AI-Driven Process Optimization 

Our lab leverages artificial intelligence (AI) and machine learning (ML) to accelerate research and innovation across our focus areas, including biomass conversion, algae valorization, environmental technologies, and sustainable materials development. We develop data-driven models to predict process outcomes, optimize experimental conditions, and uncover complex relationships within biological and chemical systems. By integrating AI/ML with experimental approaches, design of experiments, and process engineering principles, we reduce trial-and-error, improve process efficiency, and accelerate technology development and scale-up. Our research includes algorithm development, model training using experimental datasets, and the application of intelligent optimization strategies to enable adaptive and sustainable biomanufacturing systems. 

Students involved: Antonio Franco Aguado, Tianlei Chen, Micah Goff