Bio

Scott's a PhD student in the engineering department at Cambridge University, researching theoretical and applied reinforcement learning. He works with a US firm called Emerson Electric to build RL agents that can control energy-intensive industrial processes efficiently, drawing power from the grid at optimal times to better match intermittent renewable supply with demand. Doing so requires agents that are capable of learning useful polices in low-data regimes, which creates several interesting theoretical challenges that he believes are best mitigated by model-based RL. More generally, he's interested in probabilistic approaches to machine learning, and the role these techniques can play in climate change mitigation.