Research

We aim to develop advanced methodologies for intelligent and autonomous process systems by integrating data-driven intelligence with first-principles understanding. Our work focuses on building rigorous, uncertainty-aware models and decision-making algorithms that can learn from operations, generalize across conditions, and adapt to evolving objectives and constraints. By bridging modeling and optimization with real-time control, we pursue scalable frameworks that coordinate decisions across multiple time and spatial scales and enable safer, more efficient, and more sustainable process operation in dynamic industrial environments.

 
Link:

• Process AI

• Process Modeling and Optimization

• Process Control and Scheduling