Queen Mary University of London Turing Fellow Project Presentations Showcase
Queen Mary University of London Institute of Applied Data Science cordially invites you to attend our Turing Fellow Project Presentations Showcase Event.
This event will showcase successes and research outputs from the Alan Turing Institute funded projects led by Queen Mary University of London Turing Fellows.
Date and time: Monday, 26 April 2021, 15:00-16:45
Format: Online via Zoom
Registration is required! Register in advance for this event by clicking this link. After registering, you will receive a confirmation email containing information about joining the event.
Queen Mary University of London Turing Fellow Project Presentations Showcase - Agenda
15:00-15:05 Introduction by Professor Michael Farber, Director of Institute of Applied Data Science
15:05-15:35 Presentation #1: "ElasticSketch: Towards network traffic measurements at scale" by Professor Steve Uhlig, Professor of Networks, School of Electronic Engineering and Computer Science
Abstract: Network measurements provide indispensable information at the best of times for network operations, quality of service, network billing and anomaly detection in data centers and backbone networks. However, measurements are all the more important when a network is undergoing problems (congestion, scan attacks, or DDoS attacks). During such times, traffic characteristics vary drastically, significantly degrading the performance of most measurement tasks. In this talk, I will present our recent efforts to design data structures capable of adapting to changing network traffic conditions, to keep network measurement tasks going.
15:35-16:05 Presentation #2: "Protecting Personal Information in Image, Audio and Motion Data" by Professor Andrea Cavallaro, Professor of Multimedia Signal Processing, School of Electronic Engineering and Computer Science
Abstract: Images, sounds and motion data we share in social media networks, and through services like voice interfaces and health apps reveal information about our behaviours, personal choices and preferences, which can be inferred by machine-learning classifiers. To prevent privacy violations, I will discuss how to protect personal information from unwanted automatic inferences by learning feature representations that disentangle sensitive from non-sensitive attributes as well as by crafting perturbations that protect selected attributes. I will show examples and discuss application scenarios for each data type.
16:05-16:35 Presentation #3: "The Less is More: Deep Learning with User Ownership at the Edge" by Professor Shaogang Gong, Professor of Visual Computation, School of Electronic Engineering and Computer Science
Abstract: Visual search of unseen objects assumes the availability of a query image of a search target, which is limited when only a brief text description is available. Deep learning has been hugely successful in computer vision because of shared and centralised large sized training data. However, privacy concerns and a need for user-ownership of localised data pose new challenges to the conventional wisdom for centralised deep learning on big data. In this talk, I will highlight challenges and recent progress on deep learning for language guided user interactive visual search at the edge, and decentralised learning at the edge from non-shared distributed small data all having different learning tasks (non-shared labels).
16:35-16:40 Closing Remarks by Professor Michael Farber, Director of Institute of Applied Data Science
|Location:||Online via Zoom|
|Contact:||Dr Michal Filus|
Updated by: Michal Filus