Learning from Large-Scale Spatiotemporal Data
Learning from Large-Scale Spatiotemporal Data

Abstract: 

Applications such as climate science, intelligent transportation, aerospace control, and sports analytics apply machine learning for large-scale spatiotemporal data. This data is often nonlinear, high-dimensional, and demonstrates complex spatial and temporal correlation. Existing machine learning models cannot handle complex spatiotemporal dependency structures. We'll explain how to design machine learning models to learn from large-scale spatiotemporal data, especially for dealing with non-Euclidean geometry, long-term dependencies, and logical and physical constraints. We'll showcase the application of these models to problems such as long-term forecasting for transportation, long-range trajectories synthesis for sports analytics, and combating ground effect in quadcopter landing for aerospace control.

Bio: 

Dr. Rose Yu is an Assistant Professor at Northeastern University Khoury College of Computer Sciences. Previously, she was a postdoctoral researcher in the Department of Computing and Mathematical Sciences at Caltech. She earned her Ph.D. 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 its applications, especially in the emerging field of computational sustainability. 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.

Open Data Science

 

 

 

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