I am a PhD Candidate at Mila-Quebec AI Institute and Université Laval (IID/LSVN lab). I work in the area of Adversarial Machine Learning under the supervision of Prof. Christian Gagné (Mila & Université Laval) and co-supervised by Prof. Frédéric Precioso (INRIA & Université Côte d’Azur), in close collaboration with Yann Pequignot. I was previously a Google AI resident at the Accra Lab where I was mentored by Yann Dauphin. I’m currently a visiting student researcher at Stanford Artificial Intelligence Lab, in the Stanford Trustworthy AI Research group (STAIR) led by Prof. Sanmi Koyejo.

Research Interests

My research aims to build safe, efficient and trustworthy machine learning systems that are reliable when deployed in the real world. In particular, I work on:

• Robustness & Safety: adversarial robustness, robust fine-tuning, robustness to distribution shifts, and the safety and alignment of foundation models (LLMs/VLMs)
• Uncertainty & Data Efficiency: uncertainty estimation, test-time scaling and adaptation, and active learning
• Science of Deep Learning: understanding training dynamics and generalization, including grokking and emergent behaviours

Highlights/News

• Jan 2026 — Our work Robust Fine-Tuning with Epsilon-Scheduling is accepted at ICLR 2026!

• Dec 2025 — Presented two papers at the Reliable ML workshop at NeurIPS 2025:
  • Robust Fine-Tuning from Non-Robust Pretrained Models: Mitigating Suboptimal Transfer With Adversarial Scheduling
  • A Guide to Robust Generalization: The Impact of Architecture, Pre-training, and Optimization Strategy

• Sep 2025 — Excited to join Stanford University this Fall 2025 as a Visiting Student Researcher at the STAIR Lab led by Prof. Sanmi Koyejo!

• Jul 2025 — Attending DLRL 2025, the Deep Learning & Reinforcement Learning Summer School in Edmonton!

• Dec 2024 — Panelist for the 8th Annual Black in AI Workshop at NeurIPS around the theme “AI Regulation & Fairness in the Generative AI Era.”

• Dec 2024 — Neptune.ai Neurips 2024 Paper Communication Challenge (Winner): Video Link

• Nov 2024 — Won a best poster award at the “1ère Journée scientifique de l’IID”!

• Sep 2024 — Our paper on “Margin Consistency” is accepted at Neurips 2024! (Twitter thread)

• Appeared in AIMS Alumni of the Week. Read more about it here: (link)

• In the Acknowledgments of the book “Mathematics for Machine Learning” by Prof. Marc Deisenroth, published in 2020. Check out the book website: (link)

• AMMI Pioneers video and article in the magazine Jeune Afrique about the launch of the Machine Intelligence Master’s program (AMMI 2018) in Rwanda.