AutoST: AutoML for Saptio-Temporal Data

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Spatio-temporal (ST) prediction (e.g. crowd flow prediction) is of great importance in a wide range of smart city applications from urban planning, intelligent transportation and public safety. In this project, we study automated machine learning techniques (e.g. neural architecture search) for spatio-temporal data.

Senior Researcher

My research interests include deep learning, data mining, AI, big data analytics, and urban computing.