Huaiyu Dai
Professor
Education
-
Ph.D.
2002
Electrical Engineering
Princeton University, NJ -
Master's
1998
Electrical Engineering
Tsinghua University, Beijing
Research Focus
Recent Publications
- Base Station Antenna Uptilt Optimization for Cellular-Connected Drone Corridors (2023)
- Blind Post-Decision State-Based Reinforcement Learning for Intelligent IoT (2023)
- Channel Rank Improvement in Urban Drone Corridors Using Passive Intelligent Reflectors (2023)
- Decentralized Inference With Graph Neural Networks in Wireless Communication Systems (2023)
- Distributed Learning Over Networks With Graph-Attention-Based Personalization (2023)
- Learning-Based Data Gathering for Information Freshness in UAV-Assisted IoT Networks (2023)
- Multi-Job Intelligent Scheduling With Cross-Device Federated Learning (2023)
- On the outage probability of uplink IRS-aided networks: NOMA and OMA (2023)
- Optimal Relay Probing for UAV Millimeter Wave Communications with Beam Training Overhead (2023)
- Resource Constrained Vehicular Edge Federated Learning With Highly Mobile Connected Vehicles (2023)
Highlighted Awards
- University Faculty Scholars (2018)
Awards & Honors
- 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: 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: News
A new federated learning technique from ECE researchers drastically reduces the size of data transmissions, creating new opportunities for wireless AI training.