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: June 07, 2021

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: May 15, 2022

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: August 25, 2022

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: June 20, 2023

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: August 10, 2023

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: April 16, 2024

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: May 26, 2024

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: July 09, 2024

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