# Mike Pereira

Postdoctor, Mathematical Sciences

I am a postdoctoral researcher within the project STONE, which is carried out jointly by the Division of Applied Mathematics and Statistics in the Department of Mathematical Sciences and the Automatic Control Group within the Department of Electrical Engineering. The goal of the project is to use stochastic partial differential equations to model traffic flows and to estimate parameters based on data from real measurements.

My research areas are primarily spatial and computational statistics. But I also have strong interests in Machine Learning, graph theory and stochastic partial differential equations, and how these domains can foster new approaches to deal with spatial data.

My research areas are primarily spatial and computational statistics. But I also have strong interests in Machine Learning, graph theory and stochastic partial differential equations, and how these domains can foster new approaches to deal with spatial data.

TMS088/MSA410, Financial time series, 2020

**2020**

- Pereira, M., Desassis, N., Magneron, C., and Palmer, N. (2020). A matrix-free approach to geostatistical
filtering.
*arXiv:2004.02799*

**2019**

- Pereira, M. (2019).
*Generalized random fields on Riemannian manifolds: theory and practice*(Doctoral dissertation, PSL Research University). - Pereira, M., and Desassis, N. (2019). Efficient simulation of Gaussian Markov random fields by Chebyshev polynomial approximation. Spatial Statistics, 31, 100359.
- Pereira, M., Magneron, C., and Desassis, N. (2019). Geostatistical filtering of noisy seismic data using stochastic partial differential equations (SPDE). In
*Petroleum Geostatistics 2019*.