Shadan Golestan is a Machine Learning Scientist with the Advanced Tech team at Amii, where he researches and designs machine learning algorithms to solve real-world problems. His research primarily focuses on designing agents via reinforcement learning, Bayesian optimization, and large language models to solve sequential decision-making problems, such as system optimization and machine/robot adaptation techniques in real-world applications.

Prior to working at Amii, Shadan worked as a postdoctoral fellow with Amii’s Osmar Zaiane at the Computing Science department of the University of Alberta. He also brings industry experience from his time working with ShopHopper and Visier.

Shadan earned his PhD from the Computing Science department of the University of Alberta. His thesis Simulation-based Sensor Configuration Optimization to Detect Human Activities in Smart Indoor Spaces investigated the intersection of Bayesian Optimization and simulation methodologies, laying the groundwork for innovative approaches in AI-driven decision-making.

In his free time, Shadan enjoys watching NBA games, as well as playing basketball, guitar, and video games.