Biography
Mike Pereira is a researcher whose work focuses on mathematical and geostatistical modeling applied to complex dynamic systems, particularly in the fields of hydrology, road traffic, and random fields. His research focuses on developing advanced methods for predicting and simulating spatiotemporal phenomena, incorporating tools such as kriging, stochastic partial differential equations (SPDEs), and Gaussian random field models. A significant portion of his contributions concerns the improvement of modeling techniques for non-stationary anisotropies, applied to various contexts such as alluvial deposits, geological structures, and the regulation of mixed traffic (autonomous and human-driven vehicles). His recent work also explores the intersection between geostatistical methods and machine learning approaches, particularly for real-time traffic flow prediction and the simulation of random fields on Riemannian manifolds.
Publication(s)
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2025
Kriging Alluvial Thicknesses in Valley Bottoms Using Nonstationary Geometric Anisotropies DOI : 10.1007/s11004-025-10200-w
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2024
Mean-square exponential stabilization of mixed-autonomy traffic PDE system DOI : 10.1016/j.automatica.2024.111859
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2024
Continuous Simulation of Heterogeneous Media: The Karhunen-Loève Approach Versus the Turning Bands Method DOI : 10.1007/978-3-031-58665-1_3
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2024
The Traffic Reaction Model: A kinetic compartmental approach to road traffic modeling DOI : 10.1016/j.trc.2023.104435
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2023
Mean-square exponential stabilization of coupled hyperbolic systems with random parameters DOI : 10.1016/j.ifacol.2023.10.991
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2023
Data-Driven Distance Metrics for Kriging-Short-Term Urban Traffic State Prediction DOI : 10.1109/TITS.2023.3251022
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2023
Galerkin–Chebyshev approximation of Gaussian random fields on compact Riemannian manifolds DOI : 10.1007/s10543-023-00986-8
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2022
Short-term traffic prediction using physics-aware neural networks DOI : 10.1016/j.trc.2022.103772
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2022
Geostatistics for Large Datasets on Riemannian Manifolds: A Matrix-Free Approach DOI : 10.6339/22-JDS1075
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2022
Parameter and density estimation from real-world traffic data: A kinetic compartmental approach DOI : 10.1016/j.trb.2021.11.006
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2019
SPDE geostatistical filtering for seismic data DOI : 10.3997/2214-4609.201900848
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2019
Efficient simulation of Gaussian Markov random fields by Chebyshev polynomial approximation DOI : 10.1016/j.spasta.2019.100359
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2019
Plurigaussian simulations with the stochastic partial differential equation (SPDE) approach DOI : 10.3997/2214-4609.201902174
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2019
Geostatistical filtering of noisy seismic data using Stochastic Partial Differential Equations (SPDE) DOI : 10.3997/2214-4609.201902264
Projects
- 2024-2029 SICIM - PEPR Maths-Vives Participant Le projet scientifique SICIM a pour objectif de développer une approche statistique permettant la reconstitution spatio-temporelle des trajectoires de fronts de glaciers, à partir des positions observées des moraines datées, en incluant les incertitudes de datation et les moraines manquantes. La question probabiliste associée est de savoir comment, conditionnellement aux positions et dates de données censurées (les moraines non-détruites), simuler des tirages aléatoires du processus spatio-temporel complet, puis d’être capable d’effectuer l’inférence. Il s’agit donc de combiner de façon innovante théorie des records, théorie des valeurs extrêmes, et simulation conditionnelles dans un cadre statistique spatial.
Teaching
Differential, Integral, and Stochastic Calculus II (Math2)
EC2 consists of two modules: Differential Equations and Probability. The differential equations course aims to introduce students to the study of dynamical systems: existence, uniqueness, and regularity of solutions to a differential equation (Peano–Arzela, Cauchy-Lipschitz, regularity with respect to initial conditions in finite time, chaotic systems), as well as an introduction to the study of the asymptotic behavior of solutions (periodic cycles, asymptotic stability of equilibrium points, etc.) An introduction to the simulation and numerical analysis of differential equations is also provided (discretization schemes, consistency/convergence analysis, differences between explicit and implicit schemes for stiff systems, the role of symplectic schemes for Hamiltonian systems, etc.) The probability course aims to consolidate and supplement the knowledge of probability theory acquired in CPGE, but above all to develop probabilistic reasoning. In CPGE, probability was studied in the context of random phenomena with at most a countable number of possible outcomes. Probabilities defined on the real line, as well as real random variables and vectors, are first introduced within the general formalism of measure theory—covered in EC1—which allows for the inclusion of the discrete case. The concepts of independence and conditioning of random variables, sequences of random variables, and finally stochastic simulation methods are addressed in turn to cover all the prerequisites necessary for the various engineering specializations offered at the school, particularly data science.
Geostatistics
General Introduction and Introduction to the R Software (www.r-project.org) Random Function Models, Inference, and Prediction (Kriging and Simulations) Spatiotemporal Modeling
Option Géostatistique et Probabilités Appliquées
PhD supervision
- 2025 Reconstruction de l'évolution spatio-temporelle des fronts de glaciers par simulation stochastique MEGRET Maud
- 2024 Modeling of extreme event episodes on graphs MAATOUK Rita