My research combines machine learning (ML) and uncertainty quantification (UQ) for computational and data-enabled science and engineering (CDS&E).

I am interested in developing fast, reliable, and interpretable methods for data-driven engineering and uncertainty quantification, with applications in structural digital twins, advanced materials and manufacturing, and quantitative sustainability and resilience. See my research overview.

My previous work concerns the use of game theory and mechanism design in engineering and social systems, especially in urban transportation.

My curriculum vitae.