Prof. Robert Minasian, IEEE & OSA Fellow, The University of Sydney, Australia
Professor Minasian is a Chair Professor with
the School of Electrical and Information Engineering
at the University of Sydney, Australia. He is also
the Director of the Fibre-optics and Photonics
Laboratory. His research has made key contributions
to microwave photonic signal processing. He is
recognized as an author of one of the top 1% most
highly cited papers in his field worldwide.
Professor Minasian has contributed over 360 research
publications, including Invited papers in the IEEE
Transactions and Journals, and Plenary and Invited
papers at leading international conferences.
Professor Minasian was the recipient of the ATERB
Medal for Outstanding Investigator in
Telecommunications, awarded by the Australian
Telecommunications and Electronics Research Board.
He is a Fellow of the IEEE, and a Fellow of the
Optical Society of America.
Speech Title: Progress in photonic signal
Abstract: Next generation signal processing
systems will require new technologies to address the
current limitations for growth in capacity and
versatility in areas such as 5G and Internet of
Things (IoT). Microwave photonics, which merges the
worlds of RF and photonics, shows strong potential
as a key enabling technology to enable new paradigms
in the processing of high speed signals and in
sensing that can overcome inherent electronic
limitations. Photonic signal processors are
intrinsically compatible with optical-wireless
systems, and can provide connectivity with in-built
signal conditioning, while also providing important
advantages of EMI immunity. Moreover, photonic
integration on semiconductor material platforms that
co-exist with CMOS electronics enables boosting the
performance of future systems performing sensing and
communications with the potential for implementing
high bandwidth, fast and complex functionalities.
Recent photonic signal processing advances will be
presented including widely tunable single passband
filters, high-speed frequency converters, and a
range of novel high-resolution sensors based on
microwave photonic techniques.
Prof. Mao Kezhi, Nanyang Technological University, Singapore
Dr. Mao obtained his BEng, MEng and PhD from Jinan University, Northeastern University, and University of Sheffield in 1989, 1992 and 1998 respectively. Since obatining his PhD, he has been working at School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, where he is an Associate Professor. Dr. Mao has over 20 years of experience in artificial intelligence, machine learning, big data/text analytics, natural language processing, and information fusion. He has published about 100 scientific papers in the field, and has successfully developed and delivered several intelligent systems and tools to government agencies and industries. Besides research and development work, Dr. Mao has been conducting consultancy work and training courses in the field. His research interests include Machine Learning, Deep Learning for Big Data, Information Fusion, Image/Video Analytics and Scene Understanding, Knowledge Extraction and Discovery.
Prof. Xudong Jiang, Nanyang Technological University, Singapore
Prof. Xudong Jiang received the B.Sc. and M.Sc. degree from the University of Electronic Science and Technology of China, in 1983 and 1986, respectively, and received the Ph.D. degree from Helmut Schmidt University Hamburg, Germany in 1997, all in electrical and electronic engineering. From 1986 to 1993, he worked as Lecturer at the University of Electronic Science and Technology of China where he received two Science and Technology Awards from the Ministry for Electronic Industry of China. He was a recipient of the German Konrad-Adenauer Foundation young scientist scholarship. From 1993 to 1997, he was with Helmut Schmidt University Hamburg, Germany as scientific assistant. From 1998 to 2004, He worked with the Institute for Infocomm Research, A*Star, Singapore, as Senior Research Fellow, Lead Scientist and appointed as the Head of Biometrics Laboratory where he developed a fingerprint verification algorithm that achieved the fastest and the second most accurate fingerprint verification in the International Fingerprint Verification Competition (FVC2000). He joined Nanyang Technological University, Singapore as a faculty member in 2004 and served as the Director of the Centre for Information Security from 2005 to 2011. Currently, Dr Jiang is a tenured Associate Professor in School of Electrical and Electronic Engineering, Nanyang Technological University. Dr Jiang has published over hundred research papers in international refereed journals and conferences, some of which are well cited on Web of Science. He is also an inventor of 7 patents (3 US patents), some of which were commercialized. 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 Chair, Keynote Speaker and Session Chair of multiple international conferences. His research interest includes pattern recognition, computer vision, machine learning, image analysis, signal/image processing, machine learning and biometrics.
Speech Title: The Role of Dimensionality Reduction for Classification
Abstract: Finding/extracting low-dimensional structures in high-dimensional data is of increasing importance, where data/signals lie in observational spaces of thousands, millions or billions of dimensions. Many biomedical and bioinformatics datasets have very high dimension and low number of samples. The curse of dimensionality is in full play here: We have to conduct inference with a limited or no human knowledge. Machine learning is a solution that becomes hotter and hotter to boiling. This is evidenced by numerous techniques published in the past decade, many of which are in prestige journals. Nevertheless, there are some fundamental concepts and issues still unclear or in paradox. For example, we often need many processing steps in a complex information discovery/recognition system. As the information amount cannot be increased and must be reduced by any processing, why do we need it before the main processing? This seemly simple question easily answerable if each step uses different prior knowledge is nontrivial in machine learning. People proposed numerous machine learning approaches but seem either unaware of or avoiding this fundamental issue. Although extracting the most discriminative information is indisputably the ultimate objective for pattern recognition, this talk will challenge it as a proper or effective criterion for the machine learning-based dimension reduction or information/feature extraction, despite the fact that it has been employed by almost all researchers.