Advances of deep learning-based COVID-19 Detection


Yu-Dong Zhang

Professor of Knowledge Discovery and Machine Learning

School of Informatics, University of Leicester, UK

The Title of Speech: Advances of deep learning-based COVID-19 Detection

Abstract of Speech:

COVID-19 is a pandemic disease, which already caused more than 1.15 million deaths till 27/Oct/2020. Deep learning is a rapidly advancing field in recent years, in terms of both methodological development and practical applications. It allows computational models of multiple processing layers to learn and represent data with multiple levels of abstraction. It is able to implicitly capture intricate structures of large-scale data and ideally suited to some of the hardware architectures that are currently available. This invited speak presents the recent advances of using deep learning technologies in COVID-19 detection.

Biography of the Speaker:

Prof. Yu-Dong Zhang received his PhD degree in Signal and Information Processing from Southeast University in 2010. He worked as a postdoc from 2010 to 2012 with Columbia University, USA; and as an assistant research scientist from 2012 to 2013 with Research Foundation of Mental Hygiene (RFMH), USA. He served as a Full Professor from 2013 to 2017 with Nanjing Normal University. Now he serves as Professor with Department of Informatics, University of Leicester, UK. His research interests include deep learning and medical image analysis.

Prof. Zhang is the Fellow of IET (FIET), and Senior Members of IEEE and ACM. He was included in “Most Cited Chinese researchers (Computer Science)” by Elsevier from 2014 to 2018. He was the 2019 recipient of “Highly Cited Researcher” by Web of Science. He won “Emerald Citation of Excellence 2017” and “MDPI Top 10 Most Cited Papers 2015”. He was included in "Top Scientist" in Guide2Research. He is the author of over 250 peer-reviewed articles, including more than 30 “ESI Highly Cited Papers”, and 3 “ESI Hot Papers”. His citation reached 13118 in Google Scholar with h-index of 63, and 7779 in Web of Science with h-index of 50. He has conducted many successful industrial projects and academic grants from NSFC, NIH, Royal Society, EPSRC, MRC, and British Council.