Research

 

I'm generally interested in multi-disciplinary engineering and scientific problems that lie in between applied math, computer vision and signal processing. My special focus is on practically constrained problems:

  • Information/data is incomplete and/or highly noisy 
  • Information/data is massive while the system resources are limited and hence, requiring fast and efficient solutions.

I'm interested in both designing an algorithmic solution and understanding its underlying science.

Selected Projects

Practical Algorithms for Compressive Sensing
We develop novel algorithms of sampling and reconstruction
for large scale compressed sensing applications with practical requirements: low complexity, fast computation, efficient implementation, streaming capacity, hardware and optical domain friendliness 

 

Machine learning/Data mining
We develop fast and efficient
algorithms of dimensionality reduction and low-rank matrix recovery (from partial observation) for applications in machine learning and data mining