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Keynote&Invited Speakers

In order to deepen the communication in all the participants, ICCBB 2019 have invited professors from local Japan and all over the world to have speeches about Computational Biology and Bioinformatics and related fields.

Keynote Speaker I

Prof. Hesham H. Ali, University of Nebraska at Omaha, USA

Hesham H. Ali is a Professor of Computer Science and Lee and Wilma Seemann Distinguished Dean of the College of Information Science and Technology at the University of Nebraska at Omaha (UNO). He also serves as the director of the UNO Bioinformatics Core Facility that supports a large number of biomedical research projects in Nebraska. He has published numerous articles in various IT areas including scheduling, distributed systems, data analytics, wireless networks, and Bioinformatics. He has also published two books in scheduling and graph algorithms, and several book chapters in Bioinformatics. He has been serving as the PI or Co-PI of several projects funded by NSF, NIH and Nebraska Research Initiative in the areas of data analytics, wireless networks and Bioinformatics. He has also been leading a Research Group that focuses on developing innovative computational approaches to classify biological organisms and analyze big bioinformatics data. The research group is currently developing several next generation big data analytics tools for mining various types of large-scale biological and medical data. This includes the development of new graph theoretic models for assembling short reads obtained from high throughput instruments, as well as employing a novel correlation networks approach for analyzing large heterogeneous biological and health data associated with various biomedical research areas, particularly projects associated with aging and infectious diseases. He has also been leading two funded projects for developing secure and energy-aware wireless infrastructure to address tracking and monitoring problems in medical environments, particularly to study mobility profiling for healthcare research.

Invited Speaker I

Assoc. Prof. Dakun Lai, University of Electronic Science and Technology of China, China

Dr. Lai is currently the director of the Biomedical Imaging and Electrophysiology Lab at the University of Electronic Science and Technology of China (UESTC). He received his Ph.D. in Medical Electronics from Fudan University in 2008. Then he completed a three-year Postdoctoral Associate in Biomedical Engineering at the University of Minnesota, USA. From 2012, he has been on the faculty of the School of Electronic Science and Technology, UESTC, China, where he was appointed as an Associate Professor of Electrical Science and Technology. Dr. Lai is members of IEEE and the Engineering in Medicine and Biology Society, and the member of American Heart Associate. He has served as a peer reviewer of IEEE Transction on Biomedical Egnineering, IEEE ACCESS, and related Chinese Journals. He has publised 30 peer-reviewed papers in Circulation, Physics in Medicine and Biology, IEEE Transcation on Information Technology in Biomedicine etc. and holds 20 Chinese Patents. His research interests and main contributions include computational medicine and deep learning, bioelelctromagnetics and medical applications, automated detection and prediction cardiac/neruo electrical disorder.

Speech Title: "Automated Detection and Prediction of Serious Cardiac Electrical
Disorder by Using Machine Learning"

Abstract: Artificial intelligence has transformed key features of human life. Machine learning is a subset of artificial intelligence in which machines autonomously acquire information by extracting patterns from large databases. It has been progressively used in the medical science and clinical diagnosis especially within the domain of cardiac electrical disorders, such as precise detection of cardiac electrical arrhythmias and further earlier prediction of such serious diseases as sudden cardiac death. Compared with manual analysis and diagnosis in past, it shows great superiority under such current mass clinical bio-signal data, which is promoted by the modern fast communication technology and advanced wearable long-term monitoring systems. Modern machine learning models can automatically identify different electrocardiograms (ECG) with high precision; moreover automatically extract all interested features and clinically significant parameters. Several deep learning models have been developed for the high fidelity detection of common rhythm disturbances as in case of atrial fibrillation and complex arrhythmias. Here, we have highlighted numerous applications of machine learning for prediction and early detection of cardiac electrical disorders. Machine learning algorithms try to develop the model by using all the available input. In future it will be used for more healthcare areas to improve the quality of diagnosis.


Contact Us

The secretary office of ICCBB 2019 will collect contributions and finish daily organizing work. All paper review process will be completed by Program Committee and Technical Committee Members.

If you have any question, please feel free to contact our conference secretary.

Ms. Olia Lai

Email: iccbb@cbees.net

Tel.: +852-3500-0137 (Hong Kong)/+86-28-86528465 (China)

Working Time: Monday-Friday, 9:30-18:00 (UTC/GMT+08:00)  

In order to deepen the communication in all the participants, ICCBB 2019 is welcomed experts and scholars from all over the world to join in the conference committee.


Important Dates

Paper Submission (Full Paper) Before Jul. 25, 2019
Paper Submission (Abstract) Before Jul. 30, 2019
Notification of Acceptance On Aug. 15, 2019
Registration Deadline Before Sept. 05, 2019   
Conference Dates On Oct. 17-18, 2019
Academic Visit On October 19, 2019

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