Our group operates at the dynamic intersection of Artificial Intelligence, Machine Learning, and Mechanics within Energy and Resources Engineering. We are dedicated to revolutionizing traditional research paradigms by leveraging data-driven and physics-informed AI to solve frontier interdisciplinary challenges across the energy-environment-agriculture nexus.

Core Focus Areas:

• AI-Driven Cross-Scale Modeling: Developing machine learning interatomic potentials and multi-scale computational frameworks to bridge the fundamental gap between atomistic mechanics and macroscopic system performance.
• Intelligent Energy & Resource Engineering: Applying predictive ML and neural networks to optimize complex thermodynamic processes, precision thermochemical conversions, and the advanced design of sustainable materials.
• AI for Environmental & Agricultural Sustainability: Innovating programmable, responsive intelligent agents for targeted nutrient delivery and resource recovery, accelerating the transition toward precision, sustainable agriculture.
• Paradigm-Shifting Nexus Frameworks: Integrating automated machine learning with dynamic life-cycle assessment (LCA) and techno-economic analysis (TEA) to fundamentally transform how complex interdisciplinary systems are designed, evaluated, and deployed.