Gabriel D. Patrón

My research group works at the intersection of process systems engineering and interpretable machine learning. We aim to mathematically model novel process systems, develop algorithms for autonomous process operation, and computationally design new processes and chemicals. We use tools like neural networks (e.g., quantile and physics-informed), decision trees, Bayesian and classical optimization, and process control to solve problems in sustainability, energy systems, electrification, and nanomedicine applications. Current and Upcoming Research Projects: Symbolic regression for real-time optimization of continuous processes. Physics-informed neural network-based control of semi-batch processes. Neural network surrogates for stochastic optimization of electrified processes. Learnable and interpretable decision-making for chemical plant planning.