Keynote Speakers

Prof. Yike Guo, Imperial College London, UK

Yike Guo is a Professor of Computing Science in the Department of Computing at Imperial College London. He is the founding Director of the Data Science Institute at Imperial College, as well as leading the Discovery Science Group in the department. Professor Guo also holds the position of CTO of the tranSMART Foundation, a global open source community using and developing data sharing and analytics technology for translational medicine.

Professor Guo received a first-class honours degree in Computing Science from Tsinghua University, China, in 1985 and received his PhD in Computational Logic from Imperial College in 1993 under the supervision of Professor John Darlington. He founded InforSense, a software company for life science and health care data analysis, and served as CEO for several years before the company's merger with IDBS, a global advanced R&D software provider, in 2009.

He has been working on technology and platforms for scientific data analysis since the mid-1990s, where his research focuses on knowledge discovery, data mining and large-scale data management. He has contributed to numerous major research projects including: the UK EPSRC platform project, Discovery Net; the Wellcome Trust-funded Biological Atlas of Insulin Resistance (BAIR); and the European Commission U-BIOPRED project. He is currently the Principal Investigator of the European Innovative Medicines Initiative (IMI) eTRIKS project, a €23M project that is building a cloud-based informatics platform, in which tranSMART is a core component for clinico-genomic medical research, and co-Investigator of Digital City Exchange, a £5.9M research programme exploring ways to digitally link utilities and services within smart cities.

Professor Guo has published over 200 articles, papers and reports. Projects he has contributed to have been internationally recognised, including winning the “Most Innovative Data Intensive Application Award” at the Supercomputing 2002 conference for Discovery Net, and the Bio-IT World "Best Practices Award" for U-BIOPRED in 2014. He is a Senior Member of the IEEE and is a Fellow of the British Computer Society.

Prof. Frans Coenen, University of Liverpool, UK

Frans Coenen has a general background in AI, and has been working in the field of data mining and Knowledge Discovery in Data (KDD) for the last fifteen years. He is particularly interested in: Big Data; Social Network and Trend Mining; the mining of non-standard data sets such as Graph, Image and Document collections; and the practical application of data mining in its many forms. He currently leads a small research group (8 PhDs and 5 RAs) working on many aspect of data mining and KDD. He has some 320 refereed publications on KDD and AI related research, and has been on the programme committees for many KDD events. He is pleased to have been the founder of the UK KDD symposia series, which is now in its eleventh year. Frans Coenen is a member of the IFIP WG12.2 Machine Learning and Data Mining group, The British Computer Society (BCS) and the BCS' Specialist Group on AI (BCS-SGAI). Frans Coenen is currently professor within the Department of Computer Science at the University of Liverpool where he is the director of studies for the department's on-line MSc programmes.

Image Representation for Image Mining: A Study Focusing on Census Prediction using Satellite Image Data

Abstract:Image mining is an important element of the canon of data mining. Decision making is routinely supported by visual information and the visualisation of data. At the same time our ability to collect visual information is increasing rapidly, partly because of technological advancements and partly (and associated with the first) the increasingly reduced cost of collecting such data. Consequently the demand for utilising image data for the purpose of extracting information (image mining) is increasing in a corresponding manner. A taxonomy for image representation in the context of image mining is thus presented. The main premise being that the actual mining algorithms that may be used are well understood, it is the pre-processing of the image data that remains a challenge. The requirement for the output from this pre-processing is some image representation that us both sufficiently expressive while at the same time being compatible with the mining process to be applied. Three categories of representation are considered: statistics based, tree (graph) based and point series based. The suggested taxonomy is then analysed in further detail by considering a novel image mining application, the collection of individual household census data from Google earth satellite imagery. The representations are considered both in terms of generating census prediction models (using classification and regression) and in terms of applying such models for the purpose of larger scale census prediction.


Prof. Dr. Hong Zhu, Oxford Brookes University, United Kingdom

Hong Zhu obtained his BSc, MSc and PhD degrees in Computer Science from Nanjing University, China, in 1982, 1984 and 1987, respectively. He worked at Nanjing University 1987 to November 1998. From October 1990 to December 1994 while on leave from Nanjing University, he was a research fellow at Brunel University and the Open University, UK. He joined Oxford Brookes University, UK, in November 1998 as a Senior Lecturer in Computing and became a Professor of Computer Science in October 2004. Prof. Zhu chairs the Applied Formal Methods Research Group of the Department of Computing and Communication Technologies. He is a senior member of IEEE Computer Society, a member of British Computer Society, ACM, and China Computer Federation. His research interests are in the area of software development methodologies, including formal methods, agent-orientation, automated software development, foundation of software engineering, software design, modelling and testing methods, Software-as-a-Service, etc. He has published 2 books and more than 180 research papers in journals and international conferences. He has been a conference program committee chair of SOSE 2012 and ICWS 2015, etc., a conference general chair of SOSE 2013, MobileCloud 2014, MS 2016, EDGE 2017, etc. He is a member of the editorial board of the journal of Software Testing, Verification and Reliability, Software Quality Journal, International Journal of Big Data Intelligence, and the International Journal of Multi-Agent and Grid Systems.


Prof. Alfredo Cuzzocrea, University of Trieste, Italy

Alfredo Cuzzocrea is currently Associate Professor in Computer Science Engineering at the DIA Department, University of Trieste, Italy. He is also habilitated as Full Professor in Computer Science Engineering by the the French National Scientific Habilitation of the National Council of Universities (CUN) under the egira of Ministry of Higher Education and Research (MESR). Previously, he has been Senior Researcher at the Institute of High Performance Computing and Networking of the Italian National Research Council, Italy, and Adjunct Professor at the University of Calabria, Italy. He got the habilitatation as Associate Professor in Computer Science Engineering by the Italian National Scientific Habilitation of the Italian Ministry of Education, University and Research (MIUR). During the past, he has also been Adjunct Professor at the University of Catanzaro “Magna Graecia”, Italy, Adjunct Professor at the University of Messina, Italy, and Adjunct Professor at the University of Naples “Federico II”, Italy. Previously, he was Adjunct Professor at the University of Naples “Parthenope”, Italy. He holds 36 visiting positions worldwide (Europe, USA, Asia, Australia). He serves as Springer Fellow Editor. He serves as Elsevier Ambassador. He holds several roles in international scientific societies, steering committees for international conferences, and international panels, some of them having directional responsibility. He served as Panel Leader and Moderator in international conferences. He served as Invited Speaker in several international conferences worldwide (Europe, USA, Asia). He is member of scientific boards of several PhD programs worldwide (Europe, Asia, Australia). He serves as Editor for the Springer series “Communications in Computer and Information Science”. He covers a large number of roles in international journals, such as Editor-In-Chief, Associate Editor, Special Issue Editor (including JCSS, IS, KAIS, FGCS, DKE, INS, BigData Research). He edited more than 30 international books and conference proceedings. He is member of editorial advisory boards of several international books. He covers a large number of roles in international conferences, such as General Chair, Program Chair, Workshop Chair, Local Chair, Liaison Chair and Publicity Chair (including CSE, ODBASE, DaWaK, DOLAP, ICA3PP, ICEIS, APWeb, SSTDM, IDEAS, IDEAL). He served as Session Chair in a large number of international conferences (including EDBT, CIKM, DaWaK, DOLAP, ADBIS). He serves as Review Board Member in a large number of international journals (including TODS, TKDE, TKDD, TSC, TIST, TSMC, THMS, JCSS, IS, KAIS, FGCS, DKE, INS). He serves as Review Board Member in a large number of international books. He serves as Program Committee Member in a very large number of international conferences (including VLDB, ICDE, EDBT, CIKM, IJCAI, KDD, ICDM, PKDD, SDM). His current research interests include multidimensional data modeling and querying, data stream modeling and querying, data warehousing and OLAP, OLAM, XML data management, Web information systems modeling and engineering, knowledge representation and management models and techniques, Grid and P2P computing, privacy and security of very large databases and OLAP data cubes, models and algorithms for managing uncertain and imprecise information and knowledge, models and algorithms for managing complex data on the Web, models and algorithms for high-performance distributed computing and architectures. He is author or co-author of more than 340 papers in international conferences (including EDBT, CIKM, SSDBM, MDM, DaWaK, DOLAP), international journals (including JCSS, IS, KAIS, DKE, INS) and international books (mostly edited by Springer). He is also involved in several national and international research projects, where he also covers responsibility roles.


Assoc. Prof. Dr. Huseyin Seker, The University of Northumbria at Newcastle, UK

Dr. Huseyin Seker, The University of Northumbria at Newcastle, UK Huseyin Seker was awarded a full PhD scholarship by Coventry University (UK) and finished his PhD in Biomedical Computing in 2005. He is currently Reader (Assoc. Prof.) in Computer Science and Digital Technologies within the University of Northumbria at Newcastle (UK). He was previously awarded the British Council Chevening scholarship and worked as an honorary research assistant within the Medical Physics Division of the University of Leicester (UK). In addition, he worked as a lecturer and senior lecturer at the Faculty of Technology of De Montfort University (UK) where he was also appointed as the Faculty Head of Research Operations. He has received scholarships and research funding from various organisations including the British Council, Coventry University, Isaac Newton Institute for Mathematical Sciences (the University of Cambridge), HOPE against Cancer Foundation and European Mathematical Society. He was also a recipient of an international paper award from IEEE EMBC in 2002. His research interest includes Big Data Analytics, Computational Intelligence, Bio-Health Informatics, Bioinformatics and Systems Biology. He has published about 100 conference and journal papers. His research has also appeared on BBC and Photonics Magazine. He chairs the International Conference on Applied Informatics for Health and Life Sciences (Istanbul in 2013 and Kusadasi in 2014) and organises the invited special sessions “Bringing big data to its knees” (IEEE EMBC in Osaka in 2013 and Chicago in 2014). In addition, his team has recently developed CISAPS webserver for the analysis of protein sequences and released one of the most comprehensive and expanded Amino Acid Index Databases.