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

Abstract: 

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;

Bio: 

Dr. Rose Yu is an assistant professor at the University of California San Diego, Department of Computer Science and Engineering. She earned her Ph.D. in Computer Sciences at the University of Southern California in 2017. She was subsequently a Postdoctoral Fellow at the California Institute of Technology. She was an assistant professor at Northeastern University prior to her appointment at UCSD. Her research focuses on advancing machine learning techniques for large-scale spatiotemporal data analysis, with applications to sustainability, health, and physical sciences. A particular emphasis of her research is on physics-guided AI which aims to integrate first-principles with data-driven models. Among her awards, she has won Google Faculty Research Award, Adobe Data Science Research Award, NSF CRII Award, Best Dissertation Award in USC, and was nominated as one of the ’MIT Rising Stars in EECS’.