Deep Probabilistic Models for Making Sense of Unstructured Data

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. These applications are inspired by collaborations with citizenme, the NewMind Network for Mental Health Technology Research and Makerere University in Kampala, Uganda.