Deep Project

Overview

The future information infrastructure will be characterized by massive streaming sets of distributed data-sources. These data will challenge classical statistical and machine learning methodologies both from a computational and a theoretical perspective. This proposal investigates a flexible class of models for learning and inference in the context of these challenges. We will develop learning infrastructures that are powerful, flexible and ‘privacy aware’ with a user-centric focus. These learning infrastructures will be developed in the context of particular application challenges, including mental health, the developing world and personal information management.

The project is sponsored by EPSRC Project Ref EP/N014162/1 and Facebook Faculty Award Project Ref and AWS in Education Grant award Project Ref , and is a collaboration with Zoubin Ghahramani of The University of Cambridge, John Quinn of Makerere University and Pulse Lab Kampala, StJohn Deakins of CitizenMe and NewMind Network of .

Personnel from ML@SITraN

• Michael T. Smith Named Post-doctoral Researcher

• Michael Croucher Co-investigator