Gal Dalal is Senior Research Scientist working on Reinforcement Learning (RL) theory and applications at NVIDIA Research. Previously, he co-founded Amooka-AI, which later became Ford Motor Company’s L3 driving policy team. He obtained his BSc in EE from Technion, Israel, summa cum laude, and his PhD from Technion as a recipient of the IBM fellowship. Gal interned at Google DeepMind and IBM Research, and received the 2019 AAAI Best (“outstanding”) Paper Award, ranked 1st among 1150 accepted papers.
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 worked on real-world problems such as autonomous driving, GPU cache control, network congestion control, datacenter cooling, and smart-grid management.
