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Christian Gagné

Appointment

Canada CIFAR AI Chair

Pan-Canadian AI Strategy

Connect

Université Laval

Google Scholar

About

Christian Gagné is a Canada CIFAR AI Chair at Mila and a full professor in the Department of Electrical and Computer Engineering and the director of Institute Intelligence and Data (IDD) at Université Laval.

Gagné’s research interests include method development for machine learning and stochastic optimization. In particular, he is interested in deep neural networks, learning and transfer of representations, meta-learning as well as multitask learning. He is also interested in optimization approaches based on probabilistic models as well as evolutionary algorithms, involving, for example, black box optimization, and automatic programming.

Awards

  • Best Paper Award, Canadian AI, 2020
  • Best Paper Award, GECCO, 2009
  • Best Paper Award, GECCO, 2002

Relevant Publications

  • Bouchard, C., Wiesner, T., Deschênes, A., Lavoie-Cardinal, F., & Gagné, C. (2021). Task-Assisted GAN for Resolution Enhancement and Modality Translation in Fluorescence Microscopy.

  • Shui, C., Li, Z., Li, J., Gagné, C., Ling, C., & Wang, B. (2021). Aggregating From Multiple Target-Shifted Sources.

  • Shui, C., Wang, B., & Gagné, C. (2021). On the benefits of representation regularization in invariance based domain generalization.

  • Gagné, C., Zeng, M., & Rusch, L. A. (2021). Recurrent neural networks achieving MLSE performance for optical channel equalization. Optics Express, 29(9), 13033-13047.

  • Li, Z., Gagné, C., Ling, C., & Wang, B. (2020). Unified Principles For Multi-Source Transfer Learning Under Label Shifts.

Institution

Mila

Université Laval

Department

Electrical and Computer Engineering

Education

  • PhD (Electrical Engineering), Université Laval

Country

Canada

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