Biography

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. Please also see errata.

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

   • Full book in PDF format (4M)

   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:

   • Full book in PDF format (4.3M)

   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. William J. Wilkinson, Simo Särkkä, Arno Solin. Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees Accepted for publication in Journal of Machine Learning Research. (arXiv)
2. Sakira Hassan, Simo Särkkä. Fourier-Hermite Dynamic Programming for Optimal Control. Accepted for publication in IEEE Transactions on Automatic Control. (arXiv)
3. Muhammad Fuady Emzir, Zheng Zhao, Simo Särkkä. Multidimensional Projection Filters via Automatic Differentiation and Sparse-Grid Integration. Accepted for publication in Signal Processing. (arXiv)
4. Christos Merkatas and Simo Särkkä. System identification using Bayesian neural networks with nonparametric noise models. Accepted for publication in Journal of Time Series Analysis. (arXiv)
5. Zheng Zhao, Simo Särkkä, Jens Sjölund, Thomas B. Schön (2023). Probabilistic Estimation of Chirp Instantaneous Frequency Using Gaussian Processes. IEEE Transactions on Signal Processing, Volume 71, Pages 461-476. (arXiv)
6. Simo Särkkä and Ángel F. García-Fernándezl (2023). Temporal Parallelisation of Dynamic Programming and Linear Quadratic Contro. IEEE Transactions on Automatic Control, Volume 68, Issue 2, 851-866. (Open Access, arXiv, Code in GitHub)
7. Hao Dong, Xieyuanli Chenb, Simo Särkkä, Cyrill Stachniss (2023). Online pole segmentation on range images for long-term LiDAR localization in urban environments. Robotics and Autonomous Systems, Volume 159, 104283 (to appear). (Link)
8. Adrien Corenflos, Nicolas Chopin, Simo Särkkä (2022). De-Sequentialized Monte Carlo: a parallel-in-time particle smoother. Journal of Machine Learning Research 23, 1-39 (Open Access, arXiv)
9. Joel Jaskari, Jaakko Sahlsten, Theodoros Damoulas, Jeremias Knoblauch, Simo Särkkä, Leo Kärkkäinen, Kustaa Hietala, Kimmo K. Kaski (2022). Uncertainty-Aware Deep Learning Methods for Robust Diabetic Retinopathy Classification. IEEE Access, vol. 10, pp. 76669-76681. (Open Access)
10. S. Kirschenmann, M. Bezak, S. Bharthuar, E. Brücken, M. Emzir, M. Golovleva, A. Gädda, M. Kalliokoski, A. Karadzhinova-Ferrer, A. Karjalainen, P. Koponen, N. Kramarenko, P. Luukka, J. Ott, H. Petrow, T. Siiskonen, S. Särkkä, J. Tikkanen, R. Turpeinen, A. Winkler (2022). Multispectral photon-counting for medical imaging and beam characterization – a project review. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Volume 1039, 167043. (Link)
11. Sarang Thombre, Zheng Zhao, Henrik Ramm-Schmidt, José M. Vallet García, Tuomo Malkamäki, Sergey Nikolskiy, Toni Hammarberg, Hiski Nuortie, M. Zahidul H. Bhuiyan, Simo Särkkä, and Ville V. Lehtola (2022). Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review. IEEE Transactions on Intelligent Transportation Systems, , vol. 23, no. 1, pp. 64-83. (Open Access)
12. Zheng Zhao and Simo Särkkä (2021). Non-linear Gaussian smoothing with Taylor moment expansion. IEEE Signal Processing Letters, Volume 29, Pages 80-84. (arXiv, Open Access)
13. Zheng Zhao, Muhammad Emzir, Simo Särkkä (2021). Deep State-Space Gaussian Processes. Statistics and Computing, Volume 31, 75. (arXiv, Open Access)
14. Sakira Hassan, Simo Särkkä, Ángel F. García-Fernández (2021). Temporal Parallelization of Inference in Hidden Markov Models. IEEE Transactions on Signal Processing, Volume 69, Pages 4875-4887. (arXiv, Open Access, Code)
15. Rui Gao, Simo Särkkä, Rubén Claveria-Vega, Simon Godsill. Autonomous Tracking and State Estimation with Generalised Group Lasso. Accepted for publication in IEEE Transactions on Cybernetics. (arXiv)
16. Miika Arvonen, Paavo Raittinen, Oskar Niemenoja, Pauliina Ilmonen, Sari Riihijärvi, Simo Särkkä, Lauri Viitasaari (2021). Nationwide infection control strategy lowered seasonal respiratory infection rate: occupational health care perspective during the COVID-19 epidemic in Finland. Infectious Diseases, Volume 53, Issue 11, Pages 839-846. (Open Access)
17. Zheng Zhao, Toni Karvonen, Roland Hostettler, Simo Särkkä (2021). Taylor Moment Expansion for Continuous-Discrete Gaussian Filtering. IEEE Transactions on Automatic Control, Volume 66, Issue 9, Pages 4460-4467. (arXiv)
18. Jarkko Suuronen, Muhammad Emzir, Sari Lasanen, Simo Särkkä, Lassi Roininen (2020). Enhancing Industrial X-ray Tomography by Data-Centric Statistical Methods. Data-Centric Engineering journal, 1, E10. (arXiv, Open Access)
19. Toni Karvonen, Simo Särkkä, and Ken'ichiro Tanaka (2021). Kernel-based interpolation at approximate Fekete points. Numerical Algorithms, 87(1):445–468. (DOI, arXiv)
20. Rantakari K, Rinta-Koski O-P, Metsäranta M, Hollmén J, Särkkä S, Rahkonen P, Lano A, Lauronen L, Nevalainen P, Leskinen MJ, Andersson S (2021). Early oxygen levels contribute to brain injury in extremely preterm infants. Pediatric Research. (Open Access)
21. Filip Tronarp, Simo Särkkä, and Philipp Hennig (2021). Bayesian ODE Solvers: The Maximum A Posteriori Estimate Statistics and Computing 31, 23. (arXiv, Open Access)
22. Muhammad Emzir, Sari Lasanen, Zenith Purisha, Lassi Roininen, Simo Särkkä (2021). Non-Stationary Multi-layered Gaussian Priors for Bayesian Inversion. Inverse Problems 37, 015002. (arXiv)
23. Jakub Prüher, Toni Karvonen, Chris J. Oates, Ondřej Straka, Simo Särkkä (2021). Improved Calibration of Numerical Integration Error in Sigma-Point Filters. IEEE Transactions on Automatic Control, 66(3), 1286-1292. (arXiv)
24. Simo Särkkä and Ángel F. García-Fernández (2021). Temporal Parallelization of Bayesian Smoothers. IEEE Transactions on Automatic Control, Volume 66, Issue 1, Pages 299-306. (DOI, arXiv, Code examples)
25. Rui Gao, Filip Tronarp, and Simo Särkkä (2020). Variable Splitting Methods for Constrained State Estimation in Partially Observed Markov Processes. IEEE Signal Processing Letters, Volume 27, Pages 1305-1309. (arXiv)
26. Joel Jaskari, Janne Myllärinen, Markus Leskinen, Ali Bahrami Rad, Jaakko Hollmén, Sture Andersson, Simo Särkkä (2020). Machine Learning Methods for Neonatal Mortality and Morbidity Classification. IEEE Access, Volume 8, Pages 123347-123358 (Open Access)
27. Toni Karvonen, Silvère Bonnabel, Eric Moulines, and Simo Särkkä (2020). On stability of a class of filters for non-linear stochastic systems. SIAM Journal on Control and Optimization, 58(4), 2023–2049. (arXiv)
28. Toni Karvonen, George Wynne, Filip Tronarp, Chris J. Oates and Simo Särkkä (2020). Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions. SIAM/ASA Journal on Uncertainty Quantification, 8(3), 926–958. (arXiv)
29. David Luengo, Luca Martino, Monica Bugallo, Victor Elvira, and Simo Särkkä. (2020) A Survey of Monte Carlo Methods for Parameter Estimation. EURASIP Journal on Advances in Signal Processing., Volume 2020, Article Number 25. (Open Access)
30. Zheng Zhao, Simo Särkkä, Ali Bahrami Rad (2020). Kalman-based Spectro-Temporal ECG Analysis using Deep Convolutional Networks for Atrial Fibrillation Detection. Journal of Signal Processing Systems, Volume 92, pages 621–636. (arXiv)
31. Multispectral photon-counting for medical imaging and beam characterization. E. Brücken, S. Bharthuar, M. Emzir, M. Golovleva, A. Gädda, R. Hostettler, J. Härkönen, S. Kirschenmann, V. Litichevskyi, P. Luukka, L. Martikainen, T. Naaranoja, I. Nincă, J. Ott, H. Petrow, Z. Purisha, T. Siiskonen, S. Särkkä, J. Tikkanen, T. Tuuva, and A. Winkler (2020). Multispectral photon-counting for medical imaging and beam characterization. Journal of Instrumentation 15 C02024. (arXiv, DOI)
32. Toni Karvonen and Simo Särkkä (2020). Worst-case optimal approximation with increasingly flat Gaussian kernels. Advances in Computational Mathematics 46, 21. (arXiv)
33. Roland Hostettler, Filip Tronarp, Ángel F. García-Fernández, Simo Särkkä (2020). Importance Densities for Particle Filtering using Iterated Conditional Expectations. IEEE Signal Processing Letters, Volume 27, 211-215.
34. M. Raitoharju, Á. F. García-Fernández, R. Hostettler, R. Piché, and S. Särkkä (2020). Gaussian Mixture Models for Signal Mapping and Positioning. Signal Processing, Volume 168, 107330.
35. A. Solin and S. Särkkä (2020). Hilbert Space Methods for Reduced-Rank Gaussian Process Regression. Statistics and Computing, Volume 30, pages 419-446. (arXiv)
36. Hüseyin Yiğitler, Ossi Kaltiokallio, Roland Hostettler, Riku Jäntti, Neal Patwari, Simo Särkkä (2020). RSS Models for Respiration Rate Monitoring. IEEE Transactions on Mobile Computing, Volume 19, Issue 3, pages 680-696. (arXiv)
37. Rui Gao, Filip Tronarp, and Simo Särkkä (2019). Iterated Extended Kalman Smoother-based Variable Splitting for L1-Regularized State Estimation. IEEE Transactions on Signal Processing, Volume 67, Issue 19, pages 5078-5092. (arXiv)
38. Toni Karvonen, Motonobu Kanagawa, Simo Särkkä (2019). On the positivity and magnitudes of Bayesian quadrature weights. Statistics and Computing, Volume 29, pages 1317-1333. (arXiv)
39. Toni Karvonen and Simo Särkkä (2019). Gaussian kernel quadrature at scaled Gauss–Hermite nodes. BIT Numerical Mathematics, Volume 59, pages 877-902. (arXiv)
40. Filip Tronarp, Hans Kersting, Simo Särkkä, Philipp Hennig (2019). Probabilistic Solutions To Ordinary Differential Equations As Non-Linear Bayesian Filtering: A New Perspective. Statistics and Computing, Volume 29, pages 1297-1315. (arXiv)
41. Toni Karvonen, Simo Särkkä and Chris J. Oates (2019). Symmetry exploits for Bayesian cubature methods. Statistics and Computing, Volume 29, pages pages 1231-1248. (arXiv)
42. Zenith Purisha, Carl Jidling, Niklas Wahlström, Simo Särkkä, Thomas B. Schön (2019). Probabilistic approach to limited-data computed tomography reconstruction. Inverse Problems, 35(10):105004. (arXiv)
43. Ángel F. García-Fernández, Filip Tronarp, Simo Särkkä (2019). Gaussian target tracking with direction-of-arrival von Mises-Fisher measurements. IEEE Transactions on Signal Processing, Volume 67, Issue 11, Pages 2960-2972.
44. Ángel F. García-Fernández, Filip Tronarp, and Simo Särkkä (2019). Gaussian process classification using posterior linearisation. IEEE Signal Processing Letters, Volume 26, Issue 5, Pages 735-739. (arXiv)
45. Ángel F. García-Fernández, Roland Hostettler, Simo Särkkä (2019). Rao-Blackwellised posterior linearisation backward SLAM. IEEE Transactions on Vehicular Technology, Volume 68, Issue 5, Pages 4734-4747. (Preprint)
46. Juha Sarmavuori and Simo Särkkä (2019). Numerical Integration as a Finite Matrix Approximation to Multiplication Operator. Journal of Computational and Applied Mathematics, Volume 353, Pages 283-291. (arXiv)
47. Filip Tronarp, Toni Karvonen, and Simo Särkkä (2019). Student’s t-Filters for Noise Scale Estimation IEEE Signal Processing Letters, Volume 26, Issue 2, Pages 352-356. (Preprint)
48. 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)
49. 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)
50. Roland Hostettler and Simo Särkkä (2019). Rao–Blackwellized Gaussian Smoothing. IEEE Transactions on Automatic Control, Volume 64, Issue 1, Pages 305-312. (DOI, Preprint)
51. 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)
52. 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)
53. 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).
54. Toni Karvonen and Simo Särkkä (2018). Fully symmetric kernel quadrature. SIAM Journal on Scientific Computing, 40(2), A697–A720. (DOI, arXiv)
55. 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))
56. 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)
57. 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) (Code)
58. 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)
59. Ángel F. García-Fernández, Lennart Svensson, Simo Särkkä (2017). Iterated posterior linearisation smoother. IEEE Transactions on Automatic Control, Volume 62, Issue 4. (DOI, Preprint).
60. 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)
61. 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)
62. 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)
63. 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)
64. 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)
65. 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)
66. 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)
67. 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)
68. 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)
69. 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)
70. 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)
71. 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)
72. 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)
73. 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)
74. 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)
75. 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)
76. 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)
77. 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)
78. J. Sarmavuori and S. Särkkä (2012). Fourier-Hermite Kalman Filter. IEEE Transactions on Automatic Control, Volume 57, Issue 6, Pages 1511-1515. (DOI, Preprint)
79. 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)
80. 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)
81. 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)
82. 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.
83. 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)
84. 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)
85. 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)
86. S. Särkkä (2008). Unscented Rauch-Tung-Striebel Smoother. IEEE Transactions on Automatic Control, Volume 53, Issue 3, Pages 845-849. (DOI, Preprint, Matlab toolbox)
87. 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)
88. 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)
89. 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 Abubakar Yamin, Paola Valsasina, Massimo Filippi, Maria A. Rocca, Simo Särkkä, and Diego Sona (2022). Searching for Functional Brain Connectivity Markers: an Application to Multiple Sclerosis In Proceedings of IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI).
2. Christoph Hold, Archontis Politis, Simo Särkkä (2022). Sound-source position tracking from direction-of-arrival measurements: Application to distributed first-order spherical microphone arrays. In Proceedings of International Congress on Acoustics (ICA)
3. Adrien Corenflos, Zheng Zhao, and Simo Särkkä (2022). Temporal Gaussian Process Regression in Logarithmic Time. In Proceedings of FUSION 2022. (PDF, arXiv)
4. Muhammad Fuady Emzir, Niki A. Loppi, Zheng Zhao, Syeda S. Hassan and Simo Särkkä (2022). Fast optimize-and-sample method for differentiable Galerkin approximations of multi-layered Gaussian process priors. In Proceedings of FUSION 2022. (PDF)
5. Matti Raitoharju, Roland Hostettler and Simo Särkkä (2022). Posterior linearisation filter for non-linear state transformation noises. In Proceedings of FUSION 2022. (PDF)
6. Filip Tronarp and Simo Särkkä (2022). Continuous-Discrete Filtering and Smoothing on Submanifolds of Euclidean Space. In Proceedings of FUSION 2022. (PDF, arXiv)
7. Simo Särkkä, Christos Merkatas, Toni Karvonen (2021). Gaussian Approximations of SDEs in Metropolis-adjusted Langevin Algorithms. In Proceedings of MLSP. (PDF)
8. Leo McCormack, Archontis Politis, Simo Särkkä, Ville Pulkki (2021). Real-Time Tracking of Multiple Acoustical Sources Utilising Rao-Blackwellised Particle Filtering. In Proceedings of EUSIPCO.
9. Matti Raitoharju, Henri Nurminen, Demet Cilden-Guler, and Simo Särkkä (2021). Kalman filtering with empirical noise models. In Proceedings of ICL-GNSS. (arXiv)
10. Fatemeh Yaghoobi, Adrien Corenflos, Sakira Hassan, Simo Särkkä (2021). Parallel Iterated Extended and Sigma-Point Kalman Smoothers. To appear in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). (arXiv, Code in GitHub)
11. Harshit Agrawal, Ari Hietanen, Simo Särkkä (2021). Metal Artifact Reduction in Cone-beam Extremity Images using Gated Convolutions. In Proceedings of 2021 IEEE International Symposium on Biomedical Imaging (ISBI).
12. Mustaniemi J, Kannala J, Matas J, Särkkä S & Heikkilä J (2020). LSD_2 - Joint denoising and deblurring of short and long exposure images with CNNs. British Machine Vision Conference (BMVC 2020). (arXiv, GitHub)
13. Ajinkya Gorad, Zheng Zhao, and Simo Särkkä (2020). Parameter estimation in non-linear state-space models by automatic differentiation of non-linear Kalman filters. In Proc. MLSP
14. Rui Gao and Simo Särkkä (2020). Augmented Sigma-Point Lagrangian Splitting Method for Sparse Nonlinear State Estimation. Proc. EUSIPCO.
15. Simo Särkkä and Lennart Svensson (2020). Levenberg–Marquardt and Line-Search Extended Kalman Smoothers. In Proceedings of 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020). (PDF)
16. Zheng Zhao, Filip Tronarp, Roland Hostettler, and Simo Särkkä (2020). State-space Gaussian Process for Drift Estimation in Stochastic Differential Equations. In Proceedings of 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020). (PDF)
17. Salla Aario, Ajinkya Gorad, Miika Arvonen, and Simo Särkkä (2020). Respiratory Pattern Recognition from Low-Resolution Thermal Imaging. Proc. ESANN.
18. Eero Immonen, Mika Lauren, Lassi Roininen and Simo Särkkä (2020). Multiobjective model-based optimization of diesel injection rate profile by machine learning methods. Proceedings of 2020 IEEE International Systems Conference (SysCon).
19. Eero Immonen, Mika Lauren, Lassi Roininen and Simo Särkkä (2020). Neural network based identification of fuel injection rate profiles for diesel engines. Proceedings of 2020 9th International Conference on Industrial Technology and Management (ICITM).
20. Mikko K Leino, Juha Ala-Laurinaho, Zenith Purisha, Simo Särkkä, Ville Viikari (2019). Millimeter-wave imaging method based on frequency-diverse subarrays In Proc. Global Symposium on Millimeter Waves (GSMM) (Preprint)
21. Muhammad Emzir, Sari Lasanen, Zenith Purisha, Simo Särkkä (2019). Hilbert-Space Reduced-rank Methods for Deep Gaussian Processes. Proceedings IEEE International Workshop on Machine Learning for Signal Processing (MLSP).
22. Toni Karvonen, Filip Tronarp Simo Särkkä (2019). Asymptotics of maximum likelihood parameter estimates for Gaussian processes: the Ornstein-Uhlenbeck prior. Proceedings IEEE International Workshop on Machine Learning for Signal Processing (MLSP).
23. Rui Gao, Filip Tronarp, Zheng Zhao, Simo Särkkä (2019). Regularized state estimation and parameter learning via augmented Lagrangian Kalman smoother method. Proceedings IEEE International Workshop on Machine Learning for Signal Processing (MLSP).
24. Roland Hostettler, Simo Särkkä (2019), Rejection-Sampling-Based Ancestor Sampling for Particle Gibbs. Proceedings IEEE International Workshop on Machine Learning for Signal Processing (MLSP).
25. 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).
26. 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).
27. 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)
28. 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)
29. Toni Karvonen, Chris J. Oates and Simo Särkkä (2018). A Bayes–Sard cubature method. Advances in Neural Information Processing Systems 32 (NIPS 2018). (arXiv)
30. 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)
31. 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)
32. 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).
33. 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)
34. 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).
35. 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)
36. 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)
37. 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).
38. 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)
39. 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.
40. 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)
41. 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).
42. 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)
43. 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.
44. 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)
45. 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)
46. 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)
47. 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)
48. 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.
49. 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)
50. 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).
51. 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.
52. Roland Hostettler and Simo Särkkä (2016). IMU and Magnetometer Modeling for Smartphone-based PDR. In Proceedings of IPIN.
53. 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).
54. 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.
55. 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).
56. 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)
57. 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)
58. 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)
59. 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)
60. 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)
61. Juho Kokkala and Simo Särkkä (2015). On the (Non-)convergence of Particle Filters with Gaussian Importance Distributions. In Proceedings of SYSID 2015. (DOI)
62. 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)
63. Juho Kokkala and Simo Särkkä (2015). Split-Gaussian Particle Filter. In Proceedings of European Signal Processing Conference (EUSIPCO). (PDF)
64. 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)
65. 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).
66. 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)
67. 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)
68. 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)
69. S. Särkkä, V. Viikari, K. Jaakkola (2014). RFID-Based Butterfly Location Sensing System. In Proceedings of European Signal Processing Conference (EUSIPCO). (Preprint as PDF)
70. 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)
71. 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)
72. 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)
73. 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)
74. 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)
75. 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).
76. 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)
77. 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)
78. 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)
79. 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)
80. 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)
81. 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)
82. S. Särkkä and A. Solin (2012). On Continuous-Discrete Cubature Kalman Filtering. Proceedings of SYSID 2012, pages 1210-1215. (Preprint)
83. 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)
84. 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)
85. 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)
86. 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)
87. 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)
88. S. Särkkä (2011). Learning Curves for Gaussian Processes via Numerical Cubature Integration. Proceedings of International Conference on Artificial Neural Networks (ICANN) (DOI, Preprint)
89. 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)
90. 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)
91. 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)
92. 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)
93. 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)
94. 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 and Editorials

1. Simo Särkkä, Lassi Roininen, Manon Kok, Roland Hostettler, Andreas Hauptmann Guest Editorial: MLSP 2020 Special Issue. Accepted for publication in Journal of Signal Processing Systems.
2. Simo Särkkä. The Use of Gaussian Processes in System Identification (2021). In Encyclopedia of systems and control, 2nd edition. (arXiv)
3. Simo Särkkä, Andreas Hauptmann, Manon Kok, Michael Riis Andersen, Lassi Roininen (eds.) (2020). Proceedings of the 2020 IEEE International Workshop on Machine Learning for Signal Processing (MLSP).

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. 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.. (arXiv)
2. S. Särkkä and J. Hartikainen. Variational Bayesian Adaptation of Noise Covariances in Non-Linear Kalman Filtering. (arXiv)
3. S. Särkkä (2007). Notes on Quaternions. Technical report. (Report as PDF)
4. 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)
5. S. Särkkä (2000). Bayesilaiset menetelmät audiovisuaalisen puheen havaitsemisen mallintamisessa. Diploma thesis, (in Finnish) (Thesis as ps.gz)
6. S. Särkkä (1999). MCMC-menetelmät ja diagnostiikat. Technical report (in Finnish). (HTML, ps.gz)

Course material:

1. Roland Hostettler and Simo Särkkä (2020). Lecture notes on Basics of Sensor Fusion. Lecture notes of course ELEC-E8740 - Basics of sensor fusion. (Booklet as PDF)
2. 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).
3. 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. Simo Särkkä, Ángel F. García-Fernández. Temporal Parallelisation of the HJB Equation and Continuous-Time Linear Quadratic Control. Submitted. (arXiv)
2. Teemu Kuosmanen, Simo Särkkä, Ville Mustonen. Turnover shapes evolution of birth and death rates. Submitted (bioRxiv)
3. Fatemeh Yaghoobi, Adrien Corenflos, Sakira Hassan, Simo Särkkä. Parallel square-root statistical linear regression for inference in nonlinear state space models Submitted. (arXiv)
4. Adrien Corenflos, Simo Särkkä. The Coupled Rejection Sampler. Submitted. (arXiv)
5. Zheng Zhao, Rui Gao, Simo Särkkä. Hierarchical Non-Stationary Temporal Gaussian Processes With L1-Regularization. Submitted. (arXiv)
6. Juha Sarmavuori and Simo Särkkä. Strong Resolvent Convergence of Finite Matrix Approximations in Numerical Integration. Submitted. (arXiv)
7. Morteza Zabihi, Ali Bahrami Rad, Serkan Kiranyaz, Simo Särkkä, and Moncef Gabbouj. 1D Convolutional Neural Network Models for Sleep Arousal Detection. Submitted. (arXiv)
8. Ali Bahrami Rad, Morteza Zabihi, Zheng Zhao, Moncef Gabbouj, Aggelos K. Katsaggelos, and Simo Särkkä. Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network. Submitted. (arXiv)

Software

Software Packages

• LFM Toolbox for Matlab
• DRIFTER Toolbox for Matlab
• RBMCDA Toolbox for Matlab
• EKF/UKF Toolbox for Matlab
• MCMC Methods for MLP and GP and Stuff (for Matlab)
• MCMC Diagnostics for Matlab
• FBM tools for Matlab

Teaching

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

• Spring 2023: ELEC-E8106 - Bayesian Filtering and Smoothing (5 p)
• Autumn 2022: ELEC-E8740 - Basics of sensor fusion (5 p).
• Spring 2022: ELEC-E8106 - Bayesian Filtering and Smoothing (5 p)
• Spring 2022: ELEC-E8736 - Basics of MRI (5 p)
• Autumn 2021: ELEC-E8740 - Basics of sensor fusion (5 p).
• Spring 2021: ELEC-E8106 - Bayesian Filtering and Smoothing (5 p)
• Spring 2021: ELEC-E8736 - Basics of MRI (5 p)
• Autumn 2020: ELEC-E8740 - Basics of sensor fusion (5 p).
• Summer 2020: EEA-EV - Course with Varying Content: Applied Stochastic Differential Equations, Aalto University.
• Spring 2020: ELEC-E8736 - Basics of MRI (5 p).
• Spring 2020: ELEC-E8105 - Non-linear filtering and parameter estimation (5 p) L.
• Autumn 2019: ELEC-E8740 - Basics of sensor fusion (5 p).
• Spring 2019: ELEC-E8736 - Basics of MRI (5 p).
• Spring 2019: ELEC-E8105 - Non-linear filtering and parameter estimation (5 p) L.
• Autumn 2018: EEA-EV/CS-EV - Course with Varying Content: Applied Stochastic Differential Equations, Aalto University.
• Autumn 2018: ELEC-E8742 - Translational Engineering Forum (5 p)
• Autumn 2018: Tutorial on Bayesian filtering and smoothing methods for machine learning in MLSP 2018 Conference.
• Autumn 2018: Lecture in Workshop (minicourse) on Computational Mathematics and Data Science, Oulu
• Spring 2018: ELEC-E8736 - Basics of MRI (5 p).
• Spring 2018: ELEC-E8105 - Non-linear filtering and parameter estimation (5 p) L.
• Spring 2017: ELEC-E8736 - Basics of MRI (5 p).
• Spring 2017: ELEC-E8105 - Non-linear filtering and parameter estimation (5 p) L.
• Autumn 2016: EEA-EV - Course with Varying Content: Applied Stochastic Differential Equations:, Aalto University and Tampere University of Technology.
• Summer 2016: Tutorial on Introduction to Bayesian filtering and smoothing at Fusion 2016 Conference.
• Spring 2016: ELEC-E8105 - Non-linear filtering and parameter estimation (5 p) L.
• Autumn 2015: Bayesian Filtering and Smoothing, Universidad Tecnológica de Pereira, Colombia.
• Spring 2015: Becs-114.4610 Special Course in Bayesian Modelling: Bayesian estimation of time-varying systems (5 p) P
• Autumn 2014: Becs-114.4202/Mat-1.C Special Course in Computational Engineering II: Applied Stochastic Differential Equations (3 cr).
• Autumn 2014: Tutorial on Bayesian Filtering and Smoothing at EUSIPCO'2014 conference in Lisbon/Portugal.
• Spring 2014: ASE 5036 Optimal Estimation at TUT.
• Michaelmas 2013: Minicourse on Stochastic Differential Equations in Bayesian Dynamic Models and Machine Learning at University of Oxford, UK.
• Autumn 2013: Lecture in fMRI school 2013 of O.V. Lounasmaa Laboratory.
• Summer 2013: Lecture in Gaussian Process Models summer school, University of Sheffield, UK.
• Spring 2013: Guest lecture on course ASE-5030/6 Optimal estimation at TUT.
• Spring 2013: Becs-114.4610 Special Course in Bayesian Modelling: Bayesian estimation of time-varying systems (5 p) P
• Fall 2012: MAT-55216 Topics in Applied Mathematics: Applied stochastic differential equations (3 cr) at TUT
• Spring 2012: Lecture in fMRI school 2012 of O.V. Lounasmaa Laboratory.
• Spring 2012: S-114.4610 Special Course in Bayesian Modelling: Bayesian estimation of time-varying processes (5 p) P
• Spring 2011: MAT-55216 Topics in Applied Mathematics: Bayesian estimation of time-varying processes: discrete-time systems (5 cr) at TUT
• Spring 2011: S-114.4220 Research Seminar on Computational Science: Numerical Methods for Stochastic Differential Equations (3 p) P
• Spring 2010: S-114.4202 Special Course in Computational Engineering II: Bayesian Estimation of Time-Varying Processes (5 p)
• Spring 2009: S-114.4202 Special Course in Computational Engineering II: Bayesian Estimation of Time-Varying Processes (5 p)
• Spring 2008: S-114.4220 Research Seminar on Computational Science: Stochastic Models in Spatial and Image Analysis
• Fall 2007: S-114.4220 Research Seminar on Computational Science: Stochastic and Adaptive Control of Uncertain Systems (5 p) L V
• Fall 2006: S-114.4220 Research Seminar on Computational Science: Bayesian Estimation of Time-Varying Processes (5 p) L V