Ying Cui named 2026 Hellman Fellow

The fellowship will support Cui’s work on computational methods for trustworthy, risk-aware digital twins that improve the safety and resilience of critical infrastructure.

Ying Cui, an assistant professor in UC Berkeley’s Department of Industrial Engineering and Operations Research, has been named a 2026 Hellman Fellow. The fellowship recognizes promising early-career faculty and provides a $60,000 award to advance their scholarly and creative work. 

Established at UC Berkeley and UC San Diego in 1995, the Society of Hellman Fellows now operates across all 10 University of California campuses. The program supports assistant professors who demonstrate the potential for significant distinction in their fields. At Berkeley, recipients are selected primarily on the quality of their research. 

Cui received the fellowship for her project, “Reliable Diagnostics at Scale: Efficient Computation of Risk-Aware Digital Twins,” which aims to develop next-generation optimization algorithms for digital twins, which are virtual computational models that continuously integrate sensor data to monitor the condition of physical infrastructure. While digital twins have become an important tool for monitoring bridges, aircraft and other critical systems, existing methods often optimize for average operating conditions and can overlook rare but high-consequence failure events. Cui’s research will develop new mathematical and computational techniques that efficiently identify and analyze these extreme scenarios, enabling digital twins to provide more reliable assessments of structural health and resilience. 

Cui’s research has earned significant support and recognition. In 2026, she was awarded a National Science Foundation Faculty Early Career Development, or CAREER, Award for her work on the foundations and algorithms of nonconvex and nonsmooth optimization. Her research is also supported by the National Institutes of Health and includes the development of computational methods for learning from imbalanced biomedical data. Cui has also been appointed the inaugural holder of the Robert and Catherine Cliff Professorship in IEOR, recognizing her contributions to research and scholarship within the department.

The Hellman Fellowship will provide additional support for Cui’s research on scalable optimization and machine learning methods, advancing trustworthy digital twins and data-driven decision-making for critical infrastructure and other complex systems.