Integration of artificial intelligence and machine learning with traditional powder processing models represents a paradigm shift in process development. By creating intelligent systems that learn from historical data and real-time measurements, we can develop self-optimizing processes that adapt to raw material variability.

Developing comprehensive digital representations of powder processing systems that enable manufacturers to test process modifications, evaluate new formulations, and train operators in risk-free virtual environments. My research aims to create frameworks for validating these digital twins across full operating conditions.

Developing energy-efficient, waste-minimizing powder processing technologies including novel granulation techniques requiring less liquid binder, ambient condition processes, and closed-loop systems that recycle process streams.