Etienne Bernard holds a PhD in statistical physics from ENS Paris. During in thesis "Algorithms and applications of the Monte Carlo method: two-dimensional melting and perfect sampling", he designed Markov-chain Monte Carlo algorithms in order to solve physics problems.
He then worked as a postdoctoral scholar at MIT on problems related to Monte Carlo algorithms and non-equilibrium statistical physics. Etienne is now working in the Advanced Research Group of Wolfram Research, developing machine learning functionalities for the Wolfram Language.
He developed "Classify" and "Predict", which are highly automated functions to perform supervised learning. Etienne's work aims to simplify the practice of machine learning in order to spread its utilisation.
Ph.D., Statistical Physics