I am a Ph.D. candidate in Computer Science at Université de Montréal and Mila - Quebec AI Institute, advised by Ioannis Mitliagkas.
My research focuses on large-scale optimization and distributed training for deep learning, with an emphasis on high-performance computing and the efficient training of large language models. I am particularly interested in semi-synchronous and large-batch training regimes, including critical batch size analysis, as well as non-Euclidean optimization and the design of scalable optimization algorithms.
Previously, I worked on out-of-distribution generalization and confidence calibration, and investigated optimization dynamics in generative models. More recently, my research has expanded to efficient fine-tuning and fairness in large language models, bridging theoretical insights with practical large-scale systems.
I most recently completed a research internship at Meta Superintelligence Lab - Infra (Menlo Park), working on large-batch training and optimization for foundation models. I have also been a Student Researcher at Google DeepMind (Mountain View) and a research intern at Microsoft Research (Redmond).
I am a recipient of the Masason Foundation Fellowship and the RBC Borealis Fellowship. I received my B.Sc. (2017) and M.Sc. (2019) from the Tokyo Institute of Technology, graduating as Valedictorian.