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2018 the 2nd International Conference on Bioinformatics and Computational Intelligence | 2018年第二届生物信息学和计算智能国际会议(ICBCI 2018)

Hong Kong| July 28-30, 2018 香港| 2018年7月28-30日

Keynote &Plenary Speakers of 2018

 

 

IEEE Fellow, Prof. C. L. Philip Chen, Dean and Chair Professor of Faculty of Science and Technology, University of Macau, Macau

 

Speech Title: Universal Approximation Capability of Broad Learning System and its Structural Variations

 

Abstract: After a very fast and efficient discriminative Broad Learning System (BLS) that takes advantage of flatted structure and incremental learning has been developed, this talk will discuss mathematical proof of the universal approximation property of BLS. In addition, the framework of several BLS variants with their mathematical modellings are given. The variations include cascade, recurrent, and broad-deep combination that cover existing deep-wide/broad-wide structures. From the experimental results, the BLS and its variations outperforms several exist learning algorithms on regression performance over function approximation, time series prediction, and face recognition databases.

 

Biography: Biography: Dr. Chen’s research areas are in systems, cybernetics and computational intelligence. He is a Fellow of the IEEE, AAAS, and IAPR. He was the President of IEEE Systems, Man, and Cybernetics Society (SMCS) (2012-2013), where he also has been a distinguished lecturer for many years and received Outstanding Service Awards 4 times. Currently, he is the Editor-in-Chief of IEEE Transactions on Systems, Man, and Cybernetics: Systems (2014-). He has been an Associate Editor of several IEEE Transactions, and currently he is an Associate Editor of IEEE Trans on Fuzzy Systems, IEEE Trans on Cybernetics, and IEEE/CAA Automatica Sinica. He was the Chair of TC 9.1 Economic and Business Systems of IFAC (2015-2017). He is also a Fellow of CAA and Fellow of HKIE and an Academician of International Academy of Systems and Cybernetics Science (IASCYS). In March 2018, he is listed in world top 14 having the most highly cited paper in computer science area by WoS.

In addition, he is an ABET (Accreditation Board of Engineering and Technology Education, USA) Program Evaluator for Computer, Electrical, and Software Engineering programs. University of Macau’s Engineering and Computer Science programs receiving HKIE’s accreditation and Washington/Seoul Accord is his utmost contribution in engineering education for Macau as the former Dean. During his deanship, the engineering and computer science programs both have been ranked at world top 200 in the Times Higher Education (THE) world university ranking. The computer science program is also ranked at world top 161 in the US News and World Report global university ranking. Dr. Chen received Outstanding Electrical and Computer Engineering Award in 2016 from his alma mater, Purdue University, West Lafayette, where he received his Ph.D. degree in 1988, after he received his M.S. degree in electrical engineering from the University of Michigan, Ann Arbor, in 1985.

 

 

Prof. Manuel Núñez, Complutense University of Madrid, Spain

 

Speech Title: An Application of Fuzzy Automata to Analyze Heart Data

 

Abstract: In this talk I will briefly introduce a formalism to represent system where uncertainty plays an important role. I will define its syntax and semantics. Finally, I will show how this formalism can be successfully applied to define and analyze information extracted from electrocardiograms (ECGs) with the goal of identifying potential illnesses.

 

Biography: Manuel Núñez is a Professor in the Department of Computer Systems and Computation of the Complutense University of Madrid, Spain. He holds a Doctorate degree in Mathematics & Computer Science, obtained in 1996. Additionally, he holds a Master degree in Economics, obtained in 2002.

 

He has done research in the broad field of formal methods. Currently, he is interested in the study of formal methods for testing complex systems. Specifically, he has three main lines of research:

* Formal analysis of systems with distributed testers, in particular, those where time and probabilities play an important role.

* Passive testing of multi-user systems with asynchronous communications.

* Specification and testing of health related systems.

 

Manuel Núñez belongs to the following scientific committees:

* IEEE SMC Technical Committee on Computational Collective Intelligence,

* Board of Directors of the Tarot Summer School on Software Testing,

* ICCCI Steering Committee,

* A-MOST Workshop Steering Committee

He is a member of several Editorial Boards of journals and has served in more than 130 Program Committees of international events in Computer Science. He has published more than 130 papers in international scientific journals and meetings.

 

 

Prof. Ning Xiong, Mälardalen University, Sweden

 

 

Ning Xiong obtained the Ph.D with outstanding distinction from the University of Kaiserslautern (Germany) in 2000. His research addresses various aspects of computational intelligence techniques, incuding machine learning and big data analytics, evolutionary computing, fuzzy systems, uncertainty management, as well as multi-sensor data fusion, for building self-learning and adaptive systems in industrial and medical domains. He is serving as editorial board members for three international journals. He has been lead guest editor for a special issue in the journal "Neural Processing Letters" (Springer). He also has been programme committee members for a number of conferences and invited referee for many leading international journals.

 


Prof. William W. Song, Dalarna University, Sweden 


William Wei Song received his BSc in computer science from Zhejiang University, Hangzhou, China in 1982 and PhD in information systems and sciences from Stockholm University and the Royal institute of Technology, Stockholm, in Sweden in 1995.

He started his career at a university in 1982. After receiving his PhD degree, he became staff researcher at SISU, Sweden from 1995, senior researcher at ETI, Hong Kong University, China from 1999, and associate professor at Durham University, UK, from 2003. He is now a full professor in Business Intelligence and Information Systems at Dalarna University, Sweden. His research interest covers a wide range of fields, including computer science, information systems, artificial intelligence, semantic web, service science, business intelligence, e-business, e-learning, and online education. He has published more than 100 research papers in international journals and conferences.

Professor Song is also guest professor (researcher) of a number of overseas universities and sits at the board of a number of international journals.


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

 

Speech Title: Next Generation Tools for Big Data Analytics in Bioinformatics 

 

Abstract: With the increasing number and sophistication of biomedical instruments and data generation devices, there is even more increasing pressure on researchers to develop advanced data analytics tools to extract useful knowledge out of the missive collected data. This include advanced sequencing technologies responsible for the generation of huge amounts of bioinformatics data as well as wearable devices and Internet of Things systems responsible for collecting different types of health and mobility related data. The currently available data is not only massive in size but it also exhibits all the features of big data systems with a high degree of variability, veracity and velocity. Such big data systems in the biomedical domain represent great challenges as well as unlimited opportunities to advance biomedical research. Developing innovative data integration and mining techniques along with clever parallel computational methods to implement them will be critical in efficiently meeting those challenges and take advantage of the potential opportunities. In this talk, we demonstrate how graph modeling and network analysis can serve as the backbone of big data analytics tools. Such tools promise to play an important role in developing data-driven decision support systems in the next generation of biomedical research. We also present case studies illustrating how the proposed tools are used to analyze complex data associated with infectious diseases and lead to new biological discoveries.

 

Hesham H. Ali is a Professor of Computer Science and the Lee and Wilma Seaman 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 NSF or NIH funded projects in the areas of data analytics, wireless networks and Bioinformatics. He has been leading a Bioinformatics Research Group at UNO that focuses on developing innovative computational approaches to analyze complex bioinformatics data. The research group is currently developing several next generation data analytics tools for extracting medical-related knowledge from 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 integrating and analyzing large heterogeneous biological data associated with aging and infectious diseases. He has also been leading a project for developing secure and energy-aware wireless infrastructure to address tracking and monitoring problems in medical environments, particularly to study mobility profiling for various groups and develop a population analysis approach for advancing healthcare research.



 

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