I make systems that learn in real-time from experience. Currently, I work in a small team led by John Carmack. Previously, I developed efficient reinforcement learning algorithms with Richard S. Sutton. I also participated in at the 55th International Mathematical Olympiad (Honorable-mention), and the XXVI Asian Pacific Mathematical Olympiad (Bronze Medal).

My research is driven by the big world hypothesis (Javed & Sutton, 2024) [PDF, Talk], which is the idea that no matter how large and complex our agents become, they will always be small compared to the world they interact with. Some consequences of the big world hypothesis are that no amount of prior learning is sufficient, continual learning is the only way to maintain strong performance, and computationally efficient learning algorithms are essential.