معرفی
Jing Yang is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Virginia, with a secondary appointment (by courtesy) in the Department of Computer Science. Previously, she was an Assistant and then tenured Associate Professor at the Pennsylvania State University.
Her educational background includes a B.S. from the University of Science and Technology of China (USTC), and M.S. and Ph.D. degrees from the University of Maryland, College Park, all in Electrical Engineering.
Dr. Yang's research spans machine learning, wireless communications and networking, and information theory. Her current focus includes transformers and large language models (LLMs), multi-armed bandits and reinforcement learning, privacy-preserving machine learning, federated learning and distributed/decentralized learning, and machine learning applications in wireless communications. She has developed innovative approaches that bridge theoretical foundations with practical wireless networking applications, particularly in the areas of timely data transmission and resource optimization.
Her recent publications demonstrate a strong trend toward integrating large language models with wireless communication systems, developing privacy-preserving federated learning techniques, and advancing theoretical understanding of reinforcement learning algorithms. Her work consistently addresses fundamental challenges in information theory while developing practical solutions for next-generation wireless networks.
- NSF CAREER award 2015
- WICE Early Achievement Award 2020
- IEEE TCCN Exemplary Editor Award 2024
- N2Women: Stars in Computer Networking and Communications 2020
Dr. Yang has advised numerous PhD students who have gone on to successful careers in academia and industry. Her research is supported by multiple NSF grants including the CAREER award, as well as collaborations with Intel and the Department of Energy. She leads projects focused on when next-generation wireless networks meet machine learning, timely computing and learning over communication networks, and distributed differentially private data synthesis.
Her research group at UVA focuses on developing theoretical foundations and practical algorithms for machine learning in wireless networks, with particular emphasis on privacy-preserving techniques, reinforcement learning applications, and efficient transformer implementations for communication systems.
