Stats, Optimization, and Machine Learning Seminar - Rose Yu

Rose Yu, College of Computer and Information Science, Northeastern University

Learning from Large-Scale Spatiotemporal Data

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.

 
 

Wednesday, September 19, 2018 at 3:00pm to 4:00pm


Engineering Center, ECOT 226
1111 Engineering Drive, Boulder, CO 80309

Event Type

Colloquium/Seminar

Interests

Science & Technology, Research & Innovation

Audience

Students, Graduate Students, Postdoc

College, School & Unit

Engineering & Applied Science

Tags

statistics, optimization, machine learning

Group
Applied Mathematics
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