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.

I followed various courses at CentraleSupélec (Paris) while learning by myself about these domains. I aim to do research on a related field in the future.

My curriculum (details on my LinkedIn):

• Research Intern at FLOWERS (INRIA's Reinforcement Learning team in Bordeaux (France)): Population Evolutionnary Reinforcement Learning Environnement - 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: Deep Learning applied to Video Games - 6 months

• Research intern at InstaDeep : 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

• Creation and teaching of courses on AI and Reinforcement Learning.
• 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.

• 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 with CUDA), 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.
• 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]