I’m an AI researcher interested in understanding higher-order cognitive functions and developing AI systems that replicate them. I’m also interested in building a mathematical framework that links structural motifs in neural connectivity to functionality, through the lens of dynamics.
Currently, I’m a Master’s student at Mila and Université de Montréal, supervised by Guillaume Lajoie and Dhanya Sridhar. My research explores how compression functions as a unifying principle underlying various functionalities in both artificial and biological systems, from perception to abstract reasoning. Specifically, I investigate how principles of compression can induce compositional structure in learning systems, enabling robust and efficient generalization.
Previously, I interned as a Machine Learning Engineer at Intellisports Inc., where I focused on human activity recognition using sequence models. I also worked at MiQ, building scalable machine learning pipelines within a big data framework.
Aside from research, I enjoy chess, climbing, snowboarding and playing football.
