Abstract:

Writing optimising compilers is difficult. The range of programs that may be presented to the compiler is huge and the system on which they run are complex, heterogeneous, non-deterministic, and constantly changing. Machine learning has been shown to make writing compiler heuristics easier, but many issues remain.

In this talk I will discuss recent advances in using deep learning to solve compiler issues: learning heuristics and testing compiler correctness.

Speaker Bio:

Hugh is a reader (associate professor) at the University of Edinburgh. His research involves all elements of compilers and operating systems, usually targeting performance and energy optimisation, often with a focus on using machine learning for those tasks. After his PhD, also at Edinburgh, he was a Fellow of the Royal Society of Engineering. Before returning to academia, he was an engineer at Microsoft and architect and team leader at Trilogy, delivering multi-million dollar projects to Fortune 500 companies.

Abstract:

2018 was a crazy year for privacy. The General Data Protection Regulation came into force in May, and new revelations on the personal data ecosystem were making headlines on a weekly basis. I will give the behind the scenes for a lot of these events, question why they didn’t happen earlier, and offer some thoughts on the necessary future of online services. This will include a brief discussion of topics such as semantic alignment, interpretable machine learning, or new privacy-preserving data processing techniques.

Speaker Bio:

Paul-Olivier Dehaye is a mathematician by training. Affiliated to the University of Zurich as a SNSF Assistant Professor until 2016, his career then took a turn towards data protection activism and social entrepreneurship. He was the researcher on several news articles who have reached millions of readers (including many with Carole Cadwalladr), and testified in front of the UK and EU Parliaments on multiple occasions. He is on the board of MyData Global, has founded the NGO PersonalData.IO, and the project MyData Geneva.

Abstract:

Escape rooms are popular recreational activities whereby players are locked in a room and must solve a series of puzzles in order to ‘escape’. Recent years have seen a large expansion technology being used in these rooms in order to provide ever changing and increasingly immersive experiences. This technology could be used to minimise accessibility issues for users, e.g. with hearing or visual impairments, so that they can engage in the same way as their peers without disabilities. Escape room designers and players completed an online questionnaire exploring the use of technology and the accessibility of escape rooms. Results show that accessibility remains a key challenge in the design and implementation of escape rooms, despite the inclusion of technology that could be used to improve the experience of users with disabilities. This presentation will explore the lack of accessibility within Escape Rooms and the potential for technology to bridge this gap.

Speaker Bio:

Dr Rachel Menzies is the Head of Undergraduate Studies for Computing at the University of Dundee and is the current SICSA Director of Education (https://www.sicsa.ac.uk/education/). She co-directs the UX’d research group (https://www.ux-d.co.uk/) and her research interests include user centred design with marginalised user groups, such as users with disabilities, as well as exploring novel interfaces, data visualisation and CS education. Her most recent work focusses on accessibility is in escape rooms, in particular how users with varied disabilities can access and enjoy the experience alongside typical users.

Abstract:

From mapping the spread of disease to monitoring climate change, data holds the key to solving some of the world’s biggest challenges. Dependable decisions rely on understanding the provenance and reliability of data. Historically, only a small fraction of the generated data was shared and re-used, while the majority of data were used once and then erased or archived. At NPL Data Science we are defining best practice in measurement data reuse and traceability by developing metadata standards and data storage structures to locate and interpret datasets and make them available for sharing, publication and data mining.

The talk will shed light on the most burning issues in the scientific data management, and illustrate it with examples from industrial and academic practices. It will present several NPL Data Science projects that focus on delivering confidence in data obtained from life science imaging, medicine, geosciences and fundamental physics.

Speaker Bio:

Dr Marina Romanchikova joined the NPL Data Science team in 2017 to work on data quality and metadata standards. She obtained an MSc in Medical Informatics at University of Heidelberg, Germany, where she specialised in medical image processing and in management of hospital information systems. In 2010 she received a PhD on Monte Carlo dosimetry for targeted radionuclide therapy at the Institute of Cancer Research in Sutton, UK. Marina worked six years as a radiotherapy research physicist at Cambridge University Hospitals where she developed methods for curation and analysis of medical images.

Current interests

– Quantitative quality assessment of medical images and medical image segmentation
– Harmonisation of medical and healthcare data from heterogeneous sources
– Applications of machine learning in healthcare
– Automated data quality assurance