Title:  “Tensor learning for Large-Scale Spatiotemporal Analysis”

Abstract: Spatiotemporal data is ubiquitous in our daily life, including climate, transportation, and social media. Today, data is being collected at an unprecedented scale.

Yesterday’s concepts and tools are insufficient to serve tomorrow’s data-driven decision makers. Particularly, spatiotemporal data often demonstrates complex dependency structures and is of high dimensionality. This requires new machine learning algorithms that can handle highly correlated samples, perform efficient dimension reduction, and generate structured predictions.

In this talk, I will present tensor methods, a general framework for capturing high-order structures in spatiotemporal data. I will demonstrate how to learn from spatiotemporal data efficiently in both offline and online setting. I will also show interesting discoveries by our methods in climate and social media applications.

Bio: Qi (Rose) Yu is a Ph.D. candidate and Annenberg fellow at the University of Southern California focusing on machine learning and data analytics. Her research strives to develop machine learning methods to learn from large-scale spatiotemporal data, specifically in the domain of computational sustainability and social science. She has over a dozen publications in leading machine learning/data mining conferences and was nominated as one of the ``2015 MIT Rising Stars in EECS’’.