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信息管理與電子商務(wù)研究前沿系列

TITLE:

Learning from Location Big Data – The Socio-Economic Value and Tradeoffs

ABSTRACT:

The fast penetration of smart mobile and sensor technologies, combined with the wide adoption of location services, has produced a vast volume of atomic, behavior-rich mobile consumer location data. Such “l(fā)ocation big data” have led to the pervasive digitization of individual behavior at a very fine-grained level. This information provides us with a new lens through which we can better monitor, understand, evaluate, and optimize the individual decisions and policy making to improve economic welfare. This talk presents some recent studies in which we apply novel machine learning, spatial-temporal data mining, econometrics and randomized experimental methods to discern the socio-economic value and tradeoffs in learning from location data.

ID: 85285378304

密碼：123456

參會鏈接: https://us02web.zoom.us/j/85285378304

SPEAKER:

Beibei Li is a tenured Associate Professor of IT & Management and Anna Loomis McCandless Chair at Carnegie Mellon University. She received her PhD with distinction from NYU Stern School of Business. Beibei has extensive experience at leveraging large-scale observational data analytics and experimental analysis with a strong focus on understanding individual behavior across online, offline and mobile channels for decision support. She is recent recipient of over $3M research grants, and is the winner of nine Best Paper Awards and over ten faculty research awards from Google, Amazon, Facebook, Adobe, LinkedIn, etc. Beibei has won the INFORMS ISS Sandy Slaughter Early Career Award in 2019. She is also the winner of the INFORMS ISS Nunamaker-Chen Dissertation Award and the ACM SIGMIS Best Doctoral Dissertation Award.

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