Daniel Mankowitz is a Staff Research Scientist at Google Deepmind, working on solving the key challenges in Reinforcement Learning algorithms that unlock real-world applications at scale. This includes a focus on Reinforcement Learning from Human Feedback (RLHF) in the context of Large Language Models (LLMs). Daniel has worked on: code optimization, code generation, video compression, recommender systems, and controlling physical systems such as Heating Ventilation and Air-Conditioning (HVAC), with publications in Nature and Science.