• Adjunct Professor, Electrical & Computer Engineering
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Headshot of professor Simo Sarkka

Research Interests

    • Bayesian filtering and smoothing
    • Computational methods for stochastic dynamic systems
    • Physics-informed machine learning
    • Time-parallel methods for estimation and control
    • Spatial and spatio-temporal Gaussian processes
    • Location sensing and target tracking
    • Robotics
    • Health and medical technology
    • Spatio-temporal models in machine learning, inverse problems, and Kriging

Certifications & Memberships

  • ELLIS Fellow
  • Senior Member of IEEE

Selected Publications

  • Simo Särkkä, Ángel F. García-Fernández (2025). Temporal Parallelisation of the HJB Equation and Continuous-Time Linear Quadratic Control. IEEE Transactions on Automatic Control, Volume 70, Number 6, Pages 3755--3770.
  • Nathanael Bosch, Adrien Corenflos, Fatemeh Yaghoobi, Filip Tronarp, Philipp Hennig, Simo Särkkä (2024). Parallel-in-Time Probabilistic Numerical ODE Solvers. Journal of Machine Learning Research, Volume 25.
  • Simo Särkkä and Lennart Svensson (2023). Bayesian Filtering and Smoothing, Second Edition. Cambridge University Press.
  • 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.
  • Simo Särkkä and Arno Solin (2019). Applied Stochastic Differential Equations. Cambridge University Press.
  • 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.
  • 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.
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