Xiao Liu, assistant professor of industrial engineering, received a CAREER award from the National Science Foundation. The Faculty Early Career Development (CAREER) Program offers the foundation’s most prestigious awards in support of early-career faculty who have the potential to serve as academic role models in research and education and to lead advances in the mission of their department or organization.
His project, entitled “Domain-Aware Statistical Learning,” will contribute to the advancement of national competitiveness by transforming how governing physics and engineering domain knowledge is integrated into data-driven models for high-stakes applications. High-stakes engineering applications require interpretable models and explainable decisions.
As fundamental governing physics imposes critical constraints on how data should be modeled and how models can be interpreted, the integration of governing physics into data-driven models becomes an essential capability that will significantly accelerate the penetration of data science into a wide range of domain-knowledge-intensive applications, such as energy infrastructure, aviation safety and manufacturing. In these environments, the old paradigm of “letting the data speak for themselves” is being replaced by the capability of “letting the data speak based on the laws of physics and engineering.”
This project will address the development of methods to integrate data with physics-based models in three main use cases, namely environmental processes to enhance resilience of our national utilities during extreme events intensified by climate change, thermal modeling to improve energy efficiency in data center operations and structural dynamics to enhance aviation safety in an increasingly crowded airspace.
The accompanying educational plan aims to address the gaps between general-purpose data science education at the school and university level and the specific needs for next-generation engineering students with diverse backgrounds. The educational plan also aims to improve data literacy among the general public by improving awareness of the increasing availability of data and the capability of interpreting those data through local community activities.
“The lack of explainable models and actionable insights has become the main barrier that impedes the penetration of black-box data-driven approaches into high-stakes engineering applications,” Liu said. “The long-term vision is to break that barrier by creating a domain-aware statistical learning paradigm that enables the direct embedding of fundamental governing physics into interpretable data-driven models and nurturing next-generation engineers who apply analytics tools in domain-knowledge-intensive environments.”
“We are excited to see Dr. Liu’s work recognized by the National Science Foundation with the foundation’s most prestigious award for junior faculty,” said Ed Pohl, department head. “This grant helps build the foundation for integrating education and research throughout his career. His work associated with creating explainable models and actionable insight will have a significant impact in helping us better design, analyze and operate complex engineering systems in the future. We are extremely proud of him and are fortunate to have him as member of our team.”
Liu received the Rising Star Award from the College of Engineering in Spring 2022. “It’s no surprise to learn that the National Science Foundation has recognized the importance and impact of Xiao’s research with this prestigious early career award,” said Kim Needy, dean of the College of Engineering. “This funding helps cement Xiao’s status as a rising research star in industrial engineering.”