Keynote Speaker I
Department of Biomedical Engineering, Linkoping University, Sweden
Tuan D. Pham is Professor of Biomedical Engineering at Linkoping University, University Hospital Campus, Linkoping, Sweden. Prior to the current position, he was appointed as Professor and Leader of the Aizu Research Cluster for Medical Engineering and Informatics, and the Medical Image Processing Lab, both at the University of Aizu, Japan. Before his appointments in Japan, he was the Bioinformatics Research Group Leader at the University of New South Wales, Canberra, Australia. He has been an Editorial Member and Associate Editor of Pattern Recognition (Elsevier), served as Guest Editor of Computer Methods and Programs in Biomedicine (Elsevier), Computers in Medicine and Biology (Elsevier), BioMedical Engineering OnLine (BioMed Central), and Associate Editor of IEEE Engineering in Medicine and Biology Conference series. Dr. Pham has published extensively on pattern recognition, image processing, and time-series analysis in medicine, biology, and mental health.
Speech Title: "Recent Developments in Image and Time-Series Analyses in Medicine and Physiology"
Abstract: Image and time-series analyses are important methods for automated pattern recognition in medicine and physiology because they help improve the diagnosis, prognosis, and decision making for disease treatment. This talk will present recent developments in texture analysis, fuzzy recurrence plots, and time-shift multiscale entropy with applications to the differentiation of malignant mediastinal lymph nodes from benign nodes in patients with lung cancer on computed tomography, burn-scar assessment on digital color images, and several types of physiological time series for the classification of neurological disorders.
Keynote Speaker II
Nanyang Technological University, Singapore
Dr. Lipo Wang received the Bachelor degree from National University of Defense Technology (China) and PhD from Louisiana State University (USA). His research interest is intelligent techniques with applications to optimization, communications, image/video processing, biomedical engineering, and data mining. He is (co-)author of 300 papers, of which 100 are in journals. He holds a U.S. patent in neural networks and a Chinese patent in VLSI. He has co-authored 2 monographs and (co-)edited 15 books. He was/will be keynote/panel speaker for 30 international conferences. He is/was Associate Editor/Editorial Board Member of 30 international journals, including 3 IEEE Transactions, and guest editor for 10 journal special issues. He was a member of the Board of Governors of the International Neural Network Society (for 2 terms), IEEE Computational Intelligence Society (CIS, for 2 terms), and the IEEE Biometrics Council. He served as CIS Vice President for Technical Activities and Chair of Emergent Technologies Technical Committee, as well as Chair of Education Committee of the IEEE Engineering in Medicine and Biology Society (EMBS). He was President of the Asia-Pacific Neural Network Assembly (APNNA) and received the APNNA Excellent Service Award. He was founding Chair of both the EMBS Singapore Chapter and CIS Singapore Chapter. He serves/served as chair/committee members of over 200 international conferences.
Speech Title: "Biomedical Data Mining and Image Processing Using Machine Learning"
Abstract: This talk highlights some of our recent research results in biomedical data mining and image processing using machine learning. Our techniques include compact radial-basis-function (RBF) neural networks, support vector machines with graded resolution, class-dependent feature selection, semi-exhaustive feature search, and other novel feature selection algorithms. We demonstrate our algorithms in various challenging biomedical data mining and image processing problems, such as gene selection in microarray data, SNP selection and population classification, glaucoma diagnosis, and face recognition.
Keynote Speaker III
Chulalongkorn University, Thailand
Plenary Speaker I
Y. L. Chin
Hang Seng Management College, Hong Kong
Professor Chin received his B.A.Sc. degree from the University of Toronto in 1972, and his M.S., M.A. and Ph.D. degrees from Princeton University in 1974, 1975, and 1976, respectively. Prior to joining The University of Hong Kong (HKU) in 1985, he had taught at the University of Maryland, Baltimore County; the University of California, San Diego; the University of Alberta; the Chinese University of Hong Kong; and the University of Texas at Dallas. Professor Chin was the Chair of the Department of Computer Science at HKU and was the founding Head of the Department from its establishment until December 31, 1999. From 2002 until July 31, 2006, he had served as the Associate Dean of the Graduate School. From 2007 to his retirement from HKU in 2015, Prof Chin had served as an Associate Dean of the Faculty of Engineering. Professor Chin is an IEEE Fellow and his research interests include design and analysis of algorithms, machine learning, and bioinformatics including Motif-finding (Motif discovery) and De Novo genome assembly (IDBA). Professor Chin is now an Emeritus Professor of The University of Hong Kong. He is now working as the Chair Professor and Head of Department of Computing at Hang Seng Management College and is in-charge of a Hong Kong RGC-funded project on Deep Learning.
Speech Title: "Meta-genomics Assembly and Binning"
Abstract: Meta-genomics is the study of the collective genomes of all microorganisms from an environmental sample (also known as environmental genomics, or community genomics), for example, the diversity of microbes in humans is found to be associated with some common diseases such as gastrointestinal disturbance and inflammatory bowel disease. High-throughput next-generation sequencing (NGS) techniques enable researchers to directly sequence the genomes of multiple species obtained from such an environmental sample for analysis. In this talk, we address two important problems in meta-genomic analysis based on NGS data: (1) assembly - to reconstruct the genomes of each species; (2) binning - to group DNA fragments (or reads) from similar species together. Both of these problems are very difficult especially when up to 99% of the species found in environmental samples are unknown. My bioinformatics team at HKU has developed a series of IDBA assembly and MetaCluster binning tools for meta-genomics study. The main ideas of these software tools will be presented in this talk.