|🆕 Textbook draft 🆕||Short bio|
MVA: I teach at the MVA Master’s program of ENS Paris-Saclay the course Turing Seminar – An introduction to AGI Safety.
ML4Good Summer school: I designed the program for the international ML4Good bootcamps to help master’s students and young researchers upskill in AI safety. The program has been conducted three times in France, twice in Switzerland, and once in Germany.
Training Day at ENS Ulm: I gave a series of lectures over an entire day at ENS Ulm to introduce technical AI safety. The lecture series is available as a YouTube playlist.
Responsible ML: I contributed to the Responsible Machine Learning course of the MVA Master’s program , to evaluate student groups on their technical AI safety projects.
MLAB, TA, Berkeley: I was a TA in Berkeley during the MLAB which was a hands-on introduction to state-of-the-art ML techniques (e.g. transformers, deep RL, mechanistic interpretability).
My research focuses on AI safety. I’ve worked notably on the theory of RLHF and interpretability, as well as safe by design AI approaches.
Noteworthy papers and blog articles:
I’ve supervised several small research groups during the ML4Good training bootcamps, as well as student groups from the PSC of École Polytechnique, during the “apart research” hackathons, a hackathon I organized with Collège de France on AI safety, during the Entrepreneur First hackathon organized at Meta, and during the advanced upskilling program ARENA. For a selection of projects I’ve supervised, please refer to the Effisciences AI website.
I was a speaker at the international conference “Funding the Common”, where I delivered a talk titled “Artificial General Intelligence Alignment: Difficulties in Current Techniques & Better Preference Elicitation”.
I was the CTO for a year at a 5-person startup, attempting to create a search engine for academic papers. This involved trying to use LLMs to help compile literature reviews semi-automatically, fine-tuning numerous AIs for specialized tasks, and building a semantic database engine. Unfortunately, the technology was not mature enough at the time. A writeup (about part of the journey) is available here. The website is no longer functional. The code is open-source here.
I’ve contributed to numerous open-source projects:
I interned at PriceMoove, where I built a microeconomic inference library that utilized hierarchical Bayesian machine learning. The code is proprietary and not open-source, but I’m very proud of it.
SinGAN is a generative model that can be learned from a single natural image. We propose a rigorous method to evaluate the image creation capabilities of a GAN using the PatchMatch algorithm.
The human brain combines top-down and bottom-up signals. Bottom-up signals result from direct perception, while top-down signals take into account past experience.
I worked on the Belief Propagation algorithm, and unified the notations between causal Bayesian graphs and factor graphs. Take the time to look at our report, which contains some very nice maths. We have also written a very versatile implementation of the belief propagation algorithm for factor graphs.
I’ve worked on the coherence of the nearest neighbor algorithm with the Bayesian axioms of probability. This project required the writing of highly optimized Numba routines.
In this repository, you will find my work done during the course of 3D computer vision. During the last TP, we reconstructed a face in 3D from two photos.
Here is a solution to the Inria-BCI competition. This solution would be ranked 4th in the original competition.
In this repository you will find an efficient implementation of the paper All resolution inference for brain imaging. This paper proposes a new methodology to construct brain statistics when using fMRI data.
In this repo, I apply the methodology of MEG signal analysis using the MNE library.
In this repository, you will find the winner’s solution of the KIRO2018, in partnership with Air France. We build in an iterative way a solution to the scheduling problem of the Air France aircraft fleet.
In this repository, you will find the winner’s solution of the extended competition of the KIRO2019. We efficiently built the graph of 5G antennas connected by optical fiber in major French cities.
In this paper written to validate the Biostatistics course, I critique/destroy the statistical methodology used by two highly cited papers written at the beginning of the Covid epidemic.
Boogie-Woogie is great fun to play on the piano, and it’s also pretty easy to automate.
In this essay, I use concepts from Gestalt psychology to describe the patterns of the birds of Escher and I play with the attention of the viewer. Gestalt psychology is in a way the ancestor of the interpretability of neural networks in vision processing.