Huaiyu Dai

Biography

Huaiyu Dai received the B.E. and M.S. degrees from Tsinghua University, Beijing, China, in 1996 and 1998, respectively, and the Ph.D. degree from Princeton University, Princeton, NJ in 2002, all in Electrical Engineering.

He has been with NC State University since 2003, now holding the position of Professor and the title of University Faculty Scholar. His research interests include signal processing for communications and networking, network security, and machine learning, with over 300 peer-reviewed journal/conference papers published.

He has served as an Area Editor for IEEE Transactions on Communications, a member of the Executive Editorial Committee for IEEE Transactions on Wireless Communications (TWC), and an Editor for IEEE Transactions on Signal Processing. Currently he serves as the Editor-in-Chief for IEEE Transactions on Signal and Information Processing over Networks. He has been an Area TPC Chair for IEEE International Conference on Computer Communications (INFOCOM) since 2018. Previously, he served as a symposium Co-Chair multiple times for IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS), IEEE International Conference on Communications (ICC), and IEEE Global Communications Conference (GLOBECOM). He received Qualcomm Faculty Award, and several best paper awards at IEEE MASS, ICC, and INFOCOM BIGSECURITY Workshop. He is a Fellow of IEEE and Asia-Pacific Artificial Intelligence Association.

Education

• Ph.D. 2002
  Electrical Engineering
  Princeton University, NJ
• Master's 1998
  Electrical Engineering
  Tsinghua University, Beijing
• Bachelor's 1996
  Electrical Engineering
  Tsinghua University, Beijing

Recent Publications

• Reinforcement Learning-Based Personalized Differentially Private Federated Learning (2024)
• Accelerating the Delivery of Data Services over Uncertain Mobile Crowdsensing Networks (2024)
• Dynamic Power Control for Delay-Optimal Coded Edge Computing (2023)
• Efficient Content Delivery in User-Centric and Cache-Enabled Vehicular Edge Networks with Deadline-Constrained Heterogeneous Demands (2023)
• GA-DRL: Graph Neural Network-Augmented Deep Reinforcement Learning for DAG Task Scheduling Over Dynamic Vehicular Clouds (2024)
• Hierarchical Federated Learning in Wireless Networks: Pruning Tackles Bandwidth Scarcity and System Heterogeneity (2024)
• Joint Optimization of Charging Station Placement and UAV Trajectory for Fresh Data Collection (2024)
• Magnitude Matters: Fixing signSGD Through Magnitude-Aware Sparsification and Error Feedback in the Presence of Data Heterogeneity (2024)
• Privacy-Preserving Decentralized Inference With Graph Neural Networks in Wireless Networks (2023)
• Real-time and Low-Overhead Graph Task Scheduling over Vehicular Computing-assisted Edge Networks (2024)