Ruben obtained his Ph.D. from Ecole Normale Supérieure in Paris in machine learning. His main research interests are the uses of randomness in machine learning algorithms, be it in alternative training methods to backpropagation, kernel approximation, high-dimensional probabilities, or optimal transport. He earned his Ph.D. in collaboration with the startup LightOn which develops optical hardware to scale-up machine learning computations at a higher speed and a lower energy consumption. His work can therefore be from theoretical machine learning to computational optics or quantum physics.
Before his Ph.D., he graduated with an engineering degree in physics from ESPCI Paris, an M.Sc. in condensed matter and quantum physics degree from Ecole Normale Supérieure, and a MSc in machine learning and statistics from Sorbonne University.