Every minute, 500 hours of video are uploaded on Youtube, and 240,000 images are added on Facebook. Since it is physically impossible that this huge mass of data is entirely processed and visualized by humans, there is an absolute need to rely on advanced machine learning methods so as to sort, organize, and recommend the content to users. However, the transmission of the data from the location where they are collected toward the server where they are processed must be done as a preliminary step. The conventional data transmission framework assumes that the data should be completely reconstructed, even with some distortions, by the server. Instead, this thesis aims at developing a novel communication framework in which the server may also apply a learning task over the coded data. We aim at developing an information theoretic analysis so as to understand the fundamental limits of such systems, and develop novel coding techniques allowing for both learning and data reconstruction from the coded data.
We have an open PhD position on information theory and source-channel coding for machine learning applications. More information available here.