Cheikh Toure is a quantitative researcher with nine years of experience combining applied mathematics, machine learning and advanced optimization to deliver production-grade solutions. A PhD graduate of École Polytechnique and alumnus of École Normale Supérieure Paris-Saclay, he has built multiobjective optimization frameworks used in industrial gas storage and prostate brachytherapy. He has taught reinforcement learning, optimization and statistics, published research papers, and transitioned his academic tooling to real-world deployments. Currently at Qube Research & Technologies, he applies black-box and gradient-free methods (notably CMA-ES) to quantitative problems at scale. Beyond traditional quant work, he explores blockchain and crypto applications, reflecting a curiosity for emerging tech that complements his optimization expertise. Colleagues know him for turning rigorous theory into robust, industrialized software.
9 years of coding experience
2 years of employment as a software developer
MP* - Mathematics, Computer Science and Physics, MP* - Mathematics, Computer Science and Physics at CPGE - Lycée Henri Poincaré
Doctor of Philosophy - PhD, Applied Mathematics, Doctor of Philosophy - PhD, Applied Mathematics at École Polytechnique
Master of Science - MS at LPMA (Laboratory of Probability and Applied Maths), Mathematical Statistics and Probability, Master of Science - MS at LPMA (Laboratory of Probability and Applied Maths), Mathematical Statistics and Probability at Pierre and Marie Curie University
Bachelor of Applied Science - BASc, Mathematics and Computer Science, Bachelor of Applied Science - BASc, Mathematics and Computer Science at École Normale Supérieure Paris-Saclay
Contributions:2 PRs, 72 pushes, 3 branches in 3 months
regressioninriaridgeinternshipmachine-learning
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