Huaiyu Dai
He/Him/His
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 280 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 an Area Editor for TWC. He has been an Area TPC Chair for IEEE International Conference on Computer Communications (INFOCOM) for 7 years. 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
- Accelerating the Delivery of Data Services over Uncertain Mobile Crowdsensing Networks (2024)
- 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)
- Sign-Based Gradient Descent With Heterogeneous Data: Convergence and Byzantine Resilience (2024)
- Task-Decoding Assisted Cooperative Transmission for Coded Edge Computing (2024)
- Unleashing the Potential of Stage-Wise Decision-Making in Scheduling of Graph-Structured Tasks over Mobile Vehicular Clouds (2024)
- Base Station Antenna Uptilt Optimization for Cellular-Connected Drone Corridors (2023)
- Blind Post-Decision State-Based Reinforcement Learning for Intelligent IoT (2023)
Expert In
Security , Machine Learning , Generative AI
Involvement
-
National Science Foundation
Program Director
Highlighted Awards
- University Faculty Scholars (2018)
Awards & Honors
- 2025 - Editor in Chief, IEEE Transactions of Signal and Information Processing over Networks
- 2024 - IEEE Communications Society William R. Bennett Prize (Best Paper Award, IEEE/ACM Transactions on Networking)
- 2022 - Area Editor, IEEE Transactions on Wireless Communications
- 2021 - Asia-Pacific Artificial Intelligence Association (AAIA) Fellow
- 2019 - Qualcomm Faculty Award
- 2018 - University Faculty Scholar
- 2017 - Best Paper Award, IEEE ICC
- 2017 - Executive Editorial Committee Member, IEEE Transactions on Wireless Communications
- 2017 - IEEE Fellow
- 2016 - Best Paper Award, IEEE INFOCOM BIGSECURITY Workshop
- 2015 - Area Editor, IEEE Transactions on Communications
- 2010 - Best Paper Award, IEEE MASS
Recent News
AI Researchers Tackle Longstanding ‘Data Heterogeneity’ Problem for Federated Learning
Posted on July 15, 2022 | Filed Under: AI/ML and News
NC State ECE researchers have developed a new approach to federated learning that allows them to develop accurate artificial intelligence (AI) models more quickly and accurately.
NC State ECE Faculty Members Ranked as Top Electronics and Electrical Engineers in United States
Posted on March 3, 2022 | Filed Under: News
Fourteen ECE faculty members have been ranked as the Top Electronics and Electrical Engineering Scientists in the United States.
Technique Smooths Path for ‘Federated Learning’ AI Training in Wireless Devices
Posted on February 1, 2022 | Filed Under: AI/ML and News
A new federated learning technique from ECE researchers drastically reduces the size of data transmissions, creating new opportunities for wireless AI training.