Healthcare AI researcher appointed to Faculty position in recognition of Academy Fellowship
Postdoctoral researcher Dr Tingting Zhu has been awarded a Royal Academy of Engineering Fellowship 2019 as well as the IET J.A. Lodge award.
Dr Tingting Zhu, Associate Research Fellow of St Hilda’s College and Stipendiary Lecturer at Mansfield College, works on the development of machine learning for healthcare applications. She has been announced as one of the Royal Academy of Engineering’s 2019 Research Fellows, each of whom receives five years’ worth of funding and mentorship to advance their research careers.
Professor Philip Nelson CBE FREng, Chair of the Royal Academy of Engineering Research Fellowships Steering Group, added: "I am delighted to announce these five-year Research Fellowships to 18 of the most promising engineering academics working in the UK today. Engineering research plays a vital role in addressing societal and industrial challenges, both today and in the future, and the variety and impact of the research being done by these awardees demonstrates the depth and breadth of world-leading engineering expertise we have within our universities.”
Royal Academy of Engineering Research Fellowships enable early-career researchers to concentrate on basic research in any field of engineering. As an awardee, Dr Zhu will receive mentoring from experienced Academy Fellows, providing the valuable advice and industry links that enables researchers to establish themselves as future leaders in their fields.
She says: “It is an incredible honour to receive this highly competitive fellowship as an academic in engineering. I am grateful to the department for their support for my fellowship.
“Being an Engineering for Development Research Fellow allows me to not only create next-generation engineering systems for addressing problems in healthcare, but also to transfer and deploy them in the developing world.”
Dr Zhu attained her DPhil at Oxford in 2016, before being recruited to join the Computational Health Informatics Group, led by Professor of Clinical Machine Learning David Clifton. In recognition of her achievement in earning this highly-competitive fellowship, she has also been named a full Member of Faculty as an independent academic. Tingting had previously been appointed the Department’s first Associate Member of Faculty in 2018, under a scheme seeking to recognise distinguished early-career researchers.
Dr Zhu's DPhil focussed on the development of probabilistic techniques for combining information from wearable sensors to form a consensus that provides accurate monitoring of time-series medical data. She developed algorithms for online, unsupervised learning, combining crowd-sourced medical data, to provide real-time indication of the health status of an individual and precision medicine. After an EPSRC-funded postdoctoral research position in AI for Healthcare, she was awarded a Stipendiary Junior Research Fellowship at St. Hilda's College, Oxford.
Her current research involves the development of machine learning for understanding complex patient data, with a special emphasis on Bayesian inference, deep learning, and applications involving the developing world. She is the principal investigator for research projects awarded by the Royal Academy of Engineering, the EPSRC, the UK Global Challenges Research Fund, Cancer Research UK, and the National Institute for Health Research.
Dr Zhu achieved further success this month when she was selected by the Institute of Engineering and Technology (IET) as this year’s recipient of their prestigious J.A. Lodge award. It honours early-career researchers, who have within five-year postdoctoral or ten-year industrial experience, bringing innovative electronic or electrical engineering work to the field of biomedical engineering. As part of this, she presented her paper at the 2019 IET Annual Healthcare Lecture, alongside fellow prize winners.
Speaking about the award, Dr Zhu added: “I am delighted to be awarded this distinguished prize, in particular with the recognition of my work in patient-specific physiological monitoring and risk predication. I would also like to thank Professor David Clifton, Dr Glen Colopy, and Professor Chris Pugh for their support. I will continue to devote my effort in creating innovative data-driven engineering solutions to address healthcare challenges.”