Learning From Large-Scale Spatiotemporal Data

Abstract:
In many real-world applications, such as internet of things (IoT), transportation and physics, machine learning is applied to large-scale spatiotemporal data. Such data is often nonlinear,  high-dimensional, and demonstrates complex spatial and temporal correlations. In this talk, I     will demonstrate how to efficiently learn from such data.      In particular, I will present some recent results on
1) Low-Rank Tensor Regression for spatiotemporal causal inference and 2) Diffusion Convolutional RNNs for spatiotemporal forecasting, applied to real-world traffic and climate data. I will also discuss opportunities and challenges of learning from large-scale spatiotemporal data.

Bio:
Qi (Rose) Yu is an Assistant Professor at Northeastern University College of Computer and Information Science. Previously, she was a postdoctoral researcher in the Department of Computing and Mathematical Sciences at Caltech. She earned her PhD in Computer Science at the University of Southern California and was a visiting researcher at Stanford University.
Her research focuses on machine learning for large-scale spatiotemporal data, and is motivated by a range of applications including intelligent transportation and climate informatics. She has over a dozen publications in leading machine learning and data mining conference and several patents. She is the recipient of the USC Best Dissertation Award, “MIT Rising Stars in EECS”, and the Annenberg fellowship.