Director of the Institute Intelligence and Data (IID)
Canada-CIFAR AI Chair, associate member to Mila
Member of CVSL / CeRVIM / BDRC / REPARTI / UNIQUE / VITAM / OBVIA
Full professor at the Electrical Engineering and Computer Engineering Department of
Université Laval

Address:
Electrical Engineering and Computer Engineering Department
Adrien-Pouliot Building
Université Laval
Quebec City (Quebec)  G1V 0A6
Canada

Office: PLT-1138-F
Email: christian.gagne@gel.ulaval.ca
Phone: use email

Biosketch

Christian Gagné is a professor at the Electrical Engineering and Computer Engineering Department of Université Laval since 2008. He is the director of the Institute Intelligence and Data (IID) of l’Université Laval. He holds a Canada-CIFAR Artificial Intelligence Chair and is an associate member to Mila. He is also a member of the Computer Vision and Systems Laboratory (CVSL), a component of the Robotics, Vision and Machine Intelligence Research Centre (CeRVIM), and the Big Data Research Centre (BDRC) of Université Laval. He is also participating to the REPARTI and UNIQUE strategic clusters of the FRQNT, the VITAM FRQS center and the International Observatory on the Societal Impacts of AI (OBVIA).

He completed a PhD in Electrical Engineering (Université Laval) in 2005 and then had a postdoctoral stay jointly at INRIA Saclay (France) and the University of Lausanne (Switzerland) in 2005-2006. He worked as research associate in the industry between 2006 and 2008. He is a member of executive board the ACM Special Interest Group on Evolutionary Computation (SIGEVO) since 2017.

His research interests are on the development of methods for machine learning and stochastic optimization. In particular, he is interested by deep neural networks, representation learning and transfer, meta-learning and multitask learning. He is also interested by optimization approaches based on probabilistic models and evolutionary algorithms for black-box optimization and automatic programming, among others. A significant share of his research work is on the practical use of these techniques in domains such as computer vision, microscopy, health, energy and transportation.

For more details, see the CV.