High-Performance Data, Signal and Network Optimization Group

Research Interests

Our research focus on designing and analyzing large-scale optimization methods for problems arise in various domains such as data science, signal processing and networking.

Below is a list of (more or less) theoretical topics that we have worked on.

• Design and Analysis for first-order convex and nonconvex algorithms

• Distributed convex and nonconvex optimization

• Computational complexity for problems in signal processing and communications

• Approximability for interference management problems

• Stochastic optimization for large-scale problems

• Analysis of performance for semi-definite relaxation (SDR) based algorithms

• Analysis of equilibrium solutions for noncooperative games

Our theoretical works are greatly empowered by their applications in various engineering fields.

• Cross-Layer resource management for next-generation complex networks

• High-dimensional machine learning

• Optimization for smart power grids

• Intrusion detection and state estimation for cyber-physical systems

• Speech denoising and enhancement

• Distributed decision making in multi-agent heterogeneous networks