Jeremy Coulson

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About

Jeremy Coulson is a rising researcher in control theory and data-driven optimization, with a focus on developing robust and safe algorithms for unknown stochastic systems. His primary research areas include distributionally robust optimization, chance-constrained control, and data-enabled predictive control (DeePC). Coulson’s major contribution lies in bridging the gap between model-based and data-driven control for uncertain environments. In his most cited work, "Distributionally Robust Chance Constrained Data-Enabled Predictive Control" (2021, 34 citations), he introduces a novel framework that combines distributionally robust optimization with data-enabled predictive control to handle finite-time constrained optimal control of unknown stochastic linear time-invariant systems. This approach provides rigorous guarantees on constraint satisfaction under distributional ambiguity, making it highly relevant for safety-critical applications like autonomous systems and robotics. Coulson’s work has been recognized for its theoretical depth and practical potential, earning him a growing citation footprint. His research continues to influence the development of reliable, data-driven control methods that perform well even when system models are unavailable or uncertain.