Simon Lacoste-Julien



  • Associate Fellow
  • Learning in Machines & Brains


  • Université de Montréal
Department of Computer Science and Operations Research


  • Canada


PhD (Computer Science), University of California, Berkeley
BSc (Joint Honours Mathematics and Physics, Joint Honours Mathematics and Computer Science), McGill University


Simon Lacoste-Julien’s research focuses on machine learning, i.e., how to program a computer to learn from data and solve useful tasks.

His primary research goal is to develop and analyze machine learning techniques that can exploit, at large scale, the rich structure of data in interdisciplinary applications such as natural language processing, information retrieval, computer vision and computational biology. To this end, he combines tools from optimization, statistics and computer science, and particularly enjoys working at the interface between domains. Lacoste-Julien is best known for his contributions in three areas: structured prediction (classification problems where the outputs are structured objects such as sequences or graphs); large scale optimization (incremental gradient method and Frank-Wolfe optimization); and the combination of generative and discriminative methods.


NSERC Discovery Grant, 2017

Google Focused Research Award, 2016

Wolfson College Junior Research Fellowship, University of Cambridge, 2009–11

UC Berkeley College of Engineering Graduate Student Prize, 2008

Relevant Publications

Osokin, A., F. Bach, and S. Lacoste-Julien. "On Structured Prediction Theory with Calibrated Convex Surrogate Losses." Paper presented at NIPS conference, Long Beach, 2017.

Lacoste-Julien, S., and M. Jaggi. "On the Global Linear Convergence of Frank-Wolfe Optimization Variants." Paper presented at NIPS conference, Montreal, 2015.

Defazio, A., F. Bach, and S. Lacoste-Julien. "SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives." Paper presented at NIPS conference, Montreal, 2014.

Lacoste-Julien, S. et al. "Block-Coordinate Frank-Wolfe Optimization for Structural SVMs." Paper presented at ICML conference, Atlantia, GA, 2013.

Lacoste-Julien, S., F. Huszár, and Z. Ghahramani. "Approximate Inference for the Loss-Calibrated Bayesian." Paper presented at AISTATS conference, Fort Lauderdale, FL, 2011.