I am an AI researcher with a PhD awarded in November 2023 from Université Laval, where I worked under the supervision of François Laviolette and Jacques Corbeil, alongside Aix-Marseille Université, under the guidance of Cécile Capponi. My doctoral research focused on discovering biomarkers by applying advanced machine learning techniques to biological data, with a strong emphasis on developing novel ensemble methods and greedy optimization algorithms. This work has contributed to advancing computational methods in biomarker identification, enhancing the precision and interpretability of biological data analysis.

Currently, I am a research scientist at Cortaix Labs, where my work centers on the explainability and trustworthiness of deep neural networks (DNNs), with a particular focus on DNN compression techniques for computer vision applications. In this role, I aim to bridge the gap between the powerful capabilities of deep learning models and the need for transparent, reliable, and efficient solutions. My ongoing research explores innovative methods for making DNNs more interpretable, accountable, and applicable in real-world settings, ensuring both performance and trust in AI-driven technologies.

Proud developer for the multi-learn GitHub organization, and active member of the Corbeil Lab machine learning experts team.