Joel A. Paulson

Joel A. Paulson works on control systems and optimization methods for engineering and chemical processes, with a strong focus on Bayesian optimization—a technique for efficiently finding good solutions when experiments or simulations are expensive to run. His recent work applies these ideas to self-driving laboratories, molecular simulations, chemical process design, and building energy systems, often combining machine learning with model-based control. Students in this group would likely work on developing optimization and control algorithms and applying them to real-world engineering and chemical systems.

Publication output has grown substantially over the last decade, rising from roughly 10 papers per year in 2017-2019 to a peak of 28 in 2024, with continued high activity (15-18 papers/year) through 2025-2026.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026