Large-Scale Spatiotemporal Data Analytics and Learning
Project Description
This project focuses on the analysis and learning from large-scale spatiotemporal datasets. It aims to develop algorithms that can process and extract insights from data that varies across both space and time. The project will address challenges in data handling, visualization, and modeling to support applications in areas such as urban planning, climate analysis, and transportation systems.
Supervisor
ZHOU, Xiaofang
Quota
2
Course type
UROP1000
UROP1100
UROP2100
UROP3100
UROP3200
UROP4100
Applicant's Roles
Responsible for designing algorithms for spatiotemporal data analysis, implementing machine learning models, and ensuring effective data visualization techniques. Preferably have expertise in geographic information systems (GIS), data analytics, and statistical modeling.
Applicant's Learning Objectives
Gain knowledge in advanced spatiotemporal data analytics methods and machine learning applications. Develop skills in data visualization and interpretation, as well as working with large datasets to derive meaningful insights.
Complexity of the project
Moderate