Huaiyu Dai
Electrical and Computer Engineering
- Phone: 919-513-0299
- Email: hdai@ncsu.edu
- Office: Engineering Building II (EB2) 2108
- Website: https://www.ece.ncsu.edu/people/hdai/
Huaiyu Dai is a professor in the Electrical and Computer Engineering Department at NC State University.
Website
Research Interests
Communications and Signal Processing
Education
| Degree | Program | School | Year |
|---|---|---|---|
| Ph.D. | Doctor of Philosophy in Electrical Engineering | Princeton University | 2002 |
| MSEE | Master of Science in Electrical Engineering | Tsinghua University | 1998 |
| BSEE | Bachelor of Science in Electrical Engineering | Tsinghua University | 1995 |
Awards and Honors
- 2019 | Qualcomm Faculty Award, Qualcomm
- 2018 | University Faculty Scholar, 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
Discover More About Huaiyu Dai
- AI Researchers Tackle Longstanding ‘Data Heterogeneity’ Problem for Federated Learning
- NC State ECE Faculty Members Ranked as Top Electronics and Electrical Engineers in United States
- Technique Smooths Path for ‘Federated Learning’ AI Training in Wireless Devices
Publications
- Accelerating the Delivery of Data Services over Uncertain Mobile Crowdsensing Networks
- Liwang, M., Cheng, Z., Gong, W., Li, L., Su, Y., Jiao, Z., … Dai, H. (2024, September 9), IEEE WIRELESS COMMUNICATIONS. https://doi.org/10.1109/MWC.005.2400126
- GA-DRL: Graph Neural Network-Augmented Deep Reinforcement Learning for DAG Task Scheduling Over Dynamic Vehicular Clouds
- Liu, Z., Huang, L., Gao, Z., Luo, M., Hosseinalipour, S., & Dai, H. (2024), IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, 21(4), 4226–4242. https://doi.org/10.1109/TNSM.2024.3387707
- Hierarchical Federated Learning in Wireless Networks: Pruning Tackles Bandwidth Scarcity and System Heterogeneity
- Pervej, M. F., Jin, R., & Dai, H. (2024), IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 23(9), 11417–11432. https://doi.org/10.1109/TWC.2024.3382093
- Joint Optimization of Charging Station Placement and UAV Trajectory for Fresh Data Collection
- Liu, J., Yang, F., Wang, X., Qu, L., Jin, M., & Dai, H. (2024), IEEE INTERNET OF THINGS JOURNAL, 11(14), 25057–25073. https://doi.org/10.1109/JIOT.2024.3392410
- Magnitude Matters: Fixing signSGD Through Magnitude-Aware Sparsification and Error Feedback in the Presence of Data Heterogeneity
- Jin, R., He, X., Zhong, C., Zhang, Z., Quek, T. Q. S., & Dai, H. (2024), IEEE TRANSACTIONS ON SIGNAL PROCESSING, 72, 5140–5155. https://doi.org/10.1109/TSP.2024.3454986
- Reinforcement Learning-Based Personalized Differentially Private Federated Learning
- Lu, X., Liu, Z., Xiao, L., & Dai, H. (2025), IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 20, 465–477. https://doi.org/10.1109/TIFS.2024.3515814
- Sign-Based Gradient Descent With Heterogeneous Data: Convergence and Byzantine Resilience
- Jin, R., Liu, Y., Huang, Y., He, X., Wu, T., & Dai, H. (2024, January 12), IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, Vol. 1. https://doi.org/10.1109/TNNLS.2023.3345367
- Task-Decoding Assisted Cooperative Transmission for Coded Edge Computing
- Li, T., He, X., Jin, R., & Dai, H. (2024), IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 23(8), 9044–9058. https://doi.org/10.1109/TWC.2024.3357857
- Unleashing the Potential of Stage-Wise Decision-Making in Scheduling of Graph-Structured Tasks over Mobile Vehicular Clouds
- Liwang, M., Guo, B., Ma, Z., Su, Y., Jin, J., Hosseinalipour, S., … Dai, H. (2024, July 15), IEEE COMMUNICATIONS MAGAZINE. https://doi.org/10.1109/MCOM.001.2300816
- Base Station Antenna Uptilt Optimization for Cellular-Connected Drone Corridors
- Maeng, S. J., Chowdhury, M. M. U., Guvenc, I., Bhuyan, A., & Dai, H. (2023), IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 59(4), 4729–4737. https://doi.org/10.1109/TAES.2023.3237994
