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Abstract
Recent advancements in remote sensing technologies and large-scale computing infrastructure have led to an unprecedented volume of spatio-temporal data in various fields. Integrating human-centered machine learning techniques with this data is crucial, but the quality of training data is not always guaranteed in the era of big spatio-temporal data. Furthermore, in urban computing applications, accurate predictive models alone may be insufficient, and providing efficient and interpretable predictions is equally important. To address these challenges, this presentation will showcase innovative research on developing robust and efficient spatio-temporal graph learning frameworks. These frameworks enable better analysis of large-scale spatio-temporal data for smart cities and improve the scalability of the models.
Speaker
Dr Chao Huang is an Assistant Professor at the Musketeers Foundation Institute of Data Science & Department of Computer Science, at the University of Hong Kong. His research interests include data mining, graph neural networks, urban computing, and recommender systems. As the first author or supervisor, he has received Best Paper Nominations at WWW’2019, WSDM’2022, and WWW’2023. The academic achievements of his laboratory have been selected as the most influential papers at SIGIR’2022, ranking 1st and 3rd. He has also been recognized by Stanford University as a top 2% scientist worldwide in 2022. Moreover, he has participated in organizing top data science conferences such as KDD’2022, Recsys’2023, and WSDM’2023 and has been awarded the Outstanding Reviewer Award at WSDM’2020 and WSDM’2022.
For full biography of Dr. Huang, please refer to: https://datascience.hku.hk/people/chao-huang/
Moderator
Professor Yi Ma is a Chair Professor in the Musketeers Foundation Institute of Data Science (HKU IDS) and Department of Computer Science at the University of Hong Kong. He took up the Directorship of HKU IDS on January 12, 2023. He is also a Professor at the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He has published about 60 journal papers, 120 conference papers, and three textbooks in computer vision, generalized principal component analysis, and high-dimensional data analysis.
Professor Ma’s research interests cover computer vision, high-dimensional data analysis, and intelligent systems. For full biography of Professor Ma, please refer to: https://datascience.hku.hk/people/yi-ma/
For information, please contact:
Email: datascience@hku.hk