Chicheng Zhang receives NSF CAREER Award for advancing responsible and efficient interactive AI systems

Dr. Chicheng Zhang, an assistant professor in the Department of Computer Science at the University of Arizona, has received a prestigious National Science Foundation CAREER Award to support his groundbreaking work on interactive machine learning — a class of artificial intelligence (AI) systems that learn by engaging directly with the world around them.

Unlike traditional machine learning that rely on static datasets, interactive learning agents gather data through adaptive experimentation, making them well-suited for real-world applications where collecting data is costly or safety-critical — such as autonomous driving and human-facing AI systems like chatbots.

Dr. Zhang’s project, titled, “Foundations of Interactive Machine Learning with Rich Feedback,” seeks to tackle several persistent challenges in the field: improving data efficiency, ensuring safety, and enabling the reuse of data collected by learning systems. His team will develop algorithms that not only come with robust theoretical guarantees but also perform better under real-world constraints.

“I am grateful to the NSF for supporting my research group’s efforts to understand and harness the power of interaction in machine learning," said Dr. Zhang. "I’m also excited about the possibility to translate the theoretical insights we develop into practical algorithms for real-world applications, including those in robotics and wireless communications where I have active collaborations.”

The project’s research is organized around three major themes. The first focuses on interactive learning for single-step decision making, specifically active learning — where algorithms intelligently choose what data to request — particularly in scenarios involving complex models such as neural networks. The second thrust addresses interactive learning for sequential decision-making, developing safer and more reliable methods for AI systems to learn from human demonstrations and interventions. The third seeks to bridge the gap between interactive learning and conventional offline learning, by enabling systems to effectively reuse past data, a key step toward building AI that improves continually over time.

In addition to research, the award supports a broad education and outreach plan. Zhang will lead an intramural lecture series in partnership with the University of Arizona DataLab, host middle school outreach events through the Department of Computer Science’s Ambassadors Program, and mentor undergraduate students through hands-on research experiences and curriculum innovation.

"NSF CAREER Awards are extremely competitive and very prestigious, so Dr. Zhang's CAREER Award reflects the high regard that he has achieved as a well-respected and impactful researcher in the machine learning community,” said Dr. Ellen Riloff, professor and head of Computer Science at U of A. “Dr. Zhang's new research project will develop novel methods in interactive machine learning that will produce principled and practical state-of-the-art techniques for training AI systems efficiently. This award provides valuable support to grow Dr. Zhang's machine learning research lab in the Computer Science department and includes an educational component and outreach plan to encourage students to pursue careers in artificial intelligence and machine learning.”

The NSF CAREER Award is one of the foundation’s most prestigious honors, recognizing early-career faculty who demonstrate the potential to serve as academic role models in research and education. Zhang joins a growing list of awardees at the University of Arizona and College of Science who are driving innovation at the intersection of computing, science, and society.

Learn more about Dr. Zhang and his research here.