Welcome to my personal website ! I am a PhD student at Mila – Université de Montréal. I am supervised by Ioannis Mitliagkas and Irina Rish, and work with many wonderful collaborators on a wide variety of topics, such as optimisation, out-of-distribution generalisation and adversarial robustness, reinforcement learning, continual learning, etc. I am part of an effort led by Irina Rish to work on large language models (LLMs) as part of the 2023 INCITE allocation program by the US Department of Energy, which has allowed me to gain experience training LLMs from scratch. Generally, the common theme of my research during my PhD has been addressing distributional shifts, which is important for fair, safe and trustworthy machine learning algorithms.

I have also worked with Apple, Microsoft, AMD, Staples, Optina Diagnostics and Bosch as a consultant to supervise and mentor research projects in multimodal machine learning, speech recognition, reinforcement learning, image generation, computer vision, large language models and foundation models for scene understanding. Additionally, I have consulted for Blackbox AI, a coding assistant company.

Before my PhD, I have studied human-computer interaction in the Four Eyes lab at the University of California, Santa Barbara. Working with a multidisciplinary team, I led a research project that highlighted how beneficial Augmented Reality (AR) can be for language learning. I also worked on projects aiming to measure how AR affects the perception of user recommendations. Before that, during my BSc, I was enrolled in the Joint Honours Mathematics and Physics program at McGill University, where I did research in theoretical physics (cosmology and string theory) with Robert Brandenberger and Keshav Dasgupta.

As you might have guessed, I really enjoy all kinds of problem solving, and am always happy to tackle problems in new areas of research.

NEWS: I am currently looking for an internship host at Google DeepMind.