Three KU Engineering Assistant Professors Earn CAREER Awards
The National Science Foundation’s (NSF) Early Career Development Program (CAREER) award is one of the nation’s highest honors for early-career faculty. Currently, 11 of KU Engineering’s 20 assistant professors hold CAREER Awards.
Each award runs for five years and is approximately $500,000.
Ana Morais, assistant professor of chemical and petroleum engineering
Morais’ research is focused on improving how difficult-to-recycle plastics can be converted into useful products while advancing broader sustainability efforts through education and outreach.
Her CAREER project centers on polypropylene, a plastic commonly used in products ranging from food packaging to automotive parts. Despite being produced in massive quantities, less than 1% of polypropylene waste is recycled in the United States. Over the next several years, Morais said she hopes the research will expand into a broader framework for improving plastic recycling and other complex material-conversion processes.
Shumaiya Shomaji, assistant professor of electrical engineering and computer science
Shomaji’s project focuses on supply chain security. With products such as computer chips, medicines and food coming from complex supply chains involving many different suppliers, it can be difficult to verify where items come from and whether they are genuine. Additionally, counterfeit products can threaten public safety, health and national security.
Shomaji’s research aims to develop a secure and scalable system that can reliably track and authenticate products and individuals, helping organizations quickly verify identities, detect counterfeit items and improve trust across large supply chains and security-critical applications.
Zijun Yao, assistant professor of electrical engineering and computer science
Yao‘s project develops an integrated machine learning framework for longitudinal healthcare intelligence. Currently, medical AI systems often struggle to process complex patient records and can become highly unreliable when faced with noisy, real-world data. Because flawed algorithms can produce recommendations that conflict with established clinical knowledge, they pose direct risks that prevent doctors from safely using them in high-stakes environments.
To overcome these barriers, this project will significantly improve AI dependability by advancing how systems represent patient histories, aligning algorithmic reasoning with medical principles, and fortifying tools against unpredictable vulnerabilities. Ultimately, this robust framework will advance public health and assist doctors in providing more effective, personalized patient care.