Sara Pérez-Vieites

Sara Pérez-Vieites

I am a postdoctoral researcher at Aalto University and the Finnish Center for Artificial Intelligence (FCAI), working on approximate inference and Bayesian experimental design.

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Pages

Posts

Future Blog Post

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Blog Post number 4

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

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Blog Post number 2

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

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Pérez-Vieites, S., Mariño, I. P., & Míguez, J. (2018). Probabilistic scheme for joint parameter estimation and state prediction in complex dynamical systems. Physical Review E, 98(6), 063305.
arXiv | BibTeX | DOI | Code </span>

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Pérez-Vieites, S., Vilà-Vals, J., Bugallo, M. F., Míguez, J., & Closas, P. (2019). Second order subspace statistics for adaptive state-space partitioning in multiple particle filtering. In 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) (pp. 609-613). IEEE.
Download | BibTeX | DOI </span>

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Pérez-Vieites, S., & Míguez, J. (2020). A nested hybrid filter for parameter estimation and state tracking in homogeneous multi-scale models. In 2020 IEEE 23rd International Conference on Information Fusion (FUSION) (pp.1--8). IEEE.
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Pérez-Vieites, S., & Míguez, J. (2021). Kalman-based nested hybrid filters for recursive inference in state-space models. In 2020 28th European Signal Processing Conference (EUSIPCO) (pp.2468-2472). IEEE.
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Pérez-Vieites, S., & Míguez, J. (2021). Nested Gaussian filters for recursive Bayesian inference and nonlinear tracking in state space models. Signal Processing, 189, 108295.
arXiv | BibTeX | DOI | Code </span>

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Pérez-Vieites, S. (2022). Nested filtering methods for Bayesian inference in state space models. PhD thesis, Universidad Carlos III de Madrid.
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Pérez-Vieites, S., & Elvira, V. (2023). Adaptive Gaussian nested filter for parameter estimation and state tracking in dynamical systems. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp.1-5). IEEE.
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Cox, B., Pérez-Vieites, S., Zilberstein, N., Sevilla, M., Segarra, S. , & Elvira, V. (2024). End-to-end learning of Gaussian mixture proposals using differentiable particle filters and neural networks. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp.9701-9705). IEEE.
Download | BibTeX | DOI </span>

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Pérez-Vieites, S., Molina-Bulla, H., & Míguez, J. (2025). Nested smoothing algorithms for inference and tracking of heterogeneous multi-scale state-space systems. Foundations of Data Science.
arXiv | BibTeX | DOI </span>

talks

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teaching

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Teaching Assistant, Universidad Carlos III de Madrid (2017-2019)
112 hours conducting workshops in Linear Systems.

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Co-supervision of B.Sc. thesis, Aalto University (Jan-Jun 2025)
Thesis title: Variational inference approaches to Bayesian optimal experimental design, Aditya Agrawal.

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Guest lecturer, Aalto University (2024-2025)
Lectures in Stochastic Optimal Control in the course Digital and Optimal Control. Part of the Electrical Engineering and Automation master with major in Control, Robotics, and Autonomous Systems.

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Mini-tutorials on Monte Carlo methods, Cyber-Physical Systems group, Aalto University (Oct-Nov 2024)
All materials are available in this GitHub repository, which includes slides and Python code to reproduce the examples from these tutorials:
  • Introduction to Monte Carlo and importance sampling (08/10/2024)
  • Introduction to sequential Monte Carlo (05/11/2024 and 19/11/2024)

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Co-supervision of PhD student, Aalto University (2025-2029)
Juri Voloskin will focus on Bayesian experimental design (BED) and control for prognostics in industrial applications (collaborating with ABB).