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Simon Lacoste-Julien

Appointment

Associate Fellow

Canada CIFAR AI Chair

Learning in Machines & Brains

Pan-Canadian AI Strategy

Connect

Université de Montréal

Google Scholar

About

Simon Lacoste-Julien is the associate director of the CIFAR Learning in Machines & Brains program and  Canada CIFAR AI Chair at Mila. He is an associate professor at the Department of Computer Science and Operations Research (DIRO) at Université de Montréal and is the part-time VP Lab Director at the Samsung SAIT AI Lab in Montreal.

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.

Awards

  • Google Focused Research Award, 2016
  • NIPS Best Reviewer Award, 2015
  • Google Faculty Research Award, 2015
  • Research in Paris Fellowship, City of Paris, 2011-2012
  • Wolfson College Junior Research Fellowship, University of Cambridge, 2009-2011

Relevant Publications

  • Yaakoubi, Y., Soumis, F., & Lacoste-Julien, S. (2021). Structured Convolutional Kernel Networks for Airline Crew Scheduling.

  • Kwon, N., Na, H., Huang, G., & Lacoste-Julien, S. (2021). Repurposing Pretrained Models for Robust Out-of-domain Few-Shot Learning.

  • Larsen, E., Lachapelle, S., Bengio, Y., Frejinger, E., Lacoste-Julien, S., & Lodi, A. (2018). Predicting tactical solutions to operational planning problems under imperfect information.

  • Arpit, D., Jastrzębski, S., Ballas, N., Krueger, D., Bengio, E., Kanwal, M. S., … & Lacoste-Julien, S. (2017, July). A closer look at memorization in deep networks. In International Conference on Machine Learning (pp. 233-242). PMLR.

  • Defazio, A., Bach, F., & Lacoste-Julien, S. (2014). SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives. In Advances in neural information processing systems (pp. 1646-1654).

Institution

Mila

Samsung SAIT AI Lab in Montreal

Université de Montréal

Department

Computer Science and Operations Research (DIRO)

Education

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

Country

Canada

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