Jonathan Laurent
PHD Researcher at Carnegie Mellon University
Karlsruhe, Baden-Württemberg, Germany
Summary
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Rockstar🎓
Top SchoolJonathan Laurent is a PhD researcher in computer science at Carnegie Mellon University with 11 years of experience spanning language design for LLM-enabled software, self-supervised theorem proving, safe controller synthesis, and causal analysis of biological systems. He blends deep theoretical work with practical engineering—evidenced by a fast AlphaZero implementation in Julia where he adapted MCTS into a working TicTacToe solver. His background includes hands-on roles at Darklang optimizing an OCaml codebase, research internships at Harvard Medical School and NASA Langley, and years teaching rigorous mathematics to top French engineering students. Comfortable across formal verification, machine learning, and systems engineering, he often connects formal methods to real-world safety and biological problems. Based in Karlsruhe, Germany, he brings an uncommon mix of proof-driven research and production-minded coding that accelerates trustworthy ML systems.
11 years of coding experience
Master's degree, Machine Learning (Master Vision Apprentissage), Master's degree, Machine Learning (Master Vision Apprentissage) at ENS Paris-SaclayBachelor's degree, Computer Science, Bachelor's degree, Computer Science at Ecole normale supérieurePhD in Computer Science, PhD in Computer Science at Carnegie Mellon UniversityClasses préparatoires aux grandes écoles, MPSI / MP*, Classes préparatoires aux grandes écoles, MPSI / MP* at Lycée Louis-Le-Grand
French, English
Github Skills (6)
algorithm10
algorithms10
machine-learning10
deep-learning10
tensorflow9
python8
Programming languages (15)
Github contributions (5)
jonathan-laurent/AlphaZero.jl
Sep 2019 - Jan 2023
A generic, simple and fast implementation of Deepmind's AlphaZero algorithm.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:3 reviews, 651 commits, 27 PRs in 3 years 4 months
Contributions summary:Jonathan implemented a specialized version of the Monte Carlo Tree Search (MCTS) algorithm for AlphaZero, a deep learning method. Their contributions involved modifying the MCTS framework, specifically adapting it for the AlphaZero algorithm. The user also integrated the results into a TicTacToe solver, providing a concrete demonstration of their work.
deep-learningneural-networksmachine-learninggenericmonte-carlo-tree-search
jonathan-laurent/personal-website
Feb 2018 - Mar 2025
Contributions:64 pushes, 2 branches in 7 years 1 month
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