2018年5月18日学术报告二则(吴方向 University of Saskatchewan,陈振邦 国防科技大学)
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报告题目1: Computational Network Biology
报告时间:    5月18日10:00
报告地点:    计算机学院B403
报告人:       吴方向
报告人国籍: 加拿大
报告人单位: University of Saskatchewan, Canada.
报告人简介Dr. FangXiang Wu is a full professor of College of Engineering and of Department of Computer Science at the University of Saskatchewan, Canada. Since 2016, Dr. Wu has become a national "1000 plan" guest professor at Central South University, Changsha, China. Dr. Wu received the B. Sc. degree and the M. Sc. degree in Applied Mathematics, both from Dalian University of Technology, Dalian, China, in 1990 and 1993, respectively, the first Ph.D. in Control Theory and Its Applications from Northwestern Polytechnical University, Xi’an, China, in 1998, and the second Ph.D. in Bioinformatics and Systems Biology from University of Saskatchewan, Saskatoon, Canada, in 2004. He worked as a postdoctoral fellow with Laval University Medical Research Center, Quebec City, Quebec, during 2004-2005. His current research interests are in Bioinformatics and computational network biology, including Complex network controllability and applications, Biomolecular network analytics, Machine learning in bioinformatics, Biological data analytics, Brain images and brain networks, and Nonlinear biodynamic analytics. He has published about 200 refereed journal papers and over 100 refereed conference papers. He has been invited to deliver about 60 academic talks in the international conferences and academic institutes all over the world. Wu has served as the editorial board member/associate editor of five international journals (Scientific Reports, Neurocomputing, etc.), as the guest editor of over 20 international journals, and as the program committee chair or member of more than 30 international conferences.
报告摘要:Network biology, an emerging area focusing on various biomolecular networks, is a multidisciplinary intersection of mathematics, computer sciences, and molecular biology. With burgeoning high-throughput data, various types of biomolecular networks can be constructed. Typical biomolecular networks include protein-protein interaction networks, gene regulatory networks, metabolic networks, signal-transduction networks, drug-target networks, gene-disease networks, co-expression networks, association networks, and so on. These networks can be depicted as (un)directed graphs, where nodes (vertices) represent molecules while edges (links) represent interactions/associations among molecules. Biomolecular networks provide us great opportunities from studying individual components to understanding functional networks for biomolecular systems, cells, organs and even entire organisms. In past years I and my colleagues (my students and collaborators) have developed an array of computational methods to mine the knowledge from biomolecular networks. In this seminar, I will talk about some of our recently developed methods for identifying essential proteins, disease genes, protein complexes, drug targets from various biomolecular networks. I will also briefly introduce my researches in other areas of bioinformatics and computational biology.
邀请人:章文副教授
 


报告题目2: Symbolic Verification of Regular Properties -- Guiding and Pruning
报告时间:  2018年5月18日(周五) 上午10:00
报告地点:  计算机学院B404会议室
报告人:    陈振邦,博士,副教授
报告人单位:国防科技大学计算机学院
                                                                                                                                                                                                                                                                                                                                                                                                                 
报告人简介:
陈振邦,武汉黄陂人,博士,国防科技大学计算机学院副教授,中国计算机协会形式化方法专委会委员。分别于2002和2009年在国防科技大学获计算机科学与技术学士和博士学位,从2009年起于国防科技大学任教。目前主要的研究方向为程序分析、形式化方法及其在不同背景下的应用。相关研究成果发表在ICSE、FSE、FM、TCS、SCP等软件工程或形式化方法的会议或期刊上。2012年获国家科技进步二等奖1项,2018年获ICSE的杰出论文奖。担任过多个本方向国际国内学术会议的程序委员会委员,负责承担了多项自然科学基金项目,作为骨干参与多项包括973、863在内的国家重点项目。
 
报告摘要:
正规性质是有限状态机(或正则表达式)可表达的性质。正规性质广泛应用于软件的规约、测试与验证等领域。程序正规性质的验证非常具有挑战。我们结合程序静态分析和动态分析技术,提出了基于动态符号执行的程序正规性质的符号化验证方法,包括面向正规性质的引导和路径削减方法,可快速找到满足正规性质的程序路径,完成程序路径空间的遍历。我们针对Java程序实现了原型工具,通过在实际开源Java程序上的实验表明,我们的方法在找到目标路径上有平均两个数量级的性能提升,在完成路径空间遍历上有平均一个数量级的性能提升。
该报告主要内容源自报告人的ICSE 2015、FSE 2017和ICSE 2018论文。
 
邀请人:贾向阳

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