Gal Dalal

Gal Dalal was previously a research scientist at Ford Motor Company, developing Ford’s Reinforcement-Learning (RL) based driving policy for autonomous vehicles. He received his BSc in Electrical Engineering from Technion, Israel, summa cum laude. Directly after, he obtained his PhD from Technion as a recipient of the IBM PhD fellowship, advised by Prof. Shie Mannor. During his studies, Gal interned at IBM Research and Google DeepMind. Recently, he received the 2019 AAAI Best (“outstanding”) Paper Award.

Gal’s research interests span over both RL theory and applications. His theory work includes time-delayed decision making, multi-step greedy policies, and finite-time convergence of RL algorithms. On the application side, he focused on real-world problems such as autonomous driving, datacenter cooling, and hierarchical smart grid management.