DR. GIRIJA CHETTY UNIVERSITY OF CANBERRA, CANBERRA, AUSTRALIA
Title: Deep Fusion Technologies for Computational
Abstract: We are currently living in a complex world, with global turmoil due to several reasons, including threats associated with environmental issues, resource shortages, ethnic conflicts, terrorism events, and other unexpected natural disasters. To address these unforeseen turbulences, humans and machines need to work collectively at the global level to change our ways of interacting with one another, and with the nature and the other living species around us. Technology can come to the rescue in a big way, and by using advances in cutting edge information and communication technologies, such as AI, Big Data, Machine Learning and Information Fusion, it is possible to develop computational collective intelligence platforms for an improved actionable intelligence with better strategies for solving the complex problems, the humanity is currently facing. However, this is easier said than done. This is due to the inherent complexity of the real-world phenomena, and its internal workings, for the above mentioned complex scenarios, as it is often difficult to extract complete knowledge about the physical process of interest, from a single source or information channel. One main reason could be existence of complex information in multiple layers, with knowledge hidden and embedded within these natural phenomena. A detailed understanding, modelling and characterization of such processes can be done, with contributions from several different types of human as well as machine based systems, sensors, and computational frameworks, for providing high quality, efficient and robust technological platforms and tools for humans to deal with these challenges. The concept of 'multi-modality' and ‘data-fusion’ can often be leveraged in this context, which in general, refers to information acquisition about the process or phenomena, from multiple information sources or channels. By utilizing different data driven approaches - with information extraction from multiple disparate modalities or various representations of these modalities, to inform about the same process or phenomena, it is possible to obtain better actionable intelligence, with more complementary information, and with more degrees of freedom, leading to improved solutions to complex and challenging problems. However, using information from multiple different channels, can have a downside, with massive data deluge from several redundant sources, with important information getting buried within the big data stores, and difficulties in making any sense out of it.
The two key questions that need to be addressed in these situations are: “Is it possible to exploit the complementary, competitive and collective information available from multiple modalities and sources effectively?”, and if yes, “how to exploit this rich information synergistically”? In this talk, a novel computational collective intelligence (C2I) framework being developed in our research lab, based on a novel deep fusion paradigm, with integration of multisensory information from multiple disparate sources, and computational approaches based on Artificial Intelligence and Machine Learning techniques will be presented. The experimental validation and performance evaluation of the proposed computational algorithmic framework and its implementation as an open source technology platform, for several publicly available benchmark datasets corresponding to several real world problem scenarios, in security, health and environmental applications contexts, has resulted in promising outcomes, with several funded research projects, peer reviewed publications, algorithm workflows, and open source software tools - facilitating the vision of achieving global C2I systems, with humans and computers working together in harmony, and making this world a better place to live.
Bio: Dr. Girija Chetty has a Bachelors and Masters degree in Electrical Engineering and Computer Science, and PhD in Information Sciences and Engineering from Australia. She has more than 25 years of experience in Industry, Research and Teaching from Universities and Research and Development Companies from India and Australia, and has held several leadership positions including Head of Software Engineering and Computer Science, and Course Director for Master of Computing Course. Currently, she is the Head of Multimodal Systems and Information Fusion Group in University of Canberra, Australia, and leads a research group with several PhD students, Post Docs, research assistants and regular International and National visiting researchers. She is a Senior Member of IEEE, USA, and senior member of Australian Computer Society, and her research interests are in the area of multimodal systems, computer vision, pattern recognition and image processing. She has published extensively with more than 120 fully refereed publications in several invited book chapters, edited books, high quality conference and journals, and she is in the editorial boards, technical review committees and regular reviewer for several IEEE, Elsevier and IET journals in Computer Vision, Pattern Recognition and Image Processing.
Prof. Xudong Jiang, Nanyang Technological University, Singapore
Bio: Xudong Jiang is currently in the School of Electrical and Electronic Engineering as Professor and Director of the Centre for Information Security. He received his Bachelor and Master degrees from the University of Electronic Science and Technology of China, in 1983 and 1986, and the Ph.D. degree from the University of German Federal Armed Forces, Hamburg, Germany, in 1997, all in electrical and electronic engineering. From 1986 to 1993, he was a Lecturer at the University of Electronic Science and Technology of China where he received two Science and Technology Awards from the Ministry of Electronic Industry of China. From 1993 to 1997, he was with the University of German Federal Armed Forces Hamburg, Germany, as a scientific assistant. From 1998 to 2002, he was with Nanyang Technological University, Singapore, as a Senior/Research Fellow where he developed a fingerprint verification algorithm that achieved the fastest speed with the second most accuracy in the International Fingerprint Verification Competition (FVC2000). From 2002 to 2004, he was a Lead Scientist and Head of the Biometrics Laboratory at the Institute for Infocomm Research, A*STAR, Singapore. Dr Jiang has published over 100 international conference and journal papers. He is also an inventor of 7 patents (3 of them are United States patents). Dr Jiang is a senior member of IEEE and has been serving as Editorial Board Member, Guest Editor and Reviewer of multiple international journals, and serving as Program Committee member, Keynote Speaker and Session Chair of multiple international conferences.
Prof. Felix T. S. Chan,
The Hong Kong Polytechnic University
Bio: Professor Felix T. S. Chan received his BSc Degree in Mechanical Engineering from Brighton Polytechnic (now University), UK, and obtained his MSc and PhD in Manufacturing Engineering from the Imperial College of Science and Technology, University of London, UK. Professor Chan is now working at the Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, and also serving as Associate Dean (Research) at the Faculty of Engineering. His current research interests are Logistics and Supply Chain Management, Operations Management, Distribution Coordination, Systems Modelling and Simulation, AI Optimisation. To date, he has published 16 book chapters, over 300 refereed international journal papers and 250 peer reviewed international conference papers, h index= 30 under the Web of Science. He is a chartered member of the Chartered Institute of Logistics and Transport in Hong Kong. According to a study lately published in the International Journal of Production Research (http://dx.doi.org/10.1080/00207543.2015.1037935), the study measured the research contributions over a 26-year time frame (1985–2010) of academic institutions and individual authors to the field of Operations Management (OM) based on published articles in 11 top-rated and well-known academic OM journals. Professor Chan was among the top 50 prolific authors list who have made the greatest overall contribution to the field as measured by the number of distributed and shared articles published in the 11 designated journals. Also, Professor Chan was Ranked No. 3 in The top 100 authors as the most productive researchers in the field of Operations Management over the past 10 years (2001–2010).