Our group's research lies at the intersection of chemical engineering, systems engineering, and operations research, and specifically focuses on decision-making in complex systems. We tackle high-impact engineering optimization problems and strive to develop computational solutions that are effective, efficient, and explainable. The figure below provides an overview of our research, which covers the following four major application areas (more details at the links below):
- Enterprise-wide optimization for the process industry
- Sustainable energy and process systems
- Data-driven cellular bioprocess engineering
- Human-AI collaboration for complex decision-making
Our research is highly interdisciplinary, where we routinely collaborate with researchers from other fields, experimentalists, and industry.
Our applications often motivate fundamental research that involves the development of new mathematical abstractions, concepts, models, and algorithms. A major benefit of these developed methods is that they often can be generalized and applied across our four application areas as well as to other related problems, thereby increasing their broader impact. Here, we highlight a few areas where we focus on making such fundamental contributions:
- Decision-making under uncertainty
- Integrated learning and optimization
- Inverse optimization & explainable optimization (more details on the human-AI collaboration page)