Qi Yu
University of Southern California
qiyu@usc.edu
Bio
Qi (Rose) Yu is a fourth year Ph.D. candidate at the University of Southern California with a particular interest in Machine Learning and Data Mining. Rose’s research focuses on largescale spatiotemporal data analysis where she designs algorithms to perform predictive tasks in applications including climate informatics, mobile intelligence, and social media. Her work is supported by USC Annenberg Graduate Fellowship. She has interned in Microsoft R&D, Intel Lab, Yahoo Labs, and IBM Watson Research Center. She was selected and funded as one of 200 outstanding young computer scientists and mathematicians all over the world to participate the Heidelberg Laureate Forum.
Prior to enrolling at USC, Rose earned her Bachelors Degree in Computer Science from Cho Kochen Honors College at Zhejiang University. Before beginning her graduate studies, she was awarded Microsoft Research Asia Young Fellowship. Outside the lab, she is the technical cofounder of NowMoveMe, a neighborhood discovery startup.
Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning
Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning
Many data are spatiotemporal by nature, such as climate measurements, road traffic and user checkins. Complex spatial and temporal dependencies pose new challenges to largescale spatiotemporal data analysis. Existing models usually assume simple interdependence and are computationally expensive. In this work, we propose a unified lowrank tensor learning framework for multivariate spatiotemporal analysis, which can conveniently incorporate different properties in the data, such as spatial clustering, temporal periodicity and shared structure among variables. We demonstrate how the framework can be applied to two central tasks in spatiotemporal analysis: cokriging and forecasting. We develop an efficient greedy algorithm to solve the resulting optimization problem with convergence guarantees. Empirical evaluation shows that our method is not only significantly faster than existing methods but also more accurate.