Summary
Quentin Bouniot is an Assistant Professor in Statistical Learning Theory at Télécom Paris and a researcher in deep representation learning with nine years of experience spanning academia and industry. His work focuses on vision-language alignment, explainability, robustness, and uncertainty quantification, building on a PhD on few-shot learning for detection and segmentation. He held postdoctoral positions at TUM and Helmholtz Munich in Explainable Machine Learning and has industrial experience improving robustness of facial recognition systems at CEA-List. Comfortable teaching core algorithms as well as advancing cutting-edge ML theory, he bridges rigorous theoretical insights with practical, low-data and safety-critical applications. Notably, his trajectory blends classical mathematical training from CentraleSupélec with recent interdisciplinary research on multimodal alignment.
9 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD, Intelligence artificielle, Doctor of Philosophy - PhD, Intelligence artificielle at Université Jean Monnet Saint-Etienne
Diplôme d'ingénieur, Diplôme d'ingénieur at CentraleSupélec
Master 2 (M2), Apprentissage, Vision et Robotique, Master 2 (M2), Apprentissage, Vision et Robotique at Universite de Lorraine
Classe préparatoire aux grandes écoles - MPSI/MP*, Mathématiques, Classe préparatoire aux grandes écoles - MPSI/MP*, Mathématiques at Lycée Montaigne