Research Director at Inria, Director of the Microsoft Research-Inria Joint Centre, and Professor at the Applied Mathematics Centre of Ecole Polytechnique. Co-author of the Best Paper Award-winning papers of IEEE INFOCOM 1999, ACM SIGMETRICS 2005, ACM CoNEXT 2007, NeurIPS 2018. Elected a Technicolor Fellow in 2011. Recipient of the “Grand Prix Scientifique” from the Del Duca Foundation delivered by the French Academy of Sciences in 2017.
Topics of interest
Unsupervised learning, distributed machine learning, modeling and algorithmic design for distributed systems and networks
Project in Prairie
Laurent Massoulié will develop distributed algorithms for learning from data spread over several machines, to efficiently exploit communication resources between data locations, and storage and compute resources at data locations. He will develop efficient algorithms for unsupervised learning from ‘graphical data’. He will also address fairness and privacy challenges of machine learning, in particular in the contexts of recommender systems and matching markets.
Relational, or ‘graphical’ data is becoming ubiquitous (e.g. social / biological / transportation networks, energy grids…). Its treatment calls for new methods to construct and process adequate representations of data points in suitable spaces. There are many important scenarios where data must be distributed on several network locations, e.g. when it is too large to fit on a single machine, or when it can’t leave administrative boundaries due to privacy concerns. New distributed algorithms, and possibly new network architectures are needed for efficient learning from data distributed over a network.