ABOUT ME

I build software and machine learning systems for scientific problems.

I work at the intersection of science, data and software engineering, turning complex scientific questions into computational tools, models and reliable systems.

SCIENCE Questions · Hypotheses · Evidence
MACHINE LEARNING Models · Patterns · Prediction
SOFTWARE Systems · APIs · Applications
DATA Pipelines · Analysis · Knowledge

I don't see these as separate disciplines. They are different parts of the same process:

understand the problem → explore the data → build the computational approach → turn it into something useful.

Current focus

My current work is centered around food science and the broader challenge of building more sustainable food systems.

I'm particularly interested in how scientific knowledge, data and computational tools can help us better understand food, its properties and its interaction with consumers, while contributing to more responsible use of resources.

This includes scientific data, sensory analysis, modelling, machine learning and software systems designed to support research and experimentation.

Food science gives me a rich environment to combine my engineering and scientific interests while working on problems with a tangible impact on how we produce and consume food.

What I build

Machine Learning

Models and computational approaches to extract information, recognize patterns and solve domain-specific problems.

Data

Pipelines and systems that turn raw, heterogeneous data into something usable for research and decision-making.

Software

APIs, applications and tools that make scientific workflows more accessible, reproducible and scalable.

Scientific Computing

Algorithms, modelling and computational methods for exploring and solving scientific problems.

From an experiment to a production system, I like working across the entire technical stack.

Beyond the current domain

While food science is my current focus, I don't define myself by a single industry.

What interests me most are complex problems where scientific thinking, data and software can come together to create something useful.

My goal is to develop a versatile technical foundation that allows me to move between domains, learn new scientific contexts and apply the same engineering mindset to different kinds of problems.

The domain may change. The way I approach problems remains the same: understand deeply, experiment, build, iterate.

Background

From -

Software Engineer

INRAE

Certified Machine Learning Engineer

Mines Paris - PSL

Certified Data Scientist

Mines Paris - PSL

LET'S BUILD

Interested in building something?

I'm interested in challenging problems, scientific projects and opportunities where engineering can make a difference.

Explore my workGet in touch