Prof. Chrsitophe Claramunt
Shanghai Maritime University (China) & Naval Academy Research Institute (France)
Professor Christophe Claramunt is currently the chair of the Naval Academy Research Institute in France. He was previously a senior lecturer in computing at the Nottingham Trent University and senior researcher at the Swiss Federal Institute of Technology in Lausanne. His research is oriented towards theoretical, computational and pluri-disciplinary aspects of geographical information systems. Over the past few years he has been regularly involved in EU funded projects such as the H2020 project datAcron "Big Data Analytics for Time Critical Mobility Forecasting". Amongst other affiliations, he is a research fellow at the Research Center for Social Informatics of the Kwansei University in Japan, Centre for Planning Studies at the Laval University and at the Joint Laboratory for Geographical Information Science at the Chinese University of Hong Kong.
See also http://christophe.claramunt.free.fr/
Title: "Exploring Big Data in the Maritime Domain"
Abstract: The maritime environment provides many novel research challenges & application opportunities that still deserve to be addressed. In the maritime domain, the correlated computational exploitation of heterogeneous big data sources and streaming data is a crucial issue., while online tracking, early recognition of events, and anticipation of vessel trajectories are particularly crucial to safety and operations at sea. The objective of this talk is to review current research challenges and development directions tied to the integration, management, analysis, and visualization of objects moving at sea as well as a few computational solution for a successful development of maritime forecasting and decision-support systems. The talk will focus on developments currently addressed by the EU funded H2020 datAcron project.
Prof. Ping GUO
Beijing Normal University, China
Ping Guo, Professor, IEEE senior member, CCF senior member, School of systems science, Beijing Normal University; and Ph. D. supervisor in computer software and theory of Beijing Institute of Technology. Chair of the Key Laboratory of graphics, image and pattern recognition, Beijing Normal University, Chair of IEEE CIS Beijing Chapter (2015-2016). His research interests include computational intelligence theory and its applications inpattern recognition, image processing, software reliability engineering, and astronomical data processing. He has published more than 300 papers, hold 6patents, and the author of two books: “Computational intelligence in software reliability engineering”, and “Image semantic analysis.” received 2012 Beijing municipal government award of science and technology (third rank) entitled "regularization method and its application".
Professor Guo received his master's degree in optics from the Department of physics, Peking University, and received his Ph.D degree from the Department of computer science and engineering, Chinese University Hong Kong.
See also http://sss.bnu.edu.cn/~pguo
Title: "Astronomical Big Data Analysis with Computational Intelligence Techniques"
Abstract: With explosive growth of the astronomical data, astronomy has become a representative data-rich discipline so as to defy traditional research methodologies and paradigm to analyze data and discover new knowledge from the data. How to effectively process and analyze the astronomical data is a fundamental workwhile a key scientific requirement of modern astronomical surveys. This situation has motivated needs for fostering of a wide range of cooperation with the astronomers and computer scientists. Computational intelligence, a new development stage of the artificial intelligence, has been shown to be promising to solve complex problems in scientific research and engineering.This talk presents a review of the current state of the application of computational intelligence in astronomy. We believe that computational intelligence is expected to provide powerful tools for addressing challenges in astronomical big data analysis.
Assoc. Prof. Paul Kennedy
University of Technology, Sydney
Associate Professor Paul Kennedy is Head of Discipline (Data Analytics / Artificial Intelligence) in the School of Software, Faculty of Engineering & IT, University of Technology Sydney. He is also Director of the Biomedical Data Science Laboratory in the UTS Centre for Artificial Intelligence. He has a PhD (Computing Science) and joined UTS in 1999. Paul’s research interests are in data analytics in the biomedical domain. He is co-initiator of a 15 year research collaboration with the Tumour Bank at The Children’s Hospital at Westmead. That work develops data analytics approaches to predict treatment outcomes and aggressiveness of childhood cancers, including Acute Lymphoblastic Leukaemia and Neuroblastoma, with the aim of helping medical researchers understand the diseases and to inform clinicians in devising their treatments. He also has interests in bioinformatics and text analytics.
See also http://www.paul-kennedy.weebly.com
Title: "Cross-disciplinary Data Analytics Research in the Childhood Cancer Domain"
Abstract: Biomedical data analysis presents many interesting and challenging problems, most notably those stemming from high dimensional, noisy, unbalanced genomic datasets. Data analytics in the childhood cancer domain is more challenging because, being associated with rare diseases, the data has few instances relative to the dimensionality. This talk describes a multi-disciplinary research collaboration we have built over 15 years. It outlines a pipeline we have developed to deal with the paediatric cancer data and some of the methods developed by my team, specifically focusing on acute lymphoblastic leukaemia and neuroblastoma. Apart from data analysis techniques, the talk aims to reflect on how to enhance cross-disciplinary research, identifying barriers and success indicators.