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Keynote Speakers

 

Prof. Francis Y. L. Chin
University of Hong Kong, Hong Kong

Francis Y. L. 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.

Speech Title : "Why Genome Assembly so Difficult?"

Abstract: It has been about 60 years since Watson and Crick first discovered the double-helix structure of DNA. Each genome (about 3 billion long) define every human uniquely (e.g. hair colour, eye colour, etc.) as well as one's genetic diseases. Consequently, there is a need to find the genome of each individual for assessing the genetic risk of potential diseases. At the same time, research groups are sequencing the DNA of all kinds of organisms, e.g., the rice genome in search of higher production yields, the bacteria genome in search of a more effective cure, and the orchid genome in search of more varieties and higher financial returns.
To sequence a genome, Next-Generation Sequencing (NGS) technology is commonly used to output billions of overlapping DNA fragments (known as reads) from the genome, but without information on how these reads link together to form the genome. Then, effective sequencing software tools are used to combine these reads to form the genome. This process is called "genome assembly".
Theoretically, genome assembly is an easy task as the chance of mis-matching two reads is extremely low if they overlap 30-40 positions (because of 4^30 >>> 3x10^9). In this talk, we shall review past developments and difficulties of genome assembly and explain why some of the straightforward approaches fail. The most successful and counter-intuitive approach which breaks the reads into smaller parts before assembly will be introduced. Our recent work to develop more efficient algorithms and software tools for genome assembly will also be discussed.

Prof. Ming Chen
Zhejiang University, China

Ming Chen received his PhD in Bioinformatics from Bielefeld University, Germany, in 2004. Currently he is working as a full Professor in Bioinformatics at College of Life Sciences, Zhejiang University. His group research work mainly focuses on the systems biology, computational and functional analysis of non-coding RNAs, and bioinformatics research and application for life sciences. Prof. Chen is serving as an academic leader in Bioinformatics at Zhejiang University. He chairs the Bioinformatics society of Zhejiang Province, China. He is a committee member of Chinese societies for "Modeling and Simulation of Biological Systems", "Computational Systems Biology", "Functional Genomics & Systems Biology" and "Biomedical Information Technology".

Speech Title : "Non-Coding RNAs and their Versatile Interactions"

Abstract: Advances in RNA sequencing technologies and computational methodologies have provided a huge impetus to non-coding RNA (ncRNA) study. Previously we developed miRNA target prediction/identification approaches and constructed comprehensive miRNA- and miRNA*- mediated regulatory networks. In this talk, we provide an overview of ncRNA repertoire and highlight recent discoveries of their versatile interactions. Several ncRNA regulatory network studies are introduced: 1.) the effect of 3D architecture of chromatin on the transcriptional regulation of microRNAs; 2.) miRNA–miRNA functionally synergistic network based on the functions of miRNA targets and their topological features in different cancer cell types; 3.) functional elements embedded in lncRNAs and lncRNA-based regulatory networks; and 4.) circRNA–miRNA–mRNA regulatory networks. Moreover, to better investigate the ncRNA-mediated regulation, we describe a comprehensive workflow for in silico ncRNA analysis, providing up-to-date platforms, databases and tools dedicated to ncRNA identification and functional annotation.

Prof. Jing Bai
University of Minnesota Duluth, USA

Jing Bai is a tenured Full Professor and the Director of Graduate Studies (DGS) in Department of Electrical Engineering at the University of Minnesota Duluth, where she started as an Assistant Professor in August 2007. She received her Ph.D. degree and MS degree in Electrical and Computer Engineering at Georgia Institute of Technology in 2007 and 2003, respectively. She also earned the MEng degree in Nanyang Technological University (NTU), Singapore in 1999 and the BEng degree in Tsinghua University, P. R. China, in 1996 both in Mechanical Engineering.  From 1999 to 2001, she worked at Micromachines Laboratory in NTU as a research engineer.  She was also an invited guest researcher at the National Institute of Advanced Industrial Science and Technology (AIST), Japan from May to June 2000. She is the recipient of the 2012 SCSE Young Teacher Award.

Speech Title : "Dynamic Behavior of Nonlinear Dispersive Quantum-Cascade Lasing Medium under Different Cavity Configurations"

Abstract: In this talk, we will present a comprehensive study on the dynamics of coherent pulse propagation in a nonlinear dispersive quantum-cascade laser (QCL) medium emitting at the mid-infrared (MIR) region. Interactions among group-velocity dispersion, longitudinal and transverse Kerr nonlinearities are analyzed. The study is carried out based on the two-level Maxwell-Bloch formulism for QCLs. The coherence effect is accounted through the couplings among the electric field, the polarization and the population inversion. We performed the study on two kinds of typical waveguide configurations, i.e., the ring and Fabry-Perot cavities. Effects of cavity configurations are compared. Results obtained will provide design guidance on MIR QCLs toward desired applications through tailoring various intracavity physics phenomena and cavity configurations.

Prof. Philip O. Ogunbona

University of Wollongong, Australia

Philip Olurotimi Ogunbona was educated in Nigeria where he obtained the BSc(Hons)(1st Class) of Electronic and Electrical Engineering from the University of Ife, He studied at the Department of Electrical and Electronic Engineering, Imperial College of Science, Medicine and Technology, University of London and obtained the DIC and PhD for research conducted in the field of Image Processing. He joined the University of Wollongong, School of Electrical, Computer and Telecommunications Engineering in 1990. He left the University in 1998 to join the Visual Information Processing Lab, Motorola Labs in Sydney. He was Principal Research Engineer and later became the foundation Manager of the Digital Media Collection and Management Lab, Motorola Labs, Sydney. While at Motorola Labs, he worked on a range of research projects including, image and video segmentation, image compression (he was part of the Motorola team that worked on the JPEG2000 standardization), digital camera image processing, stereo image processing, multimedia security (watermarking and authentication) and multimedia content management for broadband applications. Apart from the many publications emanating from the research output, Philip was also co-author of several patent disclosures. He currently has four patents filed in the US and has published over 100 journal and conference papers. His current research interests include image and video processing, video surveillance, multimedia security and multimedia content management. He is a Senior Member of the IEEE and member of the IEEE NSW Committee. He has also served as the Chair of the IEEE Joint Chapter of the Communications and Signal Processing. In 2004, Philip returned to the University of Wollongong, School of InformationTechnology and Computer Science, where is now Professor and Head of School. He is also the Director of the Centre for Visual Information Processing and Content Management Research within the School.

Speech Title : "Engineering in the Age of Deep Learning"

Abstract: Machine learning, especially deep learning has recently revolutionized the landscape of engineering and computer science research and practice. In this key note address we provide a survey of some of the important results in the last 5 years. This survey will take us through aspects of power engineering, control engineering, communication engineering, computer vision, medical image analysis and social media data analytics. We conclude the address with examples of our work in computer vision.

Prof. Hyoungseop Kim

Kyushu Institute of Technology, Japan

Hyoungseop Kim received his B.A. degree in electrical engineering from Kyushu Institute of Technology in 1994, the Masters and Ph.D. degree from Kyushu Institute of Technology in 1996 and 2001, respectively. He is a professor in the department of control engineering at Kyushu Institute of Technology. His research interests are focused on medical application of image analysis.

Speech Title: "Computer Aided Diagnosis ~ Conventional Pattern Recognition and Deep Learning"

Abstract: For reducing the load to radiologist and improving of detection accuracy, a CAD (Computer Aided Diagnosis) system is expected from medical fields. In the medical image processing fields, some related works such as artificial neural networks and support vector machine are reported to develop the CAD system as helpful technical issues. In this talk, I will introduce why CAD is required in medical field. Then I will show you some CAD systems such as conventional classifier and deep learning techniques for supporting to radiologists based on pattern recognition techniques.

Dr. Lucia Ballerini

University of Edinburgh, UK

Lucia Ballerini is an expert in image analysis. She developed novel image analysis algorithms and demonstrated their successful applications in many domains. She published over 100 peer-reviewed scientific articles. Lucia Ballerini graduated in Electronic Engineering at the University of Florence in 1993. She received the PhD degree in Bioengineering in 1998, and the "Docent" in Image Analysis at Uppsala University in 2006. She has been working at the Centre for Image Analysis, Uppsala and at the European Centre for Soft Computing, Mieres, Spain. She moved to Edinburgh, UK in 2008, where the main projects she has been involved are:

Dermofit: http://www.dermofit.org (now a commercial product)  

VAMPIRE: http://vampire.computing.dundee.ac.uk/ (software suite for retinal image analysis distributed to many centres around the world)

She is now a Research Associate in brain imaging at the University of Edinburgh, working on these projects:   LBC1936: https://www.lothianbirthcohort.ed.ac.uk/ (developing image abalysis tools for brain MRI structural analysis)

EPSRC Multi-modal retinal biomarkers for vascular dementia: developing enabling image analysis tools Leducq https://www.small-vessel-disease.org/ (working on quantitative computational methods for assessing Perivascular Spaces)

Speech Title: "Image Analysis in Small Vessel Disease"

Abstract: Small vessel diseases (SVDs) are a group of disorders that result from pathological alteration of the small blood vessels in the brain. It is responsible for a large proportion of the cases of stroke and dementia worldwide. Magnetic Resonance Imaging (MRI) images from patients with SVD show characteristic abnormalities, such as white matter hyperintensities (WMHs), cerebral microbleeds, lacunes and enlarged perivascular spaces (PVS). In this talk I will review MRI imaging protocols and emerging imaging methods for detection and quantification of features of SVD.