Uncertainty Quantification, Computational Optimization, and Molecular-to-Systems Engineering

Our lab develops novel mathematical modeling and computational frameworks to optimize energy technologies across materials, devices, and systems, and infrastructure length and timescales. This multiscale perspective naturally facilitates both bottom-up and top-down thinking such as:

  • Rapidly assess the potential of new materials to impact devices, systems, and infrastructures.
  • Use infrastructure level goals (e.g., renewable adoption, emission reductions, limited water use, etc.) to set design priorities for material, device, and system-level metrics.
  • Discover new materials, devices, and systems that can help mitigate uncertainty and lead to more resilient infrastructures.

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We are especially interested in paradigms to quantify and propagate uncertainty through multiscale optimization problems. As such, our research is at intersection of engineering, applied mathematics, and computational sciences.


We are looking for outstanding undergraduate researchers, graduate students, and post doctoral scholars.

Interested in mathematical modeling and computer programming? Contact Prof. Dowling to learn more about research projects for ND undergraduate students (especially those majoring in computer science, mathematics, statistics, economics, or ANY engineering discipline).