Published in Journal of Statistical Computation and Simulation, 2021
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Published in HAL, 2022
Machine learning methods applied to the global modeling of event-driven pitch angle diffusion coefficients during high speed streams
Published in Journal of Frontier Physics, 2022
Published in Journal of Data Science Statistics and Visualisation (JDSSV), 2023
More information about “8th Meeting of Young Statisticians”.
It was a mission in Cambodia. I gave a short course of introduction to machine learning at Institut de Technologie du Cambodge (ITC).
It was an online summer school. The talk was about the applications of KFC procedure on the domain of energy. More information about the summer school can be found here. The video of the talk can be found here.
Forum for Pushing the Boundary (FPB) is a group of Cambodian scholars, creating with a purpose of sharing knowledge around Math, Sciences and general topics to Cambodian students. Here, I share one talk with them about ML. The slide can be found (here).
The talk is about a mathematical model of data compression known as Vector Quantization and its algorithmic adaptation, the well-known K-means algorithm. The goal of this talk is to show how the mathematical theories of vector quantization model became the fundamental structure of K-means clustering algorithm. The slide can be found here.
Estimating Balloon-observed Gravity Wave Moment Fluxes using Machine Learning and Input from Reanalysis
The talk took place at the SCALES conference from 26th to 30th June 2023 in Mainz, Germany. It is about reconstructing GWMFs observed by super-pressure balloons of Stratéole-2 campaign using ML and the description of large-scale flow from ERA5. It is the first part of my Postdoctoral research in atmospheric science. The slide can be found here.
This talk aims at providing key concepts of Machine Learning through real examples. Linear models in regression and classification are introduced along with some tasks. Some gradient-based optimization and penalization techniques are also discussed from Linear Regression to Deep Neural Networks. The slide can be found here: Introduction to Machine Learning. For those who want to play with the codes, you can find
jupyter notebook of the simulation here: Teaching repository.