I am Luis A. Briceno-Mena, a researcher working at the intersection of materials science, process systems engineering, and artificial intelligence. My work focuses on developing physics-informed AI methods that combine scientific principles with data-driven modeling to accelerate the design and manufacturing of advanced materials. I am particularly interested in computational frameworks that link high fidelity simulations, process data, and machine learning to create predictive and interpretable models for polymers, formulated products, and sustainable manufacturing systems. I am also interested in how multimodal learning and physics-aware representations can guide materials discovery and ensure reliable, scalable production.

Areas of interest

1. Physics-Informed Artificial Intelligence – Developing AI and machine learning models that embed physical and chemical principles to ensure interpretability, feasibility, and safe extrapolation in materials and process modeling.
2. Multiscale Simulation – Integrating high fidelity simulatoins and plant-scale process data into unified frameworks for predictive design and optimization of materials and manufacturing systems.
3. Data Representations and Multimodal Learning – Creating computational representations that capture the complexity of polymers and formulated products through graphs, signals, and images to enable data fusion and discovery.
4. Sustainable and Intelligent Manufacturing – Applying AI-driven process systems engineering to enhance energy efficiency, circularity, and resilience in industrial manufacturing and materials production.