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Technical Keynote Lecture
Michael Gleaves, Deputy Director Hartree Centre, Science and Technology Facilities Council
Factors affecting the adoption of Machine Learning Technologies in industrial collaborationsAbstract To maximise the impact of a technology, then the application that is developed must be converted into an innovation. Innovation broadly defined in terms of the developing a technology or process that is displaces an existing practice within a community. There is great opportunity for machine learning is this area. I will introduce frameworks for evaluating innovation that can be used to understand the impact and accelerate adoption within a community. Then discuss two case studies of work being carried out within Hartree Centre applying machine learning technologies to healthcare and chemistry and materials discovery. Building the Living Hospital with Hartree Cognitive Advisor. This uses exiting IBM Watson APIs to integrate cognitive technologies into patient care pathways within Alder Hey Children’s Hospital. This aims to reduce the anxiety of the child and family, provide an end to end engagement through the patient journey and complement to new hospital recently opened. Applying machine learning to materials discovery. The Hartree Centre is developing new workflows to optimise the use of High Performance Computing simulation to discovery of material properties. This case study highlights some of the difficulties is having new methods adopted in existing communities and some of the benefits that can be realised through collaboration and integration of new methods.
Michael Gleaves is Deputy Director of the Hartree Centre, which was created to deliver innovation and economic impact to UK Industry and commerce through collaboration in the areas of High Performance Computing, Big Data and Cognitive systems. The Hartree Centre was opened by the Chancellor of the Exchequer on February 1st 2013 and part of the Science and Technology Facilities Council (STFC). It focuses on using techniques that are being developed in the through Science and computing technologies and demonstrates how they could be applied to industrial related problems. Prior the working at the Hartree Centre, Michael was project lead for data and metadata capture systems for STFC large facilities and held positions in area of research, development and sales at Unilever and Dionex. Michael holds a BSc(Hons) Chemical Sciences and MBA.
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