Track Co-Chairs:
- Ahmed Alkhateeb, Arizona State University, aalkhateeb@asu.edu
- Longfei Shangguan, University of Pittsburgh, longfei@pitt.edu
- Cheng Zhang, Texas A&M University, chzhang@tamu.edu
- Yanfu Zhang, William & Mary, yzhang105@wm.edu
Description:
As networks continue to grow in scale, complexity, and heterogeneity, traditional rule-based or optimization-driven approaches struggle to cope with dynamic environments, massive data volumes, and stringent performance requirements. Meanwhile, Artificial Intelligence (AI) and Machine Learning (ML) are increasingly being integrated into communication and networking systems, enabling data-driven, adaptive, and intelligent solutions. The AI/ML for Networks & Networks for AI/ML track seeks to bring together researchers and practitioners from networking, communications, machine learning, and data science to explore two complementary directions: 1. AI/ML for Networks – advancing the design, control, and management of networks using AI/ML; and 2. Networks for AI/ML – leveraging networks to optimize and accelerate the deployment, training, and inference of AI/ML models at scale. We welcome original contributions that span theory, algorithms, architectures, system implementations, prototypes, and experimental evaluations. Submissions with novelty, scalability, interdisciplinary perspectives, or strong practical value are particularly encouraged.
• AI/ML for radio resource management and optimization
• AI/ML for channel estimation, channel modeling, and channel prediction
• AI/ML for wireless communications waveform design
• AI/ML for end-to-end wireless communications
• AI/ML for Internet of Things (IoT) and massive connectivity
• AI/ML for Multi-Access Edge Computing (MEC)
• AI/ML for signal detection and classification
• AI/ML for localization
• AI/ML for routing and management of wireless and sensor networks
• AI/ML for ultra-reliable and low latency communications
• AI/ML for massive MIMO, active and passive reconfigurable intelligent surfaces
• AI/ML for multiple access
• AI/ML for integrated sensing and communications
• AI/ML for physical layer security
• Optimization of neural networks for low-complexity hardware implementation
• Distributed and federated learning in wireless and sensor networks
• Transfer learning and meta learning in wireless and sensor networks
• Large language models for communications and networks
• Privacy-preserving AI/ML for communications and networking
• Trustworthy and explainable AI for communications and networking
• AI/ML for beam management and prediction
• AI/ML for multi-modal sensing and communications
• AI/ML-assisted digital twin construction, calibration, and applications
• Graph neural networks and graph learning for network modeling and optimization
TPC list:
• Asmaa Abdallah, King Abdullah University of Science and Technology
• Abolfazl Zakeri, University of Oulu
• Kareem Attia, University of Toronto
• Yongming Qin, Foxx
• Chenxi Chen, Foxx
• Junyi Li, Amazon
• Zhepeng Wang, Amazon
• Shangqian Gao, Florida State University, USA
• Qing Tang, Old Dominion University, USA
• Tao Chen
• Dong Zhao, Beijing University of Posts and Telecommunications, China
• Haibo Yang, Rochester Institute of Technology, USA
• Fengzhi Guo, Texas A&M University, USA
• Huanghuan Zhang, Beijing University of Posts and Telecommunications, China
• Sriram Chellappan, University of South Florida, USA