Distinguished scholars from around the world gathered at the HK Tech Forum on Data Science and AI (DSAI), hosted by the Hong Kong Institute for Advanced Study (HKIAS) at City University of Hong Kong (CityU) from 26 to 27 July, to address challenging issues in driving data science and AI technology for the benefit of society.

The DSAI Forum was the first in CityU’s HK Tech Forum series, which will invite a host of world-leading scholars to promote collaboration in tackling long-standing scientific puzzles and challenging technology issues. 

President Way Kuo of CityU extended a warm welcome to all the participants at the opening ceremony on 26 July. He said we need profound and deep research in data science and AI as we embrace the challenges and opportunities. 

 

“It is an honour for us to host such a conference, which will give us an opportunity to engage with delegates on a topic of great interest and potential. Over the past decade, CityU has been one of the most progressive universities in the world. Our dynamism, energy and boldness in tackling difficult problems stand out among our many unique features,” he said. 

The HK Tech Forum series at CityU was initiated by President Kuo. Under the theme “Empowering society for a better future”, it serves as a dynamic platform at CityU for examining innovative strategies and approaches to major challenges in science and technology, said Professor Wang Xunli, chief coordinator of the Forum series and Head of the Department of Physics at CityU.

Professor Joe Qin Sizhao, Dean and Chair Professor of the School of Data Science (SDSC) and Director of the Hong Kong Institute for Data Science (HKIDS), was the coordinator of the DSAI Forum. He said the aim of the two-day forum is to discuss new technological developments among world-leading DSAI scholars in Hong Kong and across the world.

The first day of the forum featured a plenary talk by Turing award winner Professor John Hopcroft, IBM Professor of Engineering and Applied Mathematics in Computer Science at Cornell University, US, on the topic “Math for the Big Data Revolution”.

In his talk, he explained the mathematical background needed to be successful in the information age. The size of data has become enormous. One needs significant mathematical tools to process and abstract information from big data collections, and processing larger and larger data sets will become common. 

The second day of the forum featured another plenary speech by famed AI entrepreneur Dr Lee Kai-Fu, Chairman and CEO of Sinovation Ventures.

In his talk, titled “How AI will Transform Our World”, Dr Lee guided us through the most current breakthroughs in the fields of AI, automation and robotics, life sciences, new energy, quantum computing, and the cross-pollination possibilities across these disciplines. 

The DSAI Forum was organised by HKIDS, HKIAS, and SDSC of CityU.

HK Tech Forum on Data Science and AI
http://www.ncm2.xyz/hktechforum/cof-data-science-and-ai-desc.htm

World-renowned academic and industry leaders were invited to give keynote speeches at the Forum to address pressing issues and challenges, including Professor Ma Yi, University of California, Berkeley, US; Professor Tao Dacheng, JD Explore Academy; Professor Nick Sahinidis, Georgia Institute of Technology, US; and Professor Yang Qiang, Hong Kong University of Science and Technology. 

Topics discussed during the two-day forum include “CTRL: Closed-Loop Data Transcription via Rate Reduction”; “Scalable, Heterogeneity–Aware and Trustworthy Federated Learning”; “Data-driven Optimization”; and “Quantum Computing for Optimization and Machine Learning: From Models and Algorithms to Use Cases”, to mention but a few.

HK Tech Forum series
http://www.ncm2.xyz/hktechforum/

Six forums in the series will be held in the second half of 2022 with plans to expand it into 2023 and beyond. The topics of the next five forums are Reliability and Safety of Intelligent Systems, Advanced Matter and Materials, Carbon Neutrality and Sustainable Environment, Quantum Physics and Complex Systems, and Metabolism in Health and Disease.