Our Research Areas

Sustainable Process Design, Integration, Optimization, and Control

Focusing on environmentally friendly and resource-efficient designs to minimize waste and reduce emissions in manufacturing.

Hybrid Mechanistic Data-driven Modeling

Combining physics-based models with data-driven approaches to improve predictive accuracy and decision-making in complex systems.

Blockchains and Game-theoretic approaches for resilient Supply Chains

Using advanced machine learning algorithms and artificial intelligence to optimize processes, enhance performance, and reduce costs.

Quantum Mixed Integer and Constrained Optimization, Hybrid Quantum/Classical Computing for Optimization

Developing secure and transparent blockchain solutions to streamline supply chains and enhance data integrity in Industry 4.0 applications.