中国计算机学会青年计算机科技论坛
CCF Young Computer Scientists & Engineers Forum
CCF YOCSEF深圳
时间：2016年6月24日（星期五）15:00-17:30
地点：深圳市南山区白石路深圳大学南校区计算机学院大楼A938室
报告会主题
程序自动分析与软件定义的大数据系统
主办单位：
中国计算机学会青年计算机科技论坛 深圳分论坛(CCF YOCSEF深圳)
深圳大学广东省普及型高性能计算机重点实验室
深圳市电脑学会
执行主席：
王 毅 CCF YOCSEF深圳2016-2017 AC委员、学术秘书
王健宗 CCF YOCSEF深圳2016-2017 AC委员
议程
14:45 签到
15:00 报告会开始
CCF YOCSEF深圳 组织方 致辞
15:10 特邀讲者：张翔宇，美国普渡大学教授
演讲题目：Dynamic Program Analyses and Their Applications
16:10 特邀讲者：李晓林，美国佛罗里达大学副教授
演讲题目：Towards Intelligent Platforms via Software-Defined Big Data Ecosystems
程序自动分析与软件定义的大数据系统
报告1：大数据并行与交互式计算
特邀讲者：张翔宇
张翔宇是普渡大学的副教授，将于今年8月成为正教授。1998年获得中国科学技术大学计算机科学与技术专业学士，2006年获得亚利桑那大学的博士学位。目前指导13个博士生，研究动态和静态程序分析与其在调试、测试、取证分析和数据处理等方面的应用。曾获得2006ACM SIGPLAN 杰出博士论文奖，美国国家科学基金职业生涯奖，ACM SIGSOFT 杰出论文奖，2014UNIX最佳学生论文奖、2015CCS最佳论文奖和2016NDSS杰出论文奖。
报告提要：Dynamic program analyses analyze runtime information collected during program execution. They can be classified to two categories: temporal analysis that inspects execution history and spatial analysis that studies states of program execution (e.g., memory states and disk states). They have a wide range of applications in various areas such as software debugging, testing and security.
In this talk, I will introduce a number of dynamic analysis projects in my group. Particularly, I will present two kinds of temporal analyses: (1) audit logging; and (2) forced execution. Audit logging analyzes software system behavior by inspecting their system level event traces such as file reads/writes and sockets sends/receives. It is critical for understanding advanced security attacks to enterprise systems. Forced execution forces a program to execute even when the required environmental and input conditions are not satisfied. It is highly-effective in disclosing hidden malicious logic in executable programs.
I will also introduce memory forensic analysis, which is a kind of spatial analysis. It inspects the memory snapshot of a process to recover critical information such as the files that are being edited in a document processing software, the ongoing conversation in a social-networking software, and the pictures that were taken by a camera app in the past but not saved to disk. Such information is extremely useful in attack investigation.
报告2：Towards Intelligent Platforms via Software-Defined Big Data Ecosystems
特邀讲者：李晓林
Dr. Xiaolin Andy Li is an associate professor and founding director of Scalable Software Systems Laboratory (S3Lab) in the Department of Electrical and Computer Engineering at the University of Florida, Director of NSF I/UCRC Center for Big Learning (CBL). The mission of S3Lab is to create future cloud, smart life, and big data ecosystems with intelligence today. His research interests include Cloud Computing, Big Data, Deep Learning, HPC, SDN, Cyber-Physical Systems/IoT, and Security & Privacy. He has published over 90 peer-reviewed papers in journals and conference proceedings, 5 books, and 4 patents. His research has been sponsored by National Science Foundation (NSF), National Institutes of Health (NIH), Department of Homeland Security (DHS), Department of Energy (DOE), and others. He was a faculty member (with early promotion and tenure) in the Computer Science Department at Oklahoma State University (OSU), a visiting professor at Nokia Research Center Beijing (NRC), a visiting scholar at University of Texas at Austin (UT), an Extreme Blue intern at IBM, a graduate research assistant at Rutgers University (RU), a research staff at Institute for Infocomm Research (I2R), and a research scholar at National University of Singapore (NUS). He received a PhD degree in Computer Engineering from Rutgers University and Communications & Information Engineering from National University of Singapore, a MS degree from Zhejiang University, and BS degree from Qingdao University. He is a recipient of the National Science Foundation CAREER Award in 2010, the Internet2 Innovative Application Award in 2013, NSF I-Corps Top Team Award in 2015, the CAGI Challenge on Detecting Bipolar Disorder Top Team Award (DeepBipolar) in 2016, and best paper awards (IEEE SECON 2016, ACM CAC 2013 and IEEE UbiSafe 2007).
报告提要：The tremendous big data generated from natural systems, engineered systems, and human activities require new capabilities in algorithms and systems to explore insights and make decisions through high performance big data platforms. To address these challenges, we propose a two-pronged solution: data-driven self-programmed models and self-programmed systems, collectively called software-defined ecosystems. In particular, self-programmed systems feature software-defined networking and computing; self-programmed models feature deep hierarchical learning representation and data-driven self-programming. Our preliminary prototypes CognitiveEngine and DeepCloud are one of the first attempts to realize such a coherently symbiotic intelligent platforms. Case studies on enabling intelligence in scientific discovery and precision medicine will be highlighted.
参加人员：IT领域专业人士、研究生、媒体、其他有兴趣者
报名联系：王毅 Email：yiwang@szu.edu.cn
参会回执
|
姓名
|
|
单位
|
|
|
电话
|
|
Email
|
|
|
是否CCF会员
|
|
会员号
|
|
交通：详见下图
京公网安备 11010802032778号