FAU Researchers Receive NSF CAREER Awards

Two researchers from Florida Atlantic University’s College of Engineering and Computer Science have received the coveted National Science Foundation (NSF) Early Career (CAREER) awards. The CAREER program offers the NSF’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.

The researchers are Xiangnan Zhong, Ph.D., an assistant professor; and Zhen Ni, Ph.D., an assistant professor, both in the Department of Electrical Engineering & Computer Science, who received the NSF CAREER awards to drive the current artificial intelligence (AI) wave.

“Although existing achievements in artificial intelligence and reinforcement learning are exciting, the fundamental research of data aggregation, learning and approximation capability and the performance generalization during uncertainties is not yet fully developed,” said Stella Batalama, Ph.D., dean, College of Engineering and Computer Science. “The prestigious NSF CAREER awards that professors Zhong and Ni have received will help us to close the gap from the current state-of-the-art techniques to the artificial general intelligence that will bring good performance in learning speed, data efficiency, and generalization of the optimization performance. Moreover, the activities resulting from these projects will vigorously contribute to the nation’s artificial intelligence workforce development.”

Zhong has received a $503,000 NSF CAREER grant to investigate the intelligent learning control to enable the cyber physical systems (CPS) with the capabilities of autonomous learning and generalization to rapidly adapt in unknown situations. The results are expected to transform how agents interact in high-dimensional and heterogeneous environments, and therefore could potentially provide in-depth findings for exploring creativity in frontier AI techniques.

Zhong also will develop cooperative learning strategies to share with extended skills to facilitate exploration and prevent agents from getting confused by the action details. In addition, this project will develop self-motivated learning structures to contribute toward the global objectives for team-wide success in a distributed perspective.  

“The integration of research and education plans will prepare our future workforce in the fields of cyber physical systems, AI, learning and control,” said Zhong.

Ni has received a $500,000 NSF CAREER grant for a natural concurrent reinforcement learning framework that has three major advantages over traditional reinforcement learning methods. These include advantages of simultaneously learning multimodal properties of the complex system; structural advantages of using a personalized learning scheme; and implementation advantages of the data-driven sample-efficient design. Within this framework, Ni will design two concurrent reinforcement learning methods to build the learning-in-learning control paradigm. The applications on smart energy community will support the novel learning framework and theoretical results.

“Beyond the scientific impacts, this research has broader impacts for a wide range of research disciplines including transportation, rehabilitation and robotics,” said Ni. “Furthermore, the integration of research and education activities will positively impact institutions both regionally and nationally.”