SIG on Signal Processing Techniques for Big Data and Wireless Edge Intelligence

Chair: Cong Shen, University of Virginia
Vice Chair: Jie Xu, University of Miami

Provide a platform for researchers to exchange ideas, initiate collaboration, and promote research activities that are broadly related to signal processing for big data and machine learning in wireless networks.

Areas of Interest:
Edge computing and edge intelligence
Distributed machine learning over wireless networks
Low-complexity and approximate algorithms for machine learning
Designing wireless systems using machine learning
Low-dimensional and sparse signal representations
Signal processing based on graphic models
Resource management (energy, storage, bandwidth, complexity, etc.)
Multi-relational data
High-dimensional statistical learning
Scalable and decentralized optimizations
Randomized algorithms
Novel architecture for large scale data sensing, processing, and transmission
Fundamental limits: convergence, consistency, robustness, and performance guarantees