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We develop fundamental methodologies and concrete tools of process control and process systems engineering that are capable of sensing, learning, reasoning, and actuating on chemical and physical systems based on observational data and domain knowledge.

The research develops on four foundational pillars:

1. Data
2. Phenomenological and probabilistic modelling
3. Statistical inference/learning
4. Optimal control/decision

The formal framework for modelling and control is given as a model of the system in which the uncertainties associated with our knowledge and the measuring process are clearly stated. An important objective in our research is the design and control of models that capture complex dynamics.