Scott Fujimoto is a Member of Technical Staff at AMI Labs, where he contributes to developing advanced machine intelligence. His research centers on deep reinforcement learning, particularly off-policy learning, batch reinforcement learning, policy evaluation, and designing algorithms that learn effectively from previously collected data. His publications also extend into sentiment analysis and geometric deep learning.

Before joining AMI Labs, Scott was a Research Scientist at Meta from 2024 to 2026. He previously spent more than five years as a Graduate Research Assistant at Mila—the Quebec Artificial Intelligence Institute—and held research positions at Google and Facebook. Alongside his research, he served for more than six years as a teaching assistant at McGill University, combining extensive academic experience with applied research inside several of the world’s leading artificial-intelligence organizations.

Scott earned his PhD and master’s degree in computer science from McGill University. He also holds a Bachelor of Science in Honors Statistics and Computer Science, with a minor in management, from McGill. This combination of machine learning, mathematics, statistics, and management provides the foundation for his work on technically rigorous and practically relevant AI systems.