The Royal Academy of Engineering awards new Research Fellowship
Welcoming the new awardees, Professor Ric Parker CBE, FREng, Director of Research and Technology, Rolls-Royce Group and Chair of the Academy’s Research and Secondments Committee said: “Innovation is at the heart of industrial and economic growth, and the UK’s engineering sector constantly needs the fresh approach to both old and new problems that comes from young, enthusiastic and dedicated researchers.”
“The Academy is committed to encouraging excellence in all aspects of engineering. Through the Research Fellowships scheme we offer not only financial support, but mentoring and guidance as well, to help already outstanding individuals to develop into even greater research leaders.”
Healthcare systems world-wide are starting to acquire an ever-increasing amount of complex data within electronic patient records, concerning all aspects of patient care throughout the life of a patient, including the results of genomic tests. This “big data” problem is set to exceed the capabilities of clinical experts. Dr Clifton’s research focuses on the development of complex “machine learning” methods able to learn from experience and cope with this large sea of different and seemingly incompatible data to help improve patient outcomes in the hospital and home.
A graduate of the Department of Engineering Science in 2009, Dr. Clifton said,
“Oxford is well-placed at the heart of both internationally-leading clinical research and biomedical engineering to bridge the gap between machine learning and healthcare. With the generous support of the Royal Academy of Engineering, this Fellowship will allow me to tackle three initial problems: (1) tracking and identifying new infectious diseases throughout the healthcare system, identified by the Chief Medical Officer in 2013 as being “as great a threat to national security as global warming”; (2) reducing mortality for in-hospital patients by using novel machine learning methods to exploit data acquired throughout the healthcare system, and (3) developing low-cost, intelligent mobile systems based on machine learning technologies for improving access to healthcare in the developing world.”