2024 5th International Conference on Public Health and Data Science(ICPHDS 2024)
Speakers (2023)
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Jixin Ma 教授.png

Professor Jixin Ma

IEEE Member

University of Greenwich, UK

Dr Jixin Ma is a Professor in the School of Computing and Mathematical Sciences, at the University of Greenwich, U.K. He is the Director of the Centre for Computer and Computational Science, and the Director of School’s PhD/MPhil Programme. Dr Ma is also a Guest/Visiting Professor of Beijing Normal University, Auhui University, Zhengzhou Light Industrial University and Hainan University, China.

Professor Ma obtained his BSc and MSc of Mathematics in 1982 and 1988, respectively, and PhD of Computer Sciences in 1994. His main research areas include Artificial Intelligence, Software Engineering and Information Systems, with special interests in Temporal Logic, Temporal Databases, Reasoning about Action and Change, Case-Based Reasoning, Pattern Recognition, Graph Matching and Information Security. He has been a member of British Computer Society, American Association of Artificial Intelligence, ICIS/IEEE, and Special Group of Artificial Intelligence of BCS. Professor Ma has been the Editor of several international journals and international conference proceedings, and Program Chair/Invited Keynote Speakers of many international conferences. He has published more than 150 research papers in international journals and conferences.

Title: Matching Temporal Patterns

Abstract: Pattern recognition aims at the operation and design of technologies to pick up meaningful patterns in data. In conventional pattern recognition systems, various patterns are usually represented as isolated collections of data while the temporal relationships between distinct patterns are neglected in most approaches. Generally speaking, temporal representation and reasoning is essential for many computer-based systems. In particular, an appropriate representation and reasoning for temporal knowledge seems necessary for recognizing pattern histories that usually involve rich internal temporal aspects, rather than just distinct patterns. Recognizing temporal pattern actually plays an important role in solving problems including explanation/diagnosis, prediction/forecast, planning/scheduling, process management, and history reconstruction, etc. For instance, in the area of healthcare/medical information systems, a patient’s medical history is obviously very important. In fact, to prescribe the right treatment, the doctor needs to know not only the patient’s current status, but also his/her previous health records.The purpose of this talk is to: (a) motivate and address a hot and advanced intelligent computing topic of emerging importance; (b) provide an overview on some fundamental issues with respects to temporal representation and matching; (c) present a framework for representing and recognizing temporal pattern with rich internal temporal aspects.


Professor David Greenhalgh

University of Strathclyde Glasgow, UK

I gained a PhD from the University of Cambridge in 1984 and worked at Imperial College, London from 1984 to 1986. I also have a first class Honours degree in Mathematics and a distinction in Part III Mathematics. I am currently a member of the Population Modelling and Epidemiology Research Group at Strathclyde and have been a member of Strathclyde in the Departments of Mathematics, Statistics and Modelling Science and Mathematics and Statistics since 1986. I am currently Fourth Year Adviser of Studies and also Executive Editor of Journal of Biological Systems.

In 2015 I was awarded a two year (2015-2017) Leverhulme Trust Research Fellowship grant (50K RF-2015-88) as PI to study mathematical modelling of vaccination against dengue.

Title: Optimal Vaccination Age for Dengue in Brazil with a Tetravalent Dengue Vaccine

Abstract: With the first vaccine against Dengue being licensed in several endemic countries an important aspect that needs to be considered is the age at which it should bead ministered. If the vaccine is given at young ages when individuals may still be protected by maternal antibodies it is ineffective, but if it is done later the infection may spread in the younger age groups. Additionally the risk of requiring hospitalisation due to an infection changes with the age of infection, which is influenced by vaccination. Finding the optimal vaccination age is further complicated by the possible coexistence of up to four distinct dengue serotypes and the cross-reactions between these serotypes and dengue antibodies. We adapt a method due to Hethcote previously applied to other infectious diseases and define the lifetime expected risk due to dengue with respect to the risk of requiring hospital treatment which we then seek to minimize for a given three dose vaccination strategy. Our results show that the optimal vaccination age highly depends on the number and combination of serotypes in circulation, as well as on underlying assumptions about cross-immunity and antibody dependent enhancement (ADE). We present results both with constant vaccine efficacy and age-dependent vaccine efficacy.