My research has three intertwined goals: (i) develop theory and models that capture the fundamental limits of estimation and learning from data, (ii) construct fair and private learning algorithms with performance guarantees based on these limits, and (iii) use this methodology as a design driver for future information processing and content distribution systems. In order to achieve these goals I use theoretical tools from information theory, statistics, cryptography and machine learning.

I consider myself a scientist who is an engineer at heart, so I enjoy doing fundamental research that serves as a design driver for practical applications. I have a broad set of interests which include information theory, statistics, communications and optimization. You can find more details in the publications below.