Wed, Jun 07, 2017 @ 01:30 PM - 03:30 PM
PhD Candidate: Rose Yu
Date: June 7, 2017
Location: SAL 213
Mahdi Soltanolkotabi (outside member)
Tensor learning for Large-Scale Spatiotemporal Analysis
Spatiotemporal data is ubiquitous in our daily life, including climate, transportation,
and social media. Today, data is being collected at an unprecedented scale.
Yesterdays 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 scalable 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.
Audiences: Everyone Is Invited
Contact: Lizsl De Leon