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University of Manchester Data Sciences Institute
Three unmissable presentations from the one of the UK’s leading data sciences institutes
Data Science Institute – Wednesday 15th November (14.30 – 16.00)
Manchester’s Data Science Institute acts as an access point to the University’s expertise in data science, facilitates interactions between data science researchers and problem holders, owns the University’s data science strategy, and will deliver sustainable support for the community.
Meet with the University of Manchester Business Engagement Team who will be on hand to discuss their research, knowledge exchange, consultancy and professional development services.
Understanding Anonymisation and the Anonymisation Decision-Making Framework (14.30 – 15.00, stage 8)
Professor Mark Elliot
The need for well-thought-out anonymisation has never been more acute. In the current data environment, increasing numbers of large and linked datasets are created about people. At the same time there are political, social and economic drivers encouraging greater sharing of anonymised data
Cyber security: Don’t dis’ the human! There’s [almost] no such thing as user error (15.00 – 15.30, Stage 8)
Dr Daniel Dresner
Daniel is a lecturer in the School of Computer Science at the University of Manchester. He leads the University’s activity in Cybersecurity and co-created IASME – information assurance for small to medium sized business. Daniel and a colleague from University of Worcester created the IASME programme which was chosen as the best cyber security standard for small businesses by BIS and CESG, leading Daniel to contribute to the Cyber Essentials.
Machine Learning: Applications in Image Analysis (15.30-16.00, Stage 8)
Professor Timothy Cootes
Techniques for learning from data have blossomed recently. Given a particular input (such as a set of measurements or an image) the systems can learn the most likely output (such as a diagnosis of disease, or the identity of an individual). This talk will summarise some of the latest techniques and describe their application in analysing images, with a particular emphasis on the medical domain.