Simo Särkkä, and Jarmo Takala (2015). Pedestrian Localization in Moving Platforms Using Dead Reckoning, Particle Filtering and Map Matching. In Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP).
  46. A. Solin and S. Särkkä (2014). Gaussian quadratures for state space approximation of scale mixtures of squared exponential covariance functions. In Proceedings of IEEE International Workshop on Machine Learning for Signal Processing (MLSP). (Preprint as PDF)
  47. S. Särkkä and R. Piché (2014). On convergence and accuracy of state-space approximations of squared exponential covariance functions. In Proceedings of IEEE International Workshop on Machine Learning for Signal Processing (MLSP). (Preprint as PDF, Code in Bitbucket)
  48. I. S. Mbalawata and S. Särkkä (2014). Weight Moment Conditions for L4 Convergence of Particle Filters for Unbounded Test Functions. In Proceedings of European Signal Processing Conference (EUSIPCO). (Preprint as PDF)
  49. S. Särkkä, V. Viikari, K. Jaakkola (2014). RFID-Based Butterfly Location Sensing System. In Proceedings of European Signal Processing Conference (EUSIPCO). (Preprint as PDF)
  50. J. Kokkala, A. Solin, and S. Särkkä (2014). Expectation Maximization Based Parameter Estimation by Sigma-Point and Particle Smoothing. In Proceedings of FUSION 2014. (Preprint as PDF)
  51. S. Särkkä, J. Hartikainen, L. Svensson, and F. Sandblom (2014). Gaussian Process Quadratures in Nonlinear Sigma-Point Filtering and Smoothing. In Proceedings of International Conference on Information Fusion (FUSION). (Preprint as PDF)
  52. T. D. Barfoot, C. H. Tong, and S. Särkkä (2014). Batch Continuous-Time Trajectory Estimation as Exactly Sparse Gaussian Process Regression. In Proceedings of Robotics: Science and Systems (RSS). (PDF)
  53. I. S. Mbalawata and S. Särkkä (2014). On The L4 Convergence of Particle Filters with General Importance Distributions. In Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP). (Preprint as PDF)
  54. A. Solin and S. Särkkä (2014). Explicit Link Between Periodic Covariance Functions and State Space Models. JMLR Workshop and Conference Proceedings Volume 33 (AISTATS 2014), Pages 904-912. (Preprint as PDF, PDF)
  55. X. Chen, S. Särkkä, and S. Godsill (2013). Probabilistic Initiation and Termination for MEG Multiple Dipole Localization Using Sequential Monte Carlo Methods. In Proceedings of International Conference on Information Fusion (FUSION).
  56. S. Särkkä and A. Solin (2013). Continuous-Space Gaussian Process Regression and Generalized Wiener Filtering with Application to Learning Curves. In Proceedings of SCIA 2013. (Preprint as PDF, DOI)
  57. S. Särkkä and J. Hartikainen (2013). Non-Linear Noise Adaptive Kalman Filtering via Variational Bayes. In Proceedings of IEEE International Workshop on Machine Learning for Signal Processing (MLSP). (Preprint as PDE)
  58. S.M.J. Lyons, A.J. Storkey, and S. Särkkä (2012). The Coloured Noise Expansion and Parameter Estimation of Diffusion Processes. Proceedings of NIPS, pages 1961-1969. (PDF)
  59. R. Piché, S. Särkkä and J. Hartikainen (2012). Recursive Outlier-Robust Filtering and Smoothing for Nonlinear Systems Using the Multivariate Student-t Distribution. In Proceedings of IEEE International Workshop on Machine Learning for Signal Processing (MLSP). (Preprint as PDF)
  60. J. Sarmavuori and S. Särkkä (2012). Fourier-Hermite Rauch-Tung-Striebel Smoother. In Proceedings of European Signal Processing Conference (EUSIPCO), pages 2109-2113. (Preprint as PDF)
  61. S. Särkkä, P. Bunch and S. J. Godsill (2012). A Backward-Simulation Based Rao-Blackwellized Particle Smoother for Conditionally Linear Gaussian Models. Proceedings of SYSID 2012, pages 506-511. (invited paper). (Preprint as PDF)
  62. S. Särkkä and A. Solin (2012). On Continuous-Discrete Cubature Kalman Filtering. Proceedings of SYSID 2012, pages 1210-1215. (Preprint)
  63. J. Hartikainen, M. Seppänen and S. Särkkä (2012). State-Space Inference for Non-Linear Latent Force Models with Application to Satellite Orbit Prediction. Proceedings of The 29th International Conference on Machine Learning (ICML 2012). (PDF)
  64. S. Särkkä and J. Hartikainen (2012). Infinite-Dimensional Kalman Filtering Approach to Spatio-Temporal Gaussian Process Regression. JMLR Workshop and Conference Proceedings Volume 22: Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS), Pages 993-1001. (Preprint)
  65. J. Hartikainen and S. Särkkä (2011). Sequential Inference for Latent Force Models. Proceedings of The 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011) (Preprint)
  66. J. Hartikainen, J. Riihimäki and S. Särkkä (2011). Sparse Spatio-Temporal Gaussian Processes with General Likelihoods. Proceedings of International Conference on Artificial Neural Networks (ICANN) (DOI, Preprint)
  67. S. Särkkä (2011). Linear Operators and Stochastic Partial Differential Equations in Gaussian Process Regression. Proceedings of International Conference on Artificial Neural Networks (ICANN) (DOI, Preprint)
  68. S. Särkkä (2011). Learning Curves for Gaussian Processes via Numerical Cubature Integration. Proceedings of International Conference on Artificial Neural Networks (ICANN) (DOI, Preprint)
  69. J. Hartikainen and S. Särkkä (2010). Kalman Filtering and Smoothing Solutions to Temporal Gaussian Process Regression Models. Proceedings of IEEE International Workshop on Machine Learning for Signal Processing (MLSP) (Preprint)
  70. S. Särkkä and J. Hartikainen (2010). Sigma Point Methods in Optimal Smoothing of Non-Linear Stochastic State Space Models. Proceedings of IEEE International Workshop on Machine Learning for Signal Processing (MLSP) (Preprint)
  71. S. Särkkä, A. Vehtari, and J. Lampinen (2007). Prediction of ESTSP Competition Time Series by Unscented Kalman Filter and RTS Smoother. Proceedings of ESTSP 2007 (Preprint)
  72. S. Särkkä, A. Vehtari, and J. Lampinen (2004). Time series prediction by Kalman smoother with cross validated noise density. Proceedings of IJCNN 2004. The Winner of Time Series Prediction Competition - The CATS Benchmark (Preprint)
  73. S. Särkkä, A. Vehtari, and J. Lampinen (2004). Rao-Blackwellized Monte Carlo data association for multiple target tracking. Proceedings of International Conference on Information Fusion (FUSION) (Preprint, Matlab toolbox)
  74. A. Vehtari, S. Särkkä, and J. Lampinen (2000). On MCMC sampling in Bayesian MLP neural networks. Proceedings of the IJCNN 2000 (Preprint as ps.gz)

Book Chapters

  1. Simo Särkkä. The Use of Gaussian Processes in System Identification. To appear in Encyclopedia of systems and control, 2nd edition. (arXiv)

Other Conference Contributions (abstracts and light review)

  1. Toni Karvonen, Arno Solin, Ángel F. García-Fernández, Filip Tronarp, Simo Särkkä, and Fa-Hsuan Lin (2017). Where is Physiological Noise Lurking in k-Space? In Proceedings of ISMRM 2017.
  2. Kevin Wen-Kai Tsai, Hsin-Ju Lee, Ching-Po Lin, Li-Wei Kuo, Wen-Jui Kuo, Toni Auranen, Simo Särkkä, Fa-Hsuan Lin (2016). A Simultaneous fMRI-EEG acquisition to minimize the MR gradient artifact on human auditory system. Intl. Soc. Mag. Reson. Med.; 3768 (abstract)
  3. A. Solin, S. Särkkä, A. Nummenmaa, A. Vehtari, T. Auranen, F.-H. Lin (2014). Catching Physiological Noise: Comparison of DRIFTER in Image and k-Space. In Proceedings of ISMRM 2014 (abstract)
  4. A. Solin, E. Glerean, and S. Särkkä (2013). Time-Frequency Dynamics of Brain Connectivity by Stochastic Oscillator Models and Kalman Filtering. In Proceedings of OHBM 2013. (abstract)
  5. A. Solin, S. Särkkä, A. Nummenmaa, A. Vehtari, T. Auranen, S. Vanni, F.-H. Lin (2013). Volumetric Space-Time Structure of Physiological Noise in BOLD fMRI. In Proceedings of ISMRM 2013. (abstract)
  6. S. Särkkä, A. Solin, A. Nummenmaa, A. Vehtari, T. Auranen, S. Vanni and F.-H. Lin (2012). Identification of Spatio-Temporal Oscillatory Signal Structure in Cerebral Hemodynamics Using DRIFTER. Proceedings of ISMRM 2012. (abstract)
  7. S. Särkkä, A. Nummenmaa, A. Solin, A. Vehtari, T. Witzel, T. Auranen, S. Vanni, M.S. Hämäläinen, and F-H. Lin (2011). Dynamical statistical modeling of physiological noise for fast BOLD fMRI. Proceedings of ISMRM 2011. (abstract)
  8. A. Solin and S. Särkkä (2014). The 10th Annual MLSP Competition: First Place. In Proceedings of MLSP.
  9. S. Särkkä (2006). On Sequential Monte Carlo Sampling of Discretely Observed Stochastic Differential Equations. Proceedings of NSSPW, (Preprint)

Doctoral Dissertation

  1. S. Särkkä (2006). Recursive Bayesian Inference on Stochastic Differential Equations. Doctoral dissertation, Helsinki University of Technology (Thesis as PDF)

Technical Reports

  1. S. Särkkä and J. Hartikainen. Variational Bayesian Adaptation of Noise Covariances in Non-Linear Kalman Filtering. (arXiv)
  2. S. Särkkä (2007). Notes on Quaternions. Technical report. (Report as PDF)
  3. S. Särkkä, Toni Tamminen, Aki Vehtari, and Jouko Lampinen (2004). Probabilistic methods in multiple target tracking - Review and bibliography. Technical report B36, ISBN 951-22-6938-4, Helsinki University of Technology. Laboratory of Computational Engineering (Report as PDF)
  4. S. Särkkä (2000). Bayesilaiset menetelmät audiovisuaalisen puheen havaitsemisen mallintamisessa. Diploma thesis, (in Finnish) (Thesis as ps.gz)
  5. S. Särkkä (1999). MCMC-menetelmät ja diagnostiikat. Technical report (in Finnish). (HTML, ps.gz)

Course material:

  1. Simo Särkkä and Arno Solin (2014). Applied Stochastic Differential Equations. Lecture notes of the course Becs-114.4202 Special Course in Computational Engineering II held in Autumn 2014. (Booklet as PDF, Slides and exercises). (2012 material is here).
  2. S. Särkkä (2012). Bayesian Estimation of Time-Varying Systems: Discrete-Time Systems. Lectures notes of the course S-114.4610 held in Spring 2012 (Booklet as PDF, Slides and Exercises). (2011 material is here).

Working papers

  1. Toni Karvonen and Simo Särkkä. Worst-case optimal approximation with increasingly flat Gaussian kernels. Submitted. (arXiv)
  2. Simo Särkkä and Ángel F. García-Fernández. Temporal Parallelization of Bayesian Filters and Smoothers. Submitted. (arXiv)
  3. Morteza Zabihi, Ali Bahrami Rad, Serkan Kiranyaz, Simo Särkkä, and Moncef Gabbouj. 1D Convolutional Neural Network Models for Sleep Arousal Detection. Submitted. (arXiv)
  4. Zheng Zhao, Simo Särkkä, Ali Bahrami Rad. Kalman-based Spectro-Temporal ECG Analysis using Deep Convolutional Networks for Atrial Fibrillation Detection. Submitted. (arXiv)
  5. Jakub Prüher, Toni Karvonen, Chris J. Oates, Ondřej Straka, Simo Särkkä. Improved Calibration of Numerical Integration Error in Sigma-Point Filters. Submitted. (arXiv)
  6. Toni Karvonen, Silvère Bonnabel, Eric Moulines, and Simo Särkkä On stability of a class of filters for non-linear stochastic systems Submitted. (arXiv)
  7. Arno Solin, Pasi Jylänki, Jaakko Kauramäki, Tom Heskes, Marcel A. J. van Gerven, Simo Särkkä. Regularizing Solutions to the MEG Inverse Problem Using Space-Time Separable Covariance Functions. Submitted. (arXiv)

Software

Software Packages

Some Matlab toolboxes where I have contributed to (see also the code examples linked in the publication list above):


Teaching

Some courses etc. that I am giving / have given / will give soon: