报告题目：Machine learning for virus detection in a viral infection outbreak
报告人：英国贝尔法斯特女王大学王辉教授
报告时间：2022年07月05日（周二）下午19:00
报告地点：腾讯会议（会议号：191-314-692）
主持人：李天瑞教授

报告摘要：
COVID-19 is still here, and the world is still living in the cloud of the global epidemic. Measures of epidemics management are still in place in many places in the world, which include (1) prevention (lockdown, social distancing, face masks, vaccine) (2) detection (testing and diagnostics) (3) response (isolation, therapeutics). Virus detection is an important means to manage an epidemic. Gold standard methods are reliable but are typically costly and time consuming; alternative methods are cheaper and faster but are typically less reliable. UK EPSRC has funded a study on virus detection using spectroscopy and machine learning. If successful, it will provide the basis for developing new virus testing systems that are cheaper and faster (than gold standard methods) but also reliable. This talk will present an overview of epidemics management measures, the findings from this UK EPSRC funded project on virus detection, and an outlook on the role of artificial intelligence in future epidemics management.

报告人简介：
Hui Wang is Professor of Computer Science in Queen’s University Belfast. Prior to that, he was Professor of Computer Science in the School of Computing, Ulster University, where he led the Artificial Intelligence Research Centre (2009-2021) and was Research Director (2018-2020). He obtained his PhD in Artificial Intelligence from Ulster University, MSc and BSc in Computer Science from Jilin University, China.

He is Principal Investigator of a number of regional, national and international projects in the areas of image/video analytics (EPSRC funded MVSE 2021-2024, Horizon 2020 funded DESIREE and ASGARD, FP7 funded SAVASA, Royal Society funded VIAD), spectral data analytics (EPSRC funded VIPIRS on virus detection 2020-2022), text analytics (INI funded DEEPFLOW, Royal Society funded BEACON), and intelligent content management (FP5 funded ICONS); and is co-investigator of several other funded projects. He is an associate editor of IEEE Transactions on Cybernetics, founding Chair of IEEE SMCS Northern Ireland Chapter (2009-2018), and a member of IEEE SMCS Board of Governors (2010-2013).

His research interests are machine learning, knowledge representation and reasoning, and their applications in image, video, spectra and text data analyses. Notable contributions include lattice machine (an algebraic framework for machine learning by generalization and knowledge representation), contextual probability (a perceptionist formulation of probability), and neighbourhood counting (a generic similarity measure that can be specialized to any form of data including multivariate data, sequences, tree and graph structures). His current focus is detection learning and knowledge-based learning.