Supervised Machine Learning with Green Algorithm
Published:
Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Page Not Found
Page not found. Your pixels are in another canvas.
About me
About me
Page not in menu
This is a page not in th emain menu
Exploratory Data Analysis
Published:
Posts
Future Blog Post
Published:
This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.
Blog Post number 4
Published:
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
Published:
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 2
Published:
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
Published:
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
📄 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 paper is published at JGR: Atmosphere and can be downloaded from https://doi.org/10.1029/2023JD040281.
📄 Optimal Condition for Initialization Variance in Deep Neural Networks: An SGD Dynamics Perspective
Published in Arxiv, 2025
talks
Consensual Aggregation of Clusters based on Bregman Divergences to Improve Predictive Models
Published:
More information about “8th Meeting of Young Statisticians”.
Introduction of Machine Learning and Some Methods of Supervised Classification
Published:
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
Published:
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
Published:
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
Published:
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
Published:
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
Published:
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
Published:
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
Published:
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.
Frequentist vs Bayesian Statistics
Published:
This is an opening talk on statistics for Mathematics Association of Cambodia. It is a part of my activity in supervising a group of students in statistics and data science. The talk is about Frequentist and Bayesian points of view in statistics. The slide can be found here.
Gravity Wave Momentum Flux Reconstruction using Transfer Learning Bayesian Neural Networks
Published:
This is a poster presentation at Cross-VESRI event in Cambridge, UK. The poster is about the uncertainty prediction of balloon-observed GWMFs using Transfer Learning Bayesian Neural Networks. The poster can be found here.
teaching
UFR de Mathématiques - Université Paris Cité
License and Master's degree courses, Université Paris Cité, UFR de Mathématiques, 2018
🎓 Master’s degree (2018 - 2023)
| Course | Year | Program |
|---|---|---|
| TD of Statistical Modeling | 2018-2023 | M2MO |
| TP R-programming of Data Analysis | 2018-2023 | M1ISIFAR |
| TP R-programming of Data Mining | 2018-2023 | M2ISIFAR |
| TP R-programming of Exploratory Data Analysis | 2018-2023 | M1MIDS |
| TP of Big data technology with Spark and Python | 2022 | M1 Math-Info |
🎓 Bachelor’s degree
| Course | Season | Program |
|---|---|---|
| TP of Algorithm and programming in Python | 2022 | L3 Info |
Institute of Technology of Cambodia (ITC)
Bachelor's and Master's degree courses, Institute of Technology of Cambodia, Applied Mathematics and Statistics, 2024
🎓 Master’s degree
American University of Phnom Penh (AUPP)
Bachelor's and Master's degree courses, American University of Phnom Penh, School of Digital Technologies, 2024