Supervised Machine Learning with Green Algorithm
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Exploratory Data Analysis
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Posts
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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 1
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portfolio
publications
KFC: A clusterwise supervised learning procedure based on aggregation of distances
Published in Journal of Statistical Computation and Simulation, 2021
Consensual aggregation on random projected high-dimensional features for regression
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
Gradient COBRA: A kernel-based consensual aggregation for regression
Published in Journal of Data Science Statistics and Visualisation (JDSSV), 2023
☛ Download paper and gradientcobra Python library is available in PyPi.
GradientCOBRA python library
Published in Pypi, 2023
GradientCOBRA python library for various ML aggregation methods.
Reconstructing balloon-observed gravity wave momentum fluxes using machine learning and input from ERA5
Published in JGR: Atmosphere, 2024
The preprint can be downloaded here. The paper is accepted for publication at JGR: Atmosphere.
talks
Consensual Aggregation of Clusters based on Bregman Divergences to Improve Predictive Models
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More information about “8th Meeting of Young Statisticians”.
Introduction of Machine Learning and Some Methods of Supervised Classification
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It was a mission in Cambodia. I gave a short course of introduction to machine learning at Institut de Technologie du Cambodge (ITC).
KFC: A Clusterwise Supervised Learning Procedure based on Aggregation of Distances
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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.
A kernel-based Consensual Aggregation for Regression
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More information about Journées de Statistique 2021. The talk was about my research topic of a kernel-based aggregation method for regression. The slide can be found here.
Supervised Machine Learning with Some Green Algorithms
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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).
$K$-means Clustering Algorithm via Vector Quantization
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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
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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.
Fundamental and Hands-on Introduction to Machine Learning
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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.
Aggregations of parameterizations and machine learning for gravity wave momentum flux reconstruction
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This is a poster presentation at EGU 2024. The talk is about aggregation of machine learning and parameterizations for gravity wave momentum flux reconstruction. The poster can be found here.