My name is Timothé Boulet and I am a french Machine Learning Engineer specialized in Deep Learning. In particular, I have a lot of interest for Reinforcement Learning. I also worked on other DL fields such as Supervised Learning, Time Series, NLP, and AI Safety & Alignment, and on other AI fields such as Game Theory/Game Playing AI, Operational Research and Search Problems/Decision Making.

My curriculum (details on my LinkedIn):

• PhD at FLOWERS: Using Large Language Model for generation of controllers in interaction agent-environment - current

• Research Intern at FLOWERS (INRIA's Reinforcement Learning team in Bordeaux (France)): Studying the dynamics of Ecosystems of Neural Agents - 6 months

• Maths Vision Apprentissage (MVA) master at Ecole Normale Supérieure Paris Saclay : one of the most renowned masters in europe for machine learning - 1 year

• R&D intern at Ubisoft La Forge Montréal: Deep Learning applied to Video Games - 6 months

• Research intern at InstaDeep Paris : Deep RL for optimization in maritime Network Design - 6 months

• Student at CentraleSupélec : top 3 French Engineer School (Deep Learning lessons and many more) - 3 years

I'm doing a PhD since december 2024 at FLOWERS on the topic of "Using Large Language Model for generation of controllers in interaction agent-environment".

• Creation and teaching of courses on AI and Reinforcement Learning.
• Template : a template for starting Research Projects with good code practice and usefull tips.
• Web App : A playground for Reinforcement Learning algorithms. It is a simple app that allows you to play with different environments, algorithms, and hyperparameters.
• A Gridworld Reinforcement Learning Environment and Framework.

• Time measure package : a simple context manager to measure the time taken by a block of code, that only add one line of code per section to measure. PyPI package : tmeasure
• VM Placement problem : The formulation sof the VM Placement problem and several variants of it, as well as algorithms for solving them.
• Deep RL Framework : a framework made by myself for Deep Reinforcement Learning. It is based on the OpenAI Gym and pytorch.
• AI agents for Search Problems : a python package for Search Problems and Search Algorithms (DFS, A*,...). PyPI package : SearchProblemsAI.
• AI agents for Games : a python package for Perfect Information Extensive-from Games (Chess, Connect4,...) and some algorithm ((Expecti)MiniMax, MCTS, ...).
• LocalPerf : an easy-to-use package for measuring the performance of python in any machine, in terms of CPU, multiprocessing and GPU (pytorch and JAX), and also verify that the GPU is used. PyPI package : localperf.
• Telegram bot : Sold-out Tickets Detector. Repository here.
• IntegOR : a python package based on scipy.optimize that aims to traduce an Operation Research problem that can be linearized to a binary integer linear problem, to solve it. PyPI package : integor.
• Stable Baselines 3 Full Pipeline. A repository built on top of SB3 with some additional features. Repository here.
• Backtracking : A python package for backtracking algorithm, as an interface and a solving function. PyPI package : backtracking.
• Computer Vision Project : Facial Expression Recognition. Information here.

• Research project with Google DeepMind : Population-based methods for Multi-agent Reinforcement Learning, studied in the minimalistic framework of Normal Form Games.
• Machine Learning CentraleSupélec 2022 Competition : our team ranked 1st out of 76 teams. Our repository here.
• QRT 2024 Data Challenge : A competition about guessing Football matches winners. Our team of 2 ranked 2nd/24 of the Academic leaderboard, and 8th/387 of the Private leaderboard.
• School Research Project : PCA-guided K-means. A paper-reproduction benchmark on various K-means initialization method, in particular PCA-guided search.
• School Project : Improved Semantic Segmentation for Night Drive (mission for Valeo).
• School Project : Task Scheduling with A* and IDA*. Implementation of an informed search algorithm A* and IDA* for solving a scheduling problem.

You can contact me on LinkedIn or by mail.

Personal Mail : [email protected]
CentraleSupélec mail : [email protected]
ENS Paris Saclay mail : [email protected]
INRIA mail : [email protected]