Collect from 网站模板

2017 International Conference on Bioinformatics and Computational Intelligence
2017年第一届生物信息学和计算智能国际会议
North China University of Technology| Sep.8-11, 2017
中国. 北方工业大学. 北京| 2017年9月8-11 日
 

Speakers of 2017

 

 

Speaker I
 


 

Prof.William W. Song, Dalarna University, Sweden 

 

Speech Title: Granularity and Semantics – Macro versus Micro Data Analysis

Abstract: How to determine an appropriate view of data analysis processes and data presentations is an extremely important issue when people want to understand trivial data or common-sense concepts as results of intensive data analyses. This issue involves a precise granularity identification as well as a flexible shifting between the different layers of views, fitting various settings and satisfying different users demands. The data analysis results of a transient change analysis of traffic flows of all crosses in a large city within a minute at a morning peak time might be useless to a car driver trapped in a traffic congestion when going to work. Likewise, it does not help very much to this driver either if a general city congestion index for a day is provided to her. However, this situation happens from time to time when researchers apply data analysis methods such as Predictive Analytics (PA) or Artificial Neuro-network (ANN) but do not realize an appropriate granularity of semantic views should be considered and figured out, resulting in difficulty for the users to understand and use the big data analysis outcomes. In this talk the author intends to point out what various data analysis methods are suitable for which views in which an appropriate granularity is defined. The author like to emphasize that, for the last few years, lack of deep analysis of semantic analysis and conceptual modelling in the research field of big data analysis for the areas such as e-commerce, social network systems, public traffic systems, and general healthcare, where huge amount of data accumulated in volume along the time is a major cause that results in this granularity problem.​


 

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.

 

Speaker II

 

 

Prof. Manuel Núñez, Complutense University of Madrid, Spain

Speech Title: An overwiev of the use of formal methods in IMDs

Abstract: The evolution of software is the main reason of many of the failures in some systems. The relevance of these failures depends on the purpose of the systems. In the case of Implantable Medical Devices (IMDs), it is essential to ensure their security and their ability to recognize some patterns of illnesses.

Formal methods can be used to appropriately check that the expected requirements are fulfilled. Formal methods allow us to formalize the requirements and specification of the system that we want to develop by taking into account aspects such as the security of the communications with external elements and their actuation mode in case of alarm or warning according to the patient state. For example, if we develop a defibrillator then we need to ensure that it reacts in case of a cardiac arrest or if the number of beats abruptly changes.

This talk will show the importance of formal methods in this area and the current state of its application in IMDs. Some of the techniques that will be explained are timed automaton, hybrid automaton and extended finite machines and the focus will be on the three main IMDs: pacemakers, defibrillators and infusion pumps.

 

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.

 

 

 

Speaker III

 

 

Prof. Lipo Wang, Nanyang Technological University, Singapore

 

Speech Title: Natural Computation for Optimization

 

Abstract: This talk highlights some of our research results in optimization using intelligent techniques inspired from nature. The techniques that we use include our noisy chaotic neural network, ant colony optimization, and genetic algorithms. In particular, neural networks are intrinsically parallel systems with potential for fast hardware implementation. We demonstrate our algorithms in several challenging optimization problems, such as optimal channel assignment in mobile communications, optimal multicast routing, topological optimization problem (TOP) in backbone network design, the broadcast scheduling problem (BSP) in packet radio networks, frequency assignment problem (FAP) in satellite communications, compact radial-basis-function (RBF) neural networks, class-dependent and class-independent feature selection, neural network tuning, and image segmentation.

 

Biography: Wang Lipo received the BS degree from National University of Defense Technology, China, and the PhD degree from Louisiana State University, USA. In 1989, he was a postdoctoral fellow at Stanford University, USA. In 1990, he was a faculty member in the Department of Electrical Engineering, University College, ADFA, University of New South Wales, Australia. From 1991 to 1993 he was on the staff of the Laboratory of Adaptive Systems, National Institutes of Health, USA. From 1994 to 1997 he was a tenured faculty member at Deakin University, Australia. Since 1998, he has been Associate Professor at the School of EEE, NTU. He has published 200 papers in journals and conferences. He holds a U.S. patent in neural networks. He has authored 2 monographs and edited 20 books. His work has been cited 400 times in the ISI Web of Science by other researchers. He was keynote/plenary speaker for several international conferences. He is Associate Editor for IEEE Transactions on Neural Networks (since 2002), IEEE Transactions on Evolutionary Computation (since 2003), and IEEE Transactions on Knowledge and Data Engineering (since 2005). He is Area Editor of the Soft Computing journal (since 2002). He serves on the Editorial Board of 8 additional international journals. He was Vice President for Technical Activities (2006-2007) and Chair of the Emergent Technologies Technical Committee (2004-2005), IEEE Computational Intelligence Society. He has been on the Governing Board of the Asia-Pacific Neural Network Assembly (APNNA) since 1999 and served as APNNA President in 2002/2003. He won the 2007 APNNA Excellent Service Award. He was Founding Chair of both the IEEE Engineering in Medicine and Biology Chapter Singapore and IEEE Computational Intelligence Chapter Singapore. He serves/served as General/Program/Steering Committee Chair for 15 international conferences and as Member of steering/advisory/organizing/program committees of over 150 international conferences.
 

Research Interests
 

Nature-inspired computation, including neural networks, evolutionary computation, fuzzy systems, and chaos, with applications to data mining, bioinformatics, and combinatorial optimization.

 

 

Speaker IV

 

 

Prof. Li Zhang, University of Cincinnatii, USA

Speech Title: Model of DNA breakpoints in cancer depended on genomic DNA sequence features and replication timing

 

Abstract: DNA breakpoints mark the endpoints of copy number variants (CNVs) in cancer genomes, which are important in carcinogenesis and development. We have analyzed the distribution of the DNA breakpoints that underlie the genomic changes in over 10,000 patients of 33 cancer types collected in The Cancer Genome Atlas (TCGA) project. Cancer genomes typically contain numerous somatic CNVs. We developed a linear regression model that predicts the distribution of DNA breakpoints observed in cancer in terms of germline DNA breakage frequency, replication time, and a number of genomic sequence features such as the density of alu units.  We found that copy number gains in tumor tend to happen in the genomic regions that are replicated early and copy number losses tend to happen in late replication regions. This pattern suggests that the background rate of CNVs may be generated from random disruption of replications, which would lead to a surplus of copy number in the early-replication regions and a deficit in the late-replication regions. We demonstrate that our model provides a framework to distinguish the cancer drivers from passengers involving CNVs.

 

Biography: Dr. Li Zhang received his MS in Biophysics, Tsinghua University, Beijing, China in 1988, his PhD in Computational Biology, University of North Carolina at Chapel Hill, NC, USA in 1995 and finished his Postdoctoral training in the Scripps Research Institute, San Diego, CA, USA, in 1998.

 

Dr. Li Zhang joined the Department of Department of Environmental Health, University of Cincinnatii as associate professor in 2016. Previous affiliations include senior scientist, Ernest Gallo Clinics and Research Center, University of California at San Francisco, CA, (1998-2001), assistant professor, Department of Biostatistics, Division of Quantitative Sciences, The University of Texas M. D. Anderson Cancer Center, Houston, TX, (2001-2006), assistant professor, Department of Bioinformatics and Computational Biology, Division of Quantitative Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, (2006-2010), associate professor, Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, (2010-2016) and associate professor (adjunct), Department of Statistics, Rice University, Houston, Texas,  (2010-2016).

 

Dr. Li Zhang is one of the members of American Association for the Advancement of Science (AAAS) and International Society of Computational Biology (ISCB). 

 

Dr. Li Zhang is also an associate editor of International Epidemiology and Genetics, Cancer Medicine and Journal of Computational Systems Biology and he is a reviewer for many journals, such as Nature Biotechnology, PNAS, Nucleic Acid Research, Cancer Research, Bioinformatics, BMC Bioinformatics, BMC Genomics and Cancer Medicine, etc. Moreover, He has published more than 80 research papers and holds 2 U.S. patents in University of Texas M.D. Anderson Cancer Center.

 

Speaker V
 

 

 

Prof. Rafał Scherer, Institute of Computional Intelligence, Poland

Speech Title: Convolutional Neural Networks – From Images to Other Types of Data

 

Abstract: Inspired by biological processes, convolutional neural networks (CNN) proved to be successful in classifying large, diverse, multi-class image datasets.  This come from multiple improvements over conventional multilayer neural networks. Convolution operation sliding over the image with shared weights reduces the number of trainable parameters, improves generalizations and makes CNN immune partially to various input image transformations. Neurons are organized in three-dimensional structures to cope with the third dimension of the input data. Pooling layers allows to gradually reduce the spatial size of the features. By rearranging input data, CNN can deal effectively with data types other than images, e.g. letters, texts or numerical and textual streams such as network traffic.

 

Biography: Rafał Scherer is an associate professor at the Częstochowa University of Technology and head of Computer Vision and Data Mining Lab. His research focuses on developing new methods in computational intelligence and data mining, ensembling methods in machine learning, content-based image indexing and classification. He authored more than 80 research papers and a book on multiple classification techniques published in Springer. He was a reviewer for major computational intelligence journals. Scherer earned MSc degree in electrical engineering at Department of Electrical Engineering and PhD degree in computer science (Methods of Classification Using Neuro-Fuzzy Systems) at the Department of Mechanical Engineering and Computer Science of Czestochowa University of Technology. He co-organizes every year or two years the International Conference on Artificial Intelligence and Soft Computing in Zakopane (http://www.icaisc.eu/) which is one of the major events on computational intelligence. He is also a co-editor of the Journal of Artificial Intelligence and Soft Computing Research (http://jaiscr.eu/).

 

 

 

 

>