Short Bio:

George Giannopoulos. Academician of Athens College (Greek National Academy of Sciences), Honorary Professor of Civil Engineering, University of Thessaloniki Aristotle, and Co-Chairman of the Standing Committee on International Cooperation of TRB, USA. For decades, he has devoted himself to the research of ITS, traffic planning and engineering. From 1977 to 1980, he was the head of the Greek delegation of the European Community and the third countries to coordinate the European Temporary Passenger Service Rules. From 1978 to 1979, he was the chairman of the ECMT Working Group on European Cargo Transport. In 1986, he visited the School of Management of MIT and the Cambridge Transport Center of the United States. From 1986 to 1990, he served as the Greek Coordinator of the European Science Foundation and in California Visiting Professor, Department of Civil Engineering, University of Berkeley, 1997-2008, ITMMA Graduate School of Economics and Maritime Transport, University of Antwerp, Belgium, and Director, Greek Transport Institute, Ministry of Communications, 2000-2015. Over the past 15 years, he has undertaken and completed 15 major international projects, including international cooperation projects in the field of European transport research, intelligent global container management projects, and sustainable freight transport system projects to stimulate Latin American countries. Fourteen books and about 250 papers have been published.

Short Bio:

Chunhua Shen is a Professor at School of Computer Science, University of Adelaide. He is a Project Leader and Chief Investigator at the Australian Research Council Centre of Excellence for Robotic Vision (ACRV), for which he leads the project on machine learning for robotic vision. Prior to that, he was with the computer vision program at NICTA (National ICT Australia), Canberra Research Laboratory for about six years. His research interests are in the intersection of computer vision and statistical machine learning.

He studied at Nanjing University, at Australian National University, and received his PhD degree from the University of Adelaide. From 2012 to 2016, he held an Australian Research Council Future Fellowship. He is Associate Editor (AE) of the Pattern Recognition journal, IEEE Transactions on Circuits and Systems for Video Technology; and served as AEs for a few journals including IEEE Transactions on Neural Networks and Learning Systems.

Short Bio:

Fei-Yue Wang is a Specially Appointed State Expert, and The Chief Scientist and Founding Director of the State Key Laboratory for Management and Control of Complex Systems of the Chinese Academy of Sciences and editor-in-chief of the IEEE Transactions on Computational Social Systems and the IEEE/CAA Journal of Automatica Sinica. Previously he was a Professor of Systems and Industrial Engineering at the University of Arizona, president of the IEEE Intelligent Transportation Systems Society, editor-in-chief of IEEE Transactions on Intelligent Transportation Systems (2009-2016) and editor-in-chief of IEEE Intelligent Systems.

Wang was elected as a Fellow of the IEEE in 2004 "for contributions to intelligent control systems and applications to complex systems". He also became a fellow of the American Association for the Advancement of Science and the ASME in 2007. In 2011 he won the Outstanding Research Award of the IEEE Intelligent Transportation Systems Society, and in 2014 he was given the Norbert Wiener Award of the IEEE Systems, Man, and Cybernetics Society "for fundamental contributions to and innovations in the theory and application of intelligent control and management to complex systems."

Keynote Title:

Network Econo-physics for Financial Market Analysis

Abstract:

In recent work we have developed new theoretical tools for time varying network analysis based on models drawn from statistical physics, manifold learnign theory and deep learning. By combining ideas from modern machine learning, quantum computing and complex networks, this leads to new methods for financial market analysis. In this talk I will review the origin of these ideas, and explain how they can be applied to problems arrising in financial market analysis. The talk commences by detailing how ideas from statistical physics and quantum computing can be used to understand how network entropy can be computed for both directed and undirected graphs, and used to both understand and model how financial networks evolve with time. Moreover, with tractable ways of computing network entropy to hand we can develop kernel methods and design deep learning architectures for ananlysing and visualising financial market time-evolution. This corpus of work can be thought of as defining a distinct interdisciplinary research area, which we refer to as "Network Econo-physics".

Short Bio:

Edwin R. Hancock holds a BSc degree in physics (1977), a PhD degree in high-energy physics (1981) and a D.Sc. degree (2008) from the University of Durham, and a doctorate Honoris Causa from the University of Alicante in 2015. From 1981-1991 he worked as a researcher in the fields of high-energy nuclear physics and pattern recognition at the Rutherford-Appleton Laboratory (now the Central Research Laboratory of the Research Councils). During this period, he worked on high energy physics experiments at the Stanford Linear Accelarator Center (SLAC) providing the first measurements of charmed particle lifetimes. He also held adjunct teaching posts at the University of Surrey and the Open University. In 1991, he moved to the University of York as a lecturer in the Department of Computer Science, where he has held a chair in Computer Vision since 1998. He leads a group of some 25 faculty, research staff, and PhD students working in the areas of computer vision and pattern recognition. His main research interests are in the use of optimization and probabilistic methods for high and intermediate level vision. He is also interested in the methodology of structural and statistical and pattern recognition. He is currently working on graph matching, shape-from-X, image databases, and statistical learning theory. His work has found applications in areas such as radar terrain analysis, seismic section analysis, remote sensing, and medical imaging. He has published about 185 journal papers and 650 refereed conference publications. He was awarded the Pattern Recognition Society medal in 1991 and an outstanding paper award in 1997 by the journal Pattern Recognition. He has also received best paper prizes at CAIP 2001, ACCV 2002, ICPR 2006, BMVC 2007 and ICIAP in 2009 and 2015. In 2009 he was awarded a Royal Society Wolfson Research Merit Award. In 1998, he became a fellow of the International Association for Pattern Recognition. He is also a fellow of the Institute of Physics, the Institute of Engineering and Technology, and the British Computer Society. In 2016 he became a fellow of the IEEE and was named Distinguished Fellow by the British Machine Vision Association. In 2018 he received the Pierre Devijver Award from the IAPR. He is currently Editor-in-Chief of the journal Pattern Recognition, and was founding Editor-in-Chief of IET Computer Vision from 2006 until 2012. He has also been a member of the editorial boards of the journals IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition, Computer Vision and Image Understanding, Image and Vision Computing, and the International Journal of Complex Networks. He has been Conference Chair for BMVC in 1994 and Progrmme Chair in 2016, Track Chair for ICPR in 2004 and 2016 and Area Chair at ECCV 2006 and CVPR in 2008 and 2014, and in 1997 established the EMMCVPR workshop series. He was a Governing Board Member of the IAPR from 2006 to 2016, and was Second Vice President of the Association (2016-2018).

Keynote Title:

Digital Wellness and Digital Addiction: Responsibility by Design

Abstract:

There is growing evidence that digital media usage can become problematic and ‘addictive’. Much research has focused on the role of user personal and social context in developing the problem, and little is known around the role of technology design in triggering and exacerbating the issue.

Digital media are equipped with powerful influence and persuasion techniques, which can increase users’ engagement and retention but at the same time, can be questioned for hurting users’ wellness. At the same time, technology offers an unprecedented opportunity for tools around assisting behavioural change and promoting a more regulated usage style. It can be designed to capture data around digital behaviour and use them to derive interactive intervention techniques and issue them intelligently.

Challenges and risks in designing such tools are paramount, mainly due to the nature of people with problematic behaviour, e.g. denial, trivialisation of the issues, the flight into health and relapse, and also due to the conflicting agendas and priorities in the tech industry. This keynote will summarise the research around the topic and argue the case for Responsibility by Design concept in which tech companies are asked to empower users and their surrogate parties (social or technical) with data and tools to regulate their digital usage and be meaningfully informed about it. The speaker will present recent projects in the gambling and social media domains, conducted closely with charities and tech industry in UK and Europe, and the policy change achieved through them.

Short Bio:

Raian is a professor in Computing at Bournemouth University, UK. He founded and is leading the Engineering and Social Informatics Research Group (ESOTICS), in which the focus is on the inter-relation between technology and social requirements such as motivation, transparency and wellbeing. Raian is leading several projects around making digital media and online gaming and gambling fairer through data-driven real-time transparency to empower users, support the conscious and regulated nature of their usage and increase digital wellness. He frequently provides consultancy and policy advice, nationally and internationally, around the theme. He published over 90 peer-reviewed papers and many are of interdisciplinary nature that embraces elements from software engineering, psychology and marketing. For more information, please see http://rali.bournemouth.ac.uk/