I am Luis A. Briceno-Mena, a researcher working at the intersection of materials science, process systems engineering, and artificial intelligence (AI). My work focuses on developing scientific AI methods that combine physics and chemistry 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 simulations 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.