Ruben Ohana

Ruben Ohana is a Research Fellow at the Simons Foundation's Center for Computational Mathematics, focusing on machine learning and mathematics to develop innovative optimization and training methods for neural networks, particularly within the Polymathic AI initiative. Previously, as a PhD candidate at École normale supérieure, Ruben specialized in designing new machine learning algorithms, applying randomness to enhance differential privacy, adversarial robustness, and neural network optimization. Additional experiences include a PhD internship at Criteo, where work was centered on Optimal Transport, and various research internships involving quantum information and experimental optics, contributing to projects at institutions such as LIP6 and MIT. Ruben holds multiple degrees, including a Master 2 in Mathematics and Statistics from Sorbonne Université and a Diplôme d'ingénieur in Physics from ESPCI Paris - PSL.