In this page I provide code for some of the projects or tutorial e.g. how to use DataFrames packages or do Bayesian inference of ODEs systems.

Code examples

Weather related

• Is the climate change warming trend easily visible in Montpellier? Here is a small Data Frame manipulation in Julia showing that the answer is yes.

• A notebook (currently in French) showcases a project called Apprentis Chercheurs (see INRAE summary for the 2024 edition), designed for middle and high school students. The theme was "Maths to the Rescue of Farmers: Weather Forecasting and Crop Selection Optimization". The notebook introduces randomness and mathematical modeling through dice rolling, building a simple weather generator, and then using it to simulate crop yields. Basic bandit algorithms are applied to optimize crop selection.

Presenting Julia

Overview of Julia

I did several presentations of Julia to researchers and students. Here is a website with intro to Julia and presentation of GANs

• Generic introduction to Julia (needs updating)

• Multiple dispatch example (adapted from the blog post First Impressions of Julia from an R User)

• Performance comparison with Python/R/C using a sum example

• Optimization, ODEs, and Bayesian inference Pluto notebook (may take a while to load)

• GAN application with Flux.jl: first on a simple Gaussian and multivariate example, then on the MNIST dataset

Composability

Julia is a very composable language i.e., two unrelated packages can be used together very easily. This is largely due to the fact that most of Julia packages are written in Julia. Plus multiple dispatch, plus automatic differentiation.

I was inspired by this Bayesian ODE tutorial and this talk by Chris Rackauckas "Integrating solvers w/ probabilistic programming through differentiable programming", to produce my own Bayesian ODE example for my colleagues. It can be found here with an ODE coming from fermentation modeling.