报告人：Raghu Ganti IBM Watson实验室研究员
报告题目：Human Mobility Modeling on CrowdSensed Data: Insights and Challenges
报告摘要：The ubiquitous availability of location sensing devices in the form of smartphones, cars, taxis, and other devices combined with the ability to collect data at scale using crowdsensing platforms enables the fine grained monitoring and modeling of human movement, both at the individual level and a group level. In this talk, we will first examine human movement data from call-detail records and taxi cabs and provide insights into "routes" and "timing" aspects of the movement patterns in large cities. We will then pose the question of efficiently analyzing large volumes of mobility data at scale (both real-time and non-real-time aspects) and have an open discussion regarding these challenges.
报告人简介：Raghu Ganti is a Research Staff Member at IBM T. J. Watson Research Center. He is part of the Cognitive IoT department. His research interests span wireless sensor networks, ubiquitous computing, and data mining. For the past several years, he has been working on spatiotemporal analytics - the analysis of moving objects and been developing various algorithms for spatiotemporally enabling IBM's big data products. Parts of his work are now embedded in products such as IBM InfoSphere Streams, SPSS statistical modeler, and InfoSphere SenseMaking. He obtained his Ph.D. degrees from the Department of Computer Science, University of Illinois at Urbana-Champaign in August 2010. He has published around 60 papers in top journals and conferencces, such as IEEE TMC, ACM TOSN, MobiCom, UbiComp, Infocom, etc. He got the best paper award at Mobiquitous2014. He has served as ICDCS2015 TPC Vice chair and IQ2S2015 TPC chair.