User-Centric Personal Data Analytics on the Edge

 Abstract: In this talk, I present our ongoing work on utilising edge-computing to improve the scalability and privacy of user-centred analytics in the context of personal/IoT data. I present an architecture where devices and resources centred around the user, collectively referred to as the edge, can complement the cloud for providing privacy-aware, yet accurate and efficient analytics. I then present the evaluations of the proposed framework for applying privacy-preserving deep learning techniques on a number of exemplar applications, and discuss the broader implications of such approaches for future systems such as the Databox platform.

 Bio: Hamed is a Senior Lecturer (~Associate Professor) and the Deputy Director of Research in the Dyson School of Design Engineering, and an Academic Fellow of the Data Science Institute, at The Faculty of Engineering at Imperial College London. He is interested in User-Centered Systems, IoT, Applied Machine Learning, and Data Security & Privacy. He enjoys designing and building systems that enable better use of our digital footprint, while respecting users’ privacy. He is also broadly interested in sensing applications and Human-Data Interaction.