谢幸博士于2001年7月加入微软亚洲研究院，现任主管研究员，并任中国科技大学兼职博士生导师。他分别于1996年和2001年在中国科技大学获得计算机软件专业学士和博士学位。目前，他的团队在空间数据挖掘、位置服务、社交网络和普适计算等领域展开创新性的研究。他在国际会议和学术期刊上发表了100余篇学术论文，并拥有50余项专利。他是ACM和IEEE高级会员，中国计算机学会普适计算专委会常委，多次担任WWW、UbiComp、ACM SIGSPATIAL、KDD等顶级国际会议程序委员会委员或主席。他参与创立了ACM SIGSPATIAL中国分会，并曾担任UbiComp 2011大会程序委员会共同主席。       

      在社交网络中，用户主动和他们的朋友们分享心情、爱好、活动和照片等各种信息。这其中的很多信息都显式或隐式的包含了用户的位置。最近兴起的基于位置的社交网络则允许移动用户在社交网络中共享各自的位置以及与位置相关的信息。在这次报告中，我们将详细介绍近期我们在基于移动社交网络数据以理解用户位置方面展开的研究和探索。我们将探讨移动社交网络对用户位置理解和预测等问题的影响。我们基于用户的地理坐标、当前时间以及签到历史，为用户的当前位置提供具体而有意义的名称。在我们的方法中，我们借鉴了位置搜索和位置命名的相似性，提出了一个参照位置搜索的算法框架，结合热门度特征和用户历史特征，来解决位置命名问题。针对用户位置预测问题，我们考虑了数据的稀疏性、签到的长期顺序性和短期非顺序性，以及签到的时空模式等多种因素。

 

        陈一昕博士，中国科技大学少年班本科毕业，美国伊利诺大学香槟分校获计算机科学博士学位. 现为美国华盛顿大学副教授，终身教授。研究领域为数据挖掘, 机器学习，优化算法, 人工智能，云计算等。在AIJ,JAIR,TKDE,TKDD,TIST,TPDS等国际一流期刊和VLDB, AAAI, KDD, IJCAI, ICML, RTSS等国际顶级会议和上发表论文90余篇。 任数据挖掘和人工智能领域的顶级期刊JAIR, TKDE, TIST的编委，和KDD, AAAI, IJCAI, ICDM, SDM等一流国际会议的程序委员会委员。为美国国家科学基金委，香港研究基金委，奥地利国家科学基金委，瑞士国家科学基金委的评审委员。中国科技大学所承担的教育部111引智计划专家组八位专家成员之一，中国计算机学会大数据专家委员会首届委员之一。陈教授的研究连续获得美国国家科学基金委，美国能源部，美国国家卫生局，美国能源研究科学计算中心，美国微软公司总部，美国Sloan-Kettering癌症中心，美国Barnes-Jewish Hospital基金, 中国科技部973计划资助。曾获AAAI (2010), ICTAI (2005), ICMLC(2004)等国际会议的最佳论文奖，和RTSS（2011），KDD(2009), ITA(2004)等国际会议的最佳论文奖提名。其开创性的研究工作获得了美国微软青年教授奖 （2007）， 美国能源科学计算中心启动项目分配奖 （2007）和 美国能源部杰出青年教授奖 （2006）。

        Early detection of clinical deterioration is essential to improving clinical outcome. In this talk, we present a novel two-tier system for clinical early warning by mining large-scale hospital databases. The system focuses on the large population of patients in the general hospital wards, who are not in the ICU and suffer from infrequent monitoring. The first tier uses data mining algorithms on existing hospital database records to identify patients most at risk of clinical deterioration. Those patients will wear custom made low-power, compact sensing devices which will collect real-time vital signs through a wireless sensor network. The second tier combines both the real-time sensor data and the clinical database records from these candidate patients to predict serious clinical deterioration and suggest the most relevant factors, based on advanced data mining and machine learning algorithms. It also adaptively reconfigures the sensing devices to improve device battery life. Promising results on real-life clinical trials at the Barnes-Jewish Hospital (the eighth largest hospital in the United States) will be discussed.

 

        Yin "David" Yang is a Research Scientist at the Advanced Digital Sciences Center, Singapore, and a Principal Research Affiliate at the Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, IL, USA. His research interests lie in database security and query optimization. He has published several papers in renowned venues on differentially private data publication and analysis, and on query authentication in outsourced databases. In addition, he has designed efficient query processing methods in various contexts, including data streams, relational keyword search, spatial databases, web portals, and wireless sensor networks.       

        Data privacy has been an important research topic in the security, theory and database communities in the last few decades. However, many existing studies have restrictive assumptions regarding the adversary’s prior knowledge, meaning that they preserve individuals’ privacy only when the adversary has rather limited background information about the sensitive data, or only uses certain kinds of attacks. Recently, differential privacy has emerged as a new paradigm for privacy protection with very conservative assumptions about the adversary’s prior knowledge. Since its proposal, differential privacy had been gaining attention in many fields of computer science, and is considered among the most promising paradigms for privacy-preserving data publication and analysis. In this talk, I will motivate its introduction as a replacement for other paradigms, present the basics of the differential privacy model from a database perspective, and describe several state-of-the-art methods in differential privacy research.