钱晨,商汤科技研发总监,本科毕业于清华交叉信息研究院软件科学实验班(姚班),硕士毕业于香港中文大学。正带领团队负责 2D 与 3D 的人脸、人体、手势,视线等相关领域研究。曾任 Google 山景城总部软件工程师,期间组织了华人googler技术论坛。在CVPR,AAAI,ECCV,IJCA等顶级会议期刊上发表过多篇论文,包括两篇Oral与一篇Spotlight;带领团队在Megaface挑战中获得人脸辨别与认证两项第一。
Issei Fujishiro is Chief Professor of the Center for Information and Computer Science, Graduate School of Science and Technology, Keio University, Yokohama. He received his Doctor of Science in information sciences from the University of Tokyo in 1988. His research interests include graphical modeling paradigms, applied visualization design, and smart multi-modal ambient media. He has been serving on the steering committee for IEEE SciVis and IEEE PacificVis and the editorial board for IEEE TVCG (1999 to 2003, 2018 to date) , Elsevier Computers & Graphics (2003-2013), and Elsevier Journal of Visual Informatics (2016 to date). He was a guest editor for IEEE CG&A (Vol. 35, No. 6, 2015 and Vol. 28, No. 5, 2008). He served as a Chair/Co-Chair for PacificVAST 2018, CGI 2017, TopoInVis 2017, ACM VRCAI 2015, PacificVis 2014, Cyberworlds 2013, and IEEE SMI 2006 and served as a Program/Paper Co-Chair for Cyberworlds 2019, SciVis 2018, VRCAI 2014, PacificVis 2008, and Volume Graphics 2005/2003. He is a member of Science Council of Japan.
报告题目:Visual Exploration of Big Data in Astrophysics
报告简介:Astrophysics can be regarded as the ultimate remote sensing because no one can travel to see real heavenly bodies located far from the Earth. The only option for astronomers is to observe and study a variety of signals emanating from the distant bodies. This might lead to their enthusiasm for data visualization, compared to ones in other disciplines. Indeed, astrophysics relies heavily on the values of observed data visualization and analysis. In this talk, an overview of the latest research results from our collaborative research projects with astronomers will be given, including asymmetric biclustering of multivariate data for correlated subspace mining and its application to supernovae categorization, TimeTubes for visually extracting feature polarization variations from long-sequences of observed blazar datasets, and aflak as a novel visual programming environment to conduct fine-grained transformations, filtering and visual analyses on multispectral astrophysical observations.
讲者2:袁晓如 研究员 (北京大学)
袁晓如 北京大学信息科学与技术学院研究员,博士生导师,机器感知与智能教育部重点实验室副主任,大数据分析与应用国家工程实验室常务副主任。2008年初在北京大学建立可视化与可视分析实验室,研究方向包括复杂流场数据可视化,高维/时空数据,交通、社会媒体数据的分析,可视化的快速构建方法。高动态范围可视化的工作获得2005年IEEEVIS大会最佳应用论文奖。2013年来指导实验室团队10余次在IEEEVAST可视化分析挑战赛中获奖。2018年获日本大川助成奖。数十次担任IEEE VIS、EuroVis和IEEE PacificVis等国际可视化会议程序委员会委员,2017年 IEEE VIS大会论文主席(SciVis)。创建中国可视化与可视分析(ChinaVis)大会。担任《中国计算机学会通讯》专题主编,《计算机辅助设计与图形学学报》,Journal of Visualization (Springer)等国内外期刊编委。中国计算学会理事,杰出会员,杰出讲者。中国计算机学会大数据专家委员会委员。中国图象图形学学会理事、可视化与可视分析专业委员会主任。
获得国际学术奖励十余项:关于两跳移动中继网络基本性能的理论解析模型,获评2012年度丹羽保次郎记念论文奖;2012年获中国优秀自费留学生奖(日本选区ICT领域唯一获奖人);2012年获藤野先生纪念奖(共5人入选);2013年获日本东北大学校长奖;2016年获IEEE通信领域旗舰会议GLOBECOM最佳论文奖;2012年和2014年两次获IEEE无线通信与网络会议WCNC最佳论文奖;2018年获IEEE IC-NIDC最佳论文奖;分层无线网络组网理论与方法获2016年度陕西高等学校科学技术一等奖;2017年当选IEEE通信协会亚太杰出青年学者(IEEE Asia-Pacific Outstanding Young Researcher Award,西部唯一);2017年荣获IEEE TVT TOP Editor Award。
现任中国计算机学会物联网专委会委员,IEEE高级会员,IEEE通信协会自组织和传感网络技术委员会(IEEE AHSN TC)秘书长,IEEE ComSoc Distinguished Lecturer(该荣誉最年轻获得者;目前国内共有三位,另两位在清华和北大),Editors for IEEE Transactions on Wireless Communications (May 2018-present),Editor for IEEE Network(2015.7月-至今),Associate Editor for IEEE Transactions on Computers (2015.10月-2017.07月),Associate Editor for IEEE Transactions on Vehicular Technology(2016.1月-至今),Associate Editor for Wiley Security and Privacy (2017.7-至今),IEEE Transactions on Emerging Topics in Computing、IEEE Internet of Things Journal等国际主流期刊Guest Editor,担任国际学术会议GLOBECOM 2017,ICC2018,ICC2019, ICCC 2019等研讨会共同主席。担任日本学术振兴会(JSPS)特别研究员(2012.4-2014.3),日本国家信息与通信技术研究所(NICT)客座研究员(Rank A, 2016.4-2017.1)。担任IEEE COMSOC无线通信、自组织和传感器网络、卫星和空间通信、通信质量和可靠性等多个技术委员会委员,是WCNC、WCSP、ICCC、WASA、ICCVE、ICACCI、CHINACOM、CWSN等20多个国际会议程序委员。在IEEE GLOBECOM 2017、IEEE ICC 2016、IEEE ICCC 2015等国际会议及东京电机大学等知名高校做Keynote、Tutorial等报告10余次。
报告摘要:System event logs have been frequently used as a valuable resource in data-driven approaches to enhance system health and stability. A typical procedure in system log analytics is to first parse unstructured logs, and then apply data analysis on the resulting structured data. Previous work on parsing system event logs focused on offline, batch processing of raw log files. But increasingly, applications demand online monitoring and processing. We propose an online streaming system Spell, which utilizes a longest common subsequence based approach, to parse system event logs. We show how to dynamically extract log patterns from incoming logs and how to maintain a set of discovered message types in streaming fashion. We also utilize deep-learning based methods to automatically learn useful patterns and models from the underlying log messages. We then use these models to perform online monitoring and anomaly detection. Evaluation results on large real system logs demonstrate that even compared with the offline alternatives, our system shows its
superiority in terms of both efficiency and effectiveness.
Shui Yu is currently a full Professor of School of Software, University of Technology Sydney, Australia. Dr Yu’s research interest includes Security and Privacy, Networking, Big Data, and Mathematical Modelling. He has published two monographs and edited two books, more than 200 technical papers, including top journals and top conferences, such as IEEE TPDS, TC, TIFS, TMC, TKDE, TETC, ToN, and INFOCOM. Dr Yu initiated the research field of networking for big data in 2013. His h-index is 32. Dr Yu actively serves his research communities in various roles. He is currently serving the editorial boards of IEEE Communications Surveys and Tutorials, IEEE Communications Magazine, IEEE Internet of Things Journal, IEEE Communications Letters, IEEE Access, and IEEE Transactions on Computational Social Systems. He has served more than 70 international conferences as a member of organizing committee, such as publication chair for IEEE Globecom 2015, IEEE INFOCOM 2016 and 2017, TPC chair for IEEE BigDataService 2015, and general chair for ACSW 2017. He is a Senior Member of IEEE, a member of AAAS and ACM, the Vice Chair of Technical Committee on Big Data of IEEE Communication Society, and a Distinguished Lecturer of IEEE Communication Society.
报告题目:Big Data Privacy: State-of-the-Art and Challenges
报告摘要:Big data is revolution for our society. However, it also introduces a significant threat to data privacy. In this talk, we firstly review the current work in privacy protection under the framework of big data. Then we discuss the challenges in the domain, and our work in customized privacy protection in data publishing and location privacy. We humbly hope this talk will shed light for forthcoming researchers to further explore the uncharted part of this promising land.
李向阳教授现为中国科学技术大学计算机科学与技术学院教授、博导、执行院长。现任ACM中国共同主席、ACM理事会常务理事、ACM SIGMobile China联合主席、ACM Publication Board成员。2015年获IEEE Fellow和 ACM Distinguished Scientist称号,2016年获中国自然基金委杰出青年基金资助。1995获得 清华大学计算机科学和工商管理双学士,1999年获得美国伊利诺伊大学厄巴纳-香槟分校硕士,2001年获得美国伊利诺伊大学厄巴纳-香槟分校博士。曾任伊利诺伊理工大学计算机科学系教授,清华大学 EMC讲席教授,微软亚洲研究院访问教授。李向阳教授研究方向包括大数据的共享交易和隐私保护,大规模无线网络与安全可信的基础理论和系统构建方面的研究。作为项目负责人承担了国家自然科学基金、中科院先导计划、美国自然科学基金等20余项项目。自2000年以来已在高水平国际期刊及会议累计发表400余篇学术论文,其中包括知名IEEE Transactions系列期刊上面近100篇论文,及计算机网络领域著名的学术会议ACM MobiCom论文14篇(近10年来获该会议最佳论文奖1次,最佳论文奖提名2次,最佳演示奖1次,最佳Poster一次)。李向阳教授6次获得国际会议最佳论文奖,如ACM MobiCom 2014 的最佳论文奖。撰写了一本无线网络领域的专著《Wireless Ad Hoc and Sensor Networks: Theory and Applications》。作为第一发明人,获14项国内发明专利,4项美国临时专利。 担任了《IEEE/ACM Transactions on Networking》,《IEEE Transactions on Mobile Computing》,《IEEE Transactions on Parallel and Distributed Systems》等多个顶级国际期刊的编委和多个知名国际学术会议程序委员会主席或者大会主席。
王国胤,重庆邮电大学教授,研究生院院长、大数据智能研究院院长、计算智能重庆市重点实验室主任,大数据智能计算示范型国合基地负责人。是教育部长江学者特聘教授,中组部“万人计划”领军人才,人社部“新世纪百千万人才工程”国家级人选,国家重点研发计划项目首席科学家。担任国际粗糙集学会(IRSS)指导委员会主席(前任理事长)、中国人工智能学会(CAAI)副理事长、中国计算机学会(CCF)理事,是《Trans. on Rough Sets》、《计算机学报》等10余种期刊编委,是IRSS会士和CAAI会士。主要从事粗糙集、粒计算、知识发现、数据挖掘、认知计算、大数据等研究,出版专著15部(含编著),发表SCI/EI收录论文200余篇,论著被他引8000多次。获国家级高等教育教学成果二等奖、重庆市自然科学一等奖等成果奖励7项。带领的团队获评“国家级教学团队”和“重庆高校创新团队”。
朱扬勇,复旦大学计算机科学技术学院教授、学术委员会主任,上海市数据科学重点实验室主任。1989年开始从事数据领域研究,1996年开始从事数据挖掘研究,2004年开始从事数据科学研究,是国际数据科学研究的主要倡导者之一。2008年提出“数据资源是重要的现代战略资源,提高数据资源开发利用水平、保护国家的战略资源是增强我国综合国力和国际竞争力的必然选择”。2009年发表了数据科学论文“Data Explosion, Data Nature and Dataology”,并出版了第一本数据科学专著《数据学》。2010年创办“International Workshop on Dataology and Data Science”,2014年创办“International Conference on Data Science”。第462次香山科学会议“数据科学与大数据的理论问题探索”的执行主席。《大数据技术与应用丛书》主编。2014年提出“大数据试验场”,2015年提出“数据财政”。
尹小燕,博士,西北大学信息科学与技术学院副教授,硕士生导师,西北大学信息科学与技术学院计算机科学与技术系副主任,物联网工程专业负责人。YOCSEF西安分论坛学术秘书,CCF网络与数据通信专委会委员,ACM西安分部理事。目前的研究工作主要集中在无线网络、物联网(无线传感器网络)、数据传输与拥塞控制、群智感知、社会网络分析与数据处理等,在IWQoS 、 MobiHoc等国际知名学术会议与Transactions on Mobile Computing、IEEE Transactions on Vehicular Technology、《软件学报》等国际、国内知名学术期刊发表了系列研究成果。申报国家发明专利15项,其中授权发明专利4项。
报告简介:Evolutionary computation (EC) algorithms have aroused great attentions from both the academic and industrial communities in recent years due to their promising performance in many real-world optimization problems. However, in the era of cloud computing and big data, many optimization problems face the challenges of Large Scale & Uncertain, Multimodal & Many-objective, and Complexity & Expensive Fitness, which make traditional centralized EC algorithms based on a single computer/computing resource result in Low Solution Accuracy, Slow Convergence Speed, and Long Running Time.
In order to promote traditional centralized EC algorithms to solve the complicated optimization problems in Big Data Era, using distributed technology to enhance EC algorithms is a promising approach. In this talk, the distributed EC (DEC) algorithms that contribute to higher accuracy, faster convergence, and shorter runtime are introduced. Especially, two DEC examples, named Cloudde and MPMO are given. Cloudde is a distributed differential evolution (DE) algorithm implemented on distributed cloud resources that uses multiple populations to enhance global search ability, and uses parallel computing on cloud virtual machines to reduce running time. MPMO is shorter for “Multiple Populations for Multiple Objectives” which is a novel distributed framework that can efficiently solve multi-objective optimization problem (MOP) and many-objective optimization problems (MaOP). Moreover, some applications of Cloudde and MPMO are introduced. At last, more related works on DECs in Big Data environments are given.
姜育刚,复旦大学计算机学院教授、博士生导师,国家优秀青年科学基金获得者、国家“万人计划”青年拔尖人才、教育部青年长江学者,上海视频技术与系统工程研究中心主任。当前的研究领域为多媒体信息检索、计算机视觉与深度学习,主要关注视频图像大数据内容识别与检索方法。曾获国际计算机学会(ACM)评选的首届中国新星奖、ACM多媒体专业组(SIGMM)在全球范围评选的Rising Star Award、教育部自然科学二等奖(排名1)等。曾入选上海市青年科技启明星、上海市青年拔尖人才等计划,获上海IT青年十大新锐、上海市青年五四奖章等荣誉。
公茂果,二级教授,博士生导师,西安电子科技大学计算智能研究所所长,校学术委员会委员,校纪委委员,陕西省重点科技创新团队负责人,国家重点研发计划项目首席。主要研究方向为计算智能理论及其在数据与影像分析中的应用。主持国家重点研发计划、国家863计划、国家自然科学基金等十余项课题,发表SCI检索论文100余篇,被引用6000余次,入选中国高被引学者,授权国家发明专利20余项,获2013年国家自然科学奖二等奖和2016年教育部自然科学奖二等奖。
担任IEEE演化计算汇刊、IEEE神经网络与学习系统汇刊等期刊编委,IEEE计算智能学会Task Force on Collaborative Learning and Optimization主席,第十/十一届BIC-TA等学术会议主席,中国人工智能学会理事等。曾获"国家高层次人才特殊支持计划"中组部青年拔尖人才、国家优秀青年科学基金、霍英东青年教师奖、教育部新世纪优秀人才支持计划等。