Portrait of Pierre-Luc St-Charles

Pierre-Luc St-Charles

pl.stcharles@mila.quebec
Senior Applied Research Scientist, Safe AI for Humanity

Biography

Pierre-Luc St-Charles is an applied researcher and developer specializing in machine learning and computer vision, with over a decade of experience bridging research and industry since 2011. He has held research roles at the Computer Research Institute of Montréal and senior research roles at Mila, collaborating extensively with industrial partners and multidisciplinary academic teams on innovative projects in natural resources, transportation, digital media, document intelligence, and earth observation. Pierre-Luc earned his PhD in Computer Vision from Polytechnique Montréal, receiving the departmental Best Thesis Award. Since 2024, he has joined LawZero, a Mila-incubated organization focused on developing safe AI technologies, underscoring his commitment to impactful and responsible AI innovation.

Publications

Multi-Image Super-Resolution for Remote Sensing using Deep Recurrent Networks
Md Rifat Arefin
Vincent Michalski
Pierre-Luc St-Charles
Alfredo Kalaitzis
Sookyung Kim
Samira Ebrahimi Kahou
Yoshua Bengio
High-resolution satellite imagery is critical for various earth observation applications related to environment monitoring, geoscience, fore… (see more)casting, and land use analysis. However, the acquisition cost of such high-quality imagery due to the scarcity of providers and needs for high-frequency revisits restricts its accessibility in many fields. In this work, we present a data-driven, multi-image super resolution approach to alleviate these problems. Our approach is based on an end-to-end deep neural network that consists of an encoder, a fusion module, and a decoder. The encoder extracts co-registered highly efficient feature representations from low-resolution images of a scene. A Gated Re-current Unit (GRU)-based module acts as the fusion module, aggregating features into a combined representation. Finally, a decoder reconstructs the super-resolved image. The proposed model is evaluated on the PROBA-V dataset released in a recent competition held by the European Space Agency. Our results show that it performs among the top contenders and offers a new practical solution for real-world applications.
2020-06-01
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (published)
doi.org