Montaser is an Applied Research Scientist in Trust and Safety at Amii, where he focuses on developing safe and reliable AI systems for high-impact applications. His research centers on AI systems that learn from experience rather than static datasets, with a particular emphasis on safety in uncertain and partially observable environments, an area known as safe reinforcement learning. Driven by real-world challenges, Montaser's work aims to ensure that AI systems can be safely and effectively deployed in complex, dynamic settings. He is particularly passionate about bridging theoretical advancements with practical applications to create intelligent systems that are both safe and trustworthy.

Montaser is also a final-year PhD candidate at the University of Alberta, advised by Professor Michael Bowling. His doctoral research explores how autonomous agents can learn to act cautiously when feedback is sparse or unavailable, enabling robust decision-making in novel and unpredictable scenarios. His work has been published in top-tier AI venues.

With over five years of combined experience in industry and academia across Japan and Canada, Montaser brings a global perspective to his work. Prior to his PhD, he worked as an AI Engineer at SonyAI, where he contributed to the development of multi-agent robotic systems. His projects included training agents through self-play and goal-conditioned reinforcement learning, transferring learned behaviors from simulation to real-world robots, and integrating learning with perception and control systems.

Outside of work, Montaser enjoys playing volleyball, cooking, and spending time in nature.