Pablo Piantanida

CNRS, CentraleSupélec

Director of International Laboratory on Learning Systems (ILLS)

Associate Academic Member Mila 

Institut québécois d'intelligence artificielle (Mila)

Email: pablo dot piantanida at

Research Interests

My research is in the areas of machine learning, computer vision and natural language processing. I am interested in developing rigorous techniques based on information measures and concepts for building safe and trustworthy AI systems and establishing confidence in their behavior and robustness, thereby securing  their use in society. Although my work focuses on machine learning I am also interested broadly in information theory—the mathematical description of information and its utilization— and its interactions with numerous areas of applied mathematics and science. Other interests include fundamental understanding of neural networks, generalization, learning theory, privacy, fairness, and applications of machine learning. 

Short Bio

Pablo Piantanida received both B.Sc. in Electrical Engineering and M.Sc degrees from the University of Buenos Aires (Argentina) in 2003, and the Ph.D. from Université Paris-Sud (Orsay, France) in 2007. In October 2007 he has joined the Department of Telecommunications at Supélec and in 2015 the Laboratoire des Signaux et Systèmes (L2S) - CNRS at CentraleSupélec within Université Paris-Saclay. He is currently director of the International Laboratory and Learning Systems (ILLS), professor at CentraleSupélec (Université Paris-Saclay) with CNRS and Associate Academic Member Mila - Quebec AI Institute (Mila). From 2018 to 2019, he was visitor researcher at Mila, Université de Montréal, and from 2019 to 2020 he was visitor researcher at  Laboratoire de Mathématiques d’Orsay (LMO). He is an IEEE Senior Member and has served as General Co-Chair of the 2019 IEEE International Symposium on Information Theory (ISIT), and as area chair for several conferences and he was Associate Editor for the IEEE Trans. on Information Forensics and Security. In the past, he has worked on the information-theoretic principles beyond distributed compression, statistical decision, universal source coding, cooperation, feedback, index coding, key generation, security and privacy, among others. His current research is in the areas of machine learning, computer vision and natural language processing.

Location at ETS Montreal 

1100 Rue Notre Dame O, Montréal, QC H3C 1K3, Canada 

(Office: A-3597)

Location at Mila

6666 Rue Saint-Urbain, Montréal, QC H2S 3H1, Canada