Research

Our group applies mathematical modeling, computational optimization, and uncertainty quantification paradigms to several chemical engineering application related to sustainable energy. Our work spans diverse length and timescales and is highly collaborative.

Uncertainty Quantification, Machine Learning, and Optimization under Uncertainty

 

Uncertainty Quantification and Optimization with Bayesian Hybrid Models

Uncertainty Quantification and Optimization with Bayesian Hybrid Models

Bayesian hybrid models combine physics-based equations with machine learning constructs to correct systematic biases (epistemic uncertainty).

 

Molecular-to-Systems Engineering