Zhang Wins NSF CAREER Award to Advance the Foundations of Multi-Agent Learning

A University of Maryland expert in machine learning has been awarded funding from the National Science Foundation (NSF) to explore how autonomous systems can learn from each other and make decisions together in complex, real-world environments. 

Kaiqing Zhang, an assistant professor of electrical and computer engineering and an affiliate assistant professor of computer science, is the principal investigator of a National Science Foundation Faculty Early Career Development Program (CAREER) award, which provides approximately $540,000 in funding over five years.

This highly competitive award, one of NSF’s most prestigious for early-career faculty, recognizes researchers with the potential to serve as academic role models and drive advances in their fields.

Zhang holds an affiliate appointment in the University of Maryland Institute for Advanced Computer Studies and is a core faculty member in the University of Maryland Center for Machine Learning.

His project, “Foundations of Dynamic Multi-Agent Learning Under Information Constraints,” seeks to address a critical gap in understanding how multiple autonomous AI agents—such as self-driving cars, robots, and smart grid systems—can learn and collaborate in real-world settings. 

These AI agents often operate with limited, noisy or delayed information, unlike traditional models that assume full knowledge of the environment. Zhang aims to develop strategies that help agents navigate uncertainty and make better decisions with only partial information.

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