Research

During the last several years, we have been inundated by a deluge of data in applications including distributed networked systems, finance, medical imaging, and seismics. My interest lies in fundamental research for problems involving vast amounts of data that must be processed effectively and rapidly in order to extract useful – potentially “actionable” – information. To approach these problems, we must use a multi-disciplinary approach, and I combine tools from information theory, statistical signal processing, machine learning, and computer science. I call this computational information processing.

Specific research directions that I have worked on include:

• MDL signal estimation and universal denoising.
• Compressed sensing (CS) – signal reconstruction algorithms, information theoretic results, distributed CS, hardware projects in CS, and other unrelated CS topics.
• Fast algorithms for universal lossless compression.
• Universality in channel coding and distributed compression.
• Worst-case constraints in compression and prediction.
• Compression and representation of multi-dimensional functions with smooth discontinuities.
• Delay-constrained performance in ALOHA networks.