Keynote Speakers

Prof. Liz Bacon

University of Greenwich, UK

Professor Liz Bacon BSc, PhD, CEng, CSci, CITP, FBCS, PFHEA, MACM is a Deputy Pro-Vice-Chancellor at the University of Greenwich in London, with a University wide remit leading the development of technology enhanced learning and pedagogic research. She was President of BCS, The Chartered Institute for IT, in the year 14-15 and is a past Chair of the BCS Academy of Computing, and the CPHC (Council of Professors and Heads of Computing) national committee. Liz is a Professor of Software Engineering with over a hundred publications and a Co-Director of the eCentre research group. She has been involved in several EU research projects, including being Principal Investigator and Project Coordinator for two EU FP7 projects in the recent past. She is an experienced systems designer and developer and her research interests include computing policy, smart systems, security and technology enhanced learning (TEL). Within TEL, she has applied her research in software engineering, artificial intelligence and security to a range of application areas such as crisis management and eHealth, focusing on: smart games-based learning environments; metacognition and learning strategies; adaptable, adaptive and personalised systems; and the use of social media in online learning, all supported by cloud technology. She also researches, publishes, and is a regular international speaker, on the supply and demand of e-skills to the IT industry. Liz is passionate about the development of her discipline and keen to inspire more people to choose computing as a career, particularly women.

Speech Title: How the intelligent use of big data is predicted to transform learning?

Abstract: The intelligent analysis of big data is already transforming society at a rapid pace however, in the education space, and particularly in models of education which have remained largely unchanged for decades, its impact has been minimal to-date. The increasing use of artificial intelligence in society, predicts the need for regular mass personalised education, as more jobs become automated, and many would argue we are on the cusp of a significant change in the how we teach and learn. This talk will discuss forthcoming changes in education due to big data, some of the research in this area and will conclude with some predictions on the direction of travel, and the ethical issues that may result from these changes.

Prof. Jin Wang

Valdosta State University, USA

Jin Wang is a Professor of Operations Research in the Department of Mathematics at Valdosta State University, USA. He received his Ph.D. degree from the School of Industrial Engineering at Purdue University in 1994. His research interests include Operations Research, Stochastic Modeling and Optimization, Supply Chain Management, Monte Carlo Simulation, Computational Finance, Portfolio Management, and Applied Probability and Statistics. Currently, he is working on Big Data and Data Mining fields. He has more than 28 years collegiate teaching experience in the field of quantitative methods and statistics at Purdue University, Florida State University, Auburn University, and Valdosta State University. Dr. Wang has been active in professional research activities. He has authored articles for publication in referred journals and conference proceedings. He has been active in INFORMS, IIE, and the Winter Simulation Conference and invited to give presentations, organize and chair sessions at national meetings. He has participated as a principal investigator in several research projects funded by federal and industrial agencies, including the National Science Foundation, General Motors, and the National Science Foundation of P.R. China. He was invited as a panel member at the National Science Foundation Workshop. Dr. Wang also served as a consultant for financial firms. His analytical Monte Carlo method using a multivariate mixture of normal distributions to simulate market data has made a great impact in education and the finance industry. This algorithm was selected as a graduate-level research project topic for many schools, such as, Columbia University Management Department, Carnegie Mellon University Economics and Finance Department, Tilburg University in Holland, Technische Universitaet Munich in Germany, Imperial College in London. This method was also implemented in many financial companies, such as, Zurcher Kantonal Bank, IRQ, Zurich Switzerland, Klosbachstrasse, Zurcher, Switzerland, Norsk Regnesentral in Norway, Cutler Group, L.P., Altis Partners (Jersey) Limited, Windham Capital Management, LLC..

Speech Title: Understanding the Big Data

Abstract: In the big data era, data is everywhere. Big data can be described by three characteristics: volume, variety, and velocity. The new technology has enabled us to measure an increasing volume and variety of variables. One of challenges is how to best summarize, display, and analyze the big data efficiently. In 1901, British mathematician Karl Pearson introduced principal components to use a low-dimensional summary to best describe a high-dimensional dataset. This method has new applications in image compression, face recognition, ranking, clustering, community detection...In this talk, we will focus on mathematical approaches to big data analysis based on computational linear algebra. Applications include PageRank, MapReduce, Spectral Clustering, Optimization, and Principal Component Analysis.


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