Learning and Mining Large-Scale Spatiotemporal Data
Learning and Mining Large-Scale Spatiotemporal Data


Many real-world phenomena such as epidemics spreading, traffic flow, team sports, and climate variation involve complex spatial patterns evolving through time. Our ability to learn and mine from large-scale spatiotemporal data is critical to real-time decision making in various science and engineering fields. However, existing machine learning methods are still insufficient for large-scale spatiotemporal data due to the presence of non-linear, non-Euclidean, multi-resolution, high-dimensional, and complicated physical characteristics. The goal of this tutorial is to (1) provide an overview of the nature of spatiotemporal data and its relevance to the data mining community (2) survey recent development in machine learning to address the challenges specific to spatiotemporal data, and (3) identify the open problems and future directions. We believe this is an emerging and high-impact topic in machine learning, which will attract both academics and practitioners in data science as well as domain scientists.

Session Outline
(1) Overview
- Applications with large-scale spatiotemporal data
- Natural of Spatiotemporal Data
- Resource and references
(2) Practical Challenges
- Motif Discovery
- Missing Values
- Anomaly Detection
- Uncertainty Quantification
(3) Scalable Algorithms
-Dimensionality Reduction
-Multiresolution Modeling
-Deep sequence model
- Neural differential equations
(4) Modeling Frameworks
-Dynamics on Regular Grid
-Dynamics on Graphs
(5) Open Problems

Background Knowledge
- deep learning,
- time series,
- spatiotemporal modeling;


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.