Parallel and Distributed Methods for Big-Data Optimization 

Nowadays, large-scale systems are ubiquitous. Some examples/applications include wireless communication  networks; electricity grid, sensor, and cloud networks; and machine learning and signal processing applications, just to name a few.  In many of the above systems,  i) data are distributively stored in the network (e.g., clouds, computers, sensors, robots), and ii) it is often impossible to run analytics on central fusion centers, owing to the volume of data, energy constraints, and/or privacy issues. Thus, distributed in-network processing with parallelized multi-processors is  preferred. Moreover, many applications of interest lead to large-scale optimization problems with nonconvex, objective functions. All this makes the analysis and design of  parallel and distributed algorithms  a  challenging task.  

In this talk we provide an overview of  our ongoing research  in this area, targeting several applications in signal processing, machine learning, and medical imaging.