Integrating Domain Knowledge into Statistical Inference

Funding sources: NSF CAREER ACI-1350374 (PI: Linwei Wang)

Scientific research across many domains has been increasingly enabled by paralleled advances in two broad disciplines: physics-based mathematical modeling that supports quantitative, multi-scale, and multi-physics simulation of the behavior and mechanism of complex systems, and modern sensor technologies that continuously improve the quantity and quality of measurement data available for analysis. However, the current state of computer modeling is generally decoupled from specific measurements of an individual system, and individualized data analysis often struggles for realistic domain contexts. This gap is ubiquitous in many science and engineering domains.

Supported through an NSF CAREER grant,  we develop theoretical and mathematical foundations that support the integration of physics-based modeling and data-driven inference methods to improve individualized assessment of systems. Our interests in particular focus on data-driven identification and adaptation of the errors in the physics-based models in the statistical inference process. 

Student members: Sandesh Ghimire, Jwala Dhamala, Jingjia Xu (alumni)