KEYNOTES

Title:

Speaker: Shanlin Yang, Academician ,Chinese Academy of Engineering,China

Abstract

Short Bio:

Title:

Speaker: Jun Lu, Academician ,Chinese Academy of Engineering,China

Abstract

Title:
Human-centred Perspectives for Data Quality Discovery

Speaker:  Shazia Sadiq, The University of Queensland, Australian Academy of Engineering, Australian

Abstract

Data workers and platforms are facing a number of challenges that have emerged from re-purposing of data and lack of data quality awareness. In this talk, I will outline how human centred perspectives can assist in overcoming some of these challenges to achieve responsible and efficient use of data.

Short Bio:

Dr Shazia Sadiq FTSE is a Professor of Computer Science at the University of Queensland. Her research focusses on data quality management and effective information use. Shazia is currently Chair of the National Committee on Information and Communication Sciences at the Australian Academy of Science, a member of The Australian Research Council College of Experts 2018-2021, and Centre Director for the ARC Industry Transformation Training Centre on Information Resilience 2020-2025. She is a Fellow of the Australian Academy of Technological Sciences and Engineering.

Title:
Data Management for Effective and Efficient Deep Learning

Speaker: Lei Chen, Hong Kong University of Science and Technology, IEEE Fellow, China

Abstract

In recent years, deep learning (DL) has significantly penetrated and has been widely adopted in various fields of application, including facial recognition, strategy games (AlphaGo and Texas hold'em) and question answering. However, the effectiveness of the models and efficiency of the training process strongly depend on how well the associated data is managed. It is very challenging to train an effective deep learning-based image classifier without properly labelled training data. Furthermore, training efficiency is severely affected by a large amount of training data, complex structures of the models and tones of hyper parameters. A lack of validation for result data and explanation also seriously affect the applicability of trained models. In this talk, I will discuss three issues on how to manage data for effective and efficient deep learning: 1) how to prepare data for effective DL, which includes data extraction and integration as well as data labelling; 2) how to optimize DL training, including data compression and computation graph optimization; and 3) how to conduct explanation to make the model robust and transparent. Some future work will be highlighted at the end.

Bio Sketch:

Lei Chen has BS degree in computer science and engineering from Tianjin University, Tianjin, China, MA degree from Asian Institute of Technology, Bangkok, Thailand, and PhD in computer science from the University of Waterloo, Canada. He is a chair professor in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology (HKUST). Currently, Prof. Chen serves as the head of Data Science and Analytic trust at HKUST (GZ), director of Big Data Institute at HKUST, director of HKUST MOE/MSRA Information Technology Key Laboratory. Prof. Chen’s research interests include human-powered machine learning, crowdsourcing, Blockchain, graph data analysis, probabilistic and uncertain databases and time series and multimedia databases. Prof. Chen got the SIGMOD Test-of-Time Award in 2015.The system developed by Prof. Chen’s team won the excellent demonstration award in VLDB 2014. Prof. Chen has served as VLDB 2019 PC Co-chair and Editor-in-Chief of VLDB Journal. Currently, Prof. Chen serves as Editor-in-Chief of IEEE Transaction on Data and Knowledge Engineering and PC Co-chairs of IEEE Conference on Data Engineering (ICDE 2023). He is an IEEE Fellow, ACM Distinguished Member and an executive member of the VLDB endowment.

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