James She, Department of Electronic and Computer Engineering
Hong Kong University of Science and Technology
The information about the social graph of a user (i.e., the social connections and related interactions of a user) is essential for many social computing techniques and applications, such as content recommendation, user profiling and community detection, in many social media and e-commerce platforms. However, this online social information is getting difficult to access due to the user privacy settings, or exclusively accessible by dominating platforms, such as Facebook, Instagram, Tencent Wechat, Pinterests, etc. and their affiliated parties. Without relying on this social graph information alone, our research investigates the possibility and how to learn the user backgrounds, interests and their connections through their shared content (e.g., user-generated pictures, videos, and their liked, shared and commented media content) using some novel machine learning and social computing techniques.
In this talk, I will describe how the joint approaches of multimedia big data analytics and social computing can offer a more accessible and powerful alternative to achieve many social computing techniques with comparable performance and interesting social applications even without the social graphs. Besides sharing our excited findings, the results from this area of our research have also led to some impacts beyond the publications, such as some patent-filing, tech-transfer activities and a startup company founded by our researcher with the related innovations from this research.
With the recent advancements in machine learning and AI technologies, more interesting research challenges and opportunities on data science in multimedia and social computing are induced. Hence, we are able to better understand what content to be recommended or generated at certain timings or locations for emerging social media and multimedia applications in our smart cities and digital societies.
Bio: James She is an Assistant Professor in the Department of Electronic and Computer Engineering at the Hong Kong University of Science and Technology (HKUST). He is also the founding director of Asia's first social media lab, HKUST-NIE Social Media Lab, and spearheads multidisciplinary research and innovation on the areas of: 1) data science in multimedia; 2) social computing, media and networks; 3) AI technologies for art, design and media; 4) IoT/ communication technologies for media systems. With these research interests, James and his research group have produced over 80+ publications in top journals and conferences with several best papers awards, innovations awards, patents and open-sourced software/hardware contributions. Some of these research outputs have led to several technology transfer activities and even startup companies supported by individual and venture investors.
In addition, James is the associate editor for both IEEE Transaction on Multimedia and ACM Transaction on Multimedia Computing, Communications and Applications. Beside taking up the organizing roles in several international conferences and academic events, he also served as the advisor or consultant for some projects/organizations in private, governments and non-profit sectors, such as World Economic Forum's Global Agenda Council (Social Media), Hong Kong Consumer Council, as well as some local high-tech companies.
He holds a Ph.D. in Electrical and Computer Engineering from the University of Waterloo, and he was the recipient of multiple national, university and scientific community awards, including the NSERC Postdoctoral Fellowship Award, NSERC Innovation Challenge Award, Canada-UK Millennium Research Award, Outstanding Achievement of Graduate Studies, Associate Editor of the Year 2017 (ACM TOMM), etc.
Please see the HKUST-NIE Social Media Lab for more detail.
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