Carson is a Principal Applied Scientist at Amazon. His research interests are in sequential decision-making in both physical and digital domains. Much of his work focuses on deep reinforcement learning for supply chain optimization, including alternatives to model predictive control and coordination between agents in large-scale systems. More recently, he has been exploring how large language models can be made to act reliably in the world, with work on self-improvement and verified code synthesis. Previously, he developed deep learning models for probabilistic time-series forecasting.

Before joining Amazon, Carson received his PhD in June 2019 from the ORFE department at Princeton University. While at Princeton, he was advised by Han Liu and his research focused on optimization, as applied to high-dimensional inference problems and stochastic decision-making. From September 2017 until August 2018, Carson interned at the Tencent AI Lab in Bellevue, Washington.