Prof. Bijoy K. Ghosh
Texas Tech University, USA
Bijoy received the B. Tech and M. Tech degrees in Electrical and Electronics Engg. from BITS Pilani and the Indian Institute of Technology, Kanpur, India, and the Ph.D. degree in Engineering Sciences from the Decision and Control Group of the Division of Applied Sciences, Harvard University, Cambridge, MA, in 1977, 1979 and 1983, respectively. From 1983 to 2007 Bijoy was with the Department of Electrical and Systems Engineering, Washington University, St. Louis, MO, USA, where he was a Professor and Director of the Center for BioCybernetics and Intelligent Systems. Currently he is the Dick and Martha Brooks Regents Professor of Mathematics and Statistics at Texas Tech University, Lubbock, TX, USA. He received the Donald P. Eckmann award in 1988 from the American Automatic Control Council, the Japan Society for the Promotion of Sciences Invitation Fellowship in 1997. He became a Fellow of the IEEE in 2000, and a Fellow of the International Federation on Automatic Control in 2014. Currently he is the IEEE Control Systems Society Representative to the IEEE-USA's Medical Technology Policy Committee. Bijoy had held visiting positions at Tokyo Institute of Technology, Osaka University and Tokyo Denki University, Japan, University of Padova in Italy, Royal Institute of Technology and Institut Mittag-Leffler, Stockholm, Sweden, Yale University, USA, Technical University of Munich, Germany, Chinese Academy of Sciences, China and Indian Institute of Technology, Kharagpur, India. Bijoy's current research interest is in BioMechanics and Control Problems in Rehabilitation.
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".
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.
Prof. Ashoka Polpitiya
Sri Lanka Technological Campus, Sri Lanka
Ashoka Polpitiya, DSc, is a Professor in Electrical Engineering at Sri Lanka Technological Campus since 2016. Prior to this, he was the Director of Bioinformatics and Biostatistics at Sera Prognostics Inc., in Salt Lake City, Utah where he still works as a consultant. He has also worked in the past as the Lead Bioinformatician for Proteomics at the Translational Genomics Research Institute in Phoenix, Arizona and as a Senior Scientist at the Pacific Northwest National Laboratory (PNNL). He has published articles and developed software tools to address various analytics issues in Genomics and Proteomics experiments. Dr. Polpitiya received his BS in Electrical Engineering from University of Peradeniya, Sri Lanka, an MS and a PhD both from the Washington University in St. Louis in 2000 and in 2004, respectively, in Systems Science and Mathematics. He spends his time in both Sri Lanka and US, working for SLTC and Sera Prognostics.
Prof. Ralf Hofestädt
Bielefeld University, Germany
Prof. Ralf Hofestädt studied Computer Science and Bioinformatics at the University of Bonn. He finished his PhD 1990 (University Bonn) and his Habilitation (Applied Computer Science and Bioinformatics) 1995 at the University of Koblenz. From 1996 to 2001, he was Professor for Applied Computer Science at the University of Magdeburg. Since 2001, he is Professor for Bioinformatics and Medical Informatics at the University Bielefeld. The research topics of the department concentrate on biomedical data management, modeling and simulation of metabolic processes, parallel computing and multimedia implementation of virtual scenarios.
Prof. Weixiong Zhang
Washington University in Saint Louis, USA
Dr. Weixiong Zhang is a professor of Computer Science and of Genetics at Washington University in Saint Louis, USA. He received his BS in Computer Engineering from Tsinghua University and his PhD in Computer Science from UCLA. His main research interests include computational and systems biology and artificial intelligence (machine learning, data mining, heuristic search and optimization). He has published one research monograph and more than 160 research papers in peer-reviewed journals and conferences in these areas. In the past 15 years, he has been focusing on developing methods and tools for analyzing large scale biological data for transcriptome modeling, analyzing noncoding RNA gene regulation, understanding genome-wide genotype-phenotype associations, and their applications to complex diseases, such as Alzheimer’s disease and psoriasis as well as plant stress tolerance in crops. His research has been supported by NIH, NSF, USDA, DARPA and the Alzheimer’s Association. He is currently serving and has previously served as Deputy Editor of PLoS Computational Biology, editorial board members of Biology Direct, Nature Scientific Reports, and J of Alzheimer’s Disease, and Associate Editor of Artificial Intelligence. More information of his research can be found at http://www.cse.wustl.edu/~zhang.
Speech Title: "Allele-Specific, Network-Based Genome-Wide Association Studies and Applications"
Abstract: Hundreds of genetic markers have been shown to be associated with various complex diseases, yet the “missing heritability” remains alarmingly elusive. Combinatorial interactions may account for a substantial portion of this missing heritability, but their discoveries have been impeded by computational complexity and genetic heterogeneity. We developed a novel systems-level approach that efficiently constructs genome-wide, allele-specific networks that accurately segregate homogenous combinations of genetic factors, tests the associations of these combinations with the given phenotype, and rigorously validates the results using a series of unbiased validation methods. We applied this networked-based GWAS method to analyze psoriasis, discovering a combinatorial pattern with an odds ratio of 3.64 and Bonferroni-corrected p-value of 5.01×10-16. This pattern was replicated in independent data, reflecting robustness of the method. We also applied our method to HapMap data from a dozen of world populations. We identified a remarkable yin-yang haplotype pattern (a stretch of DNA evolves to present two disparate forms that bear differing states for nucleotide variations along their lengths) encompassing gephyrin, a highly conserved gene that is vital for the organization of proteins at inhibitory receptors, molybdenum cofactor biosynthesis and other diverse functions The gephyrin yin-yang pair consists of 284 divergent nucleotide states and both variants vary drastically from their mutual ancestral haplotype, suggesting rapid evolution. This discovery holds potential to deepen our understanding of variable human-specific regulation of gephyrin while providing clues for rapid evolutionary events and allelic migrations buried within human history. These results demonstrate the potential of our new method for discovering combinatorial genetic associations within heterogeneous genome-wide data, thereby transcending the limiting “small effects” produced by individual markers examined in isolation.
Prof. Zhiwei Qiao
ShanXi University, China
Zhiwei Qiao received his PhD degree in transportation information engineering and control from Beijing Jiaotong University in 2011. He was a Postdoctoral Scholar and Visiting Professor with Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, IL, USA, from August 2012 to August 2014 and from January 2017 to August 2017, respectively. He is currently a professor with School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi, China. His research interests include electron paramagnetic resonance imaging (EPRI), computed tomography (CT) and magnetic resonance imaging (MRI) etc. He mainly focuses on image reconstruction algorithm, signal processing and high performance computing. He has published a series of papers on CT and EPRI image reconstruction, especially three papers on Journal of Magnetic Resonance. Now, he is constructing the China-USA united lab for medical imaging, supported by Shanxi University and The University of Chicago.
Assoc. Prof. Peng Du
Hangzhou Dianzi University, China
Dr. Peng Du is currently an associate professor at Hangzhou Dianzi University in China, where he leads an Nvidia GPU Education Center. He is an algorithm researcher at Hithink RoyalFlush Information Network Company, as well as an Nvidia Deep Learning Institute Instructor. He received his Ph.D. degree from Department of Computer Science & Technology, Zhejiang University under the advisory of Dr. Min Tang and Ruofeng Tong in 2013 and pursued his postdoctoral research at KAIST and NTU from 2014 to 2017. He also received his B.S. and M.S. degree from College of Computer, Shenyang University of Technology. His research interests include large-scale model rendering, collision detection, machine learning, medical image processing and high performance computation. He has published more than ten papers in leading journals and conferences including SIGGRAPH, PG, CASA, CGI, CVM, TOG, CGF, CAVW and JCST. And he has been granted nine patents.
|Prof. Yinglei Lai
The George Washington University, USA
|Prof. Edwin Wang
University of Calgary, Canada
|Assoc. Prof. Qijun Zhao
Sichuan University, China
|Prof. David Zhang
Hong Kong Polytechnic University, Hong Kong
|Prof. Nozomu Hamada
Universiti Teknologi Malaysia (UTM), Malaysia
|Prof. Wirachman Wisnoe
Universiti Teknologi MARA (UiTM), Malaysia