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HKUST: The Hong Kong University of Science and Technology (HKUST) is a leading international university ranked 1st by Times Higher Education Young University Rankings 2020 and 27th by the QS World University Rankings 2021. HKUST is establishing a new campus in Guangzhou, China (hkust-gz.edu.cn). Our new campus complements the existing Hong Kong campus, with extensive links and opportunities for intellectual exchange. We have an international outlook with staff, students and collaborations from around the globe, opening up a world of possibilities for our students. Cross-disciplinarity is at our heart: our new campus will be themed around hubs that bring together researchers from a range of disciplines. Collaboration is actively encouraged, and students will have the opportunity to work across traditional disciplinary boundaries. 
Location: The new HKUST (GZ) campus sits in 530,0002 metres of impressive parkland. It is adjacent to the Guangzhou-Shenzhen-Hong Kong High-speed Railway, and is accessible to six rail transit lines, including national and intercity railway links and metro lines. A high-speed train trip to Shenzhen takes 21 minutes and that to Hong Kong 35 minutes. Guangzhou itself is a diverse and exciting Alpha city, with a thriving tech sector.
About Dr. Gareth Tyson
Short Bio: Gareth Tyson is an Assistant Professor at IoT Thrust, HKUST (GZ), having served as a Senior Lecturer at Queen Mary University of London, and a Fellow at the Alan Turing Institute (UK). At Queen Mary University of London, he was Deputy Director of the Institute of Applied Data Science (IADS) and led the Social Data Science Lab (SDS). Over the last 4 years, he had been awarded almost ¥17 million in research funding. He has published more than 80 academic papers, with around 3100 citations and an h-index of 30. According to CSrankings.org, he is the highest ranked UK academic in both the Web and Measurement & Performance Analysis research categories. His work has received global coverage from news outlets such as MIT Tech Review, Washington Post, Slashdot, BBC, The Times, Daily Mail, Wired, Science Daily, Ars Technica, The Independent, Business Insider and The Register, as well being interviewed for TV, radio, and books. He serves as a program committee member for numerous “top tier” international conferences such as ACM SIGMETRICS, ACM IMC, ACM WWW, ACM CoNEXT, IEEE ICDCS and AAAI ICWSM, as well as regularly serving on the organizing committee of similar “top tier” venues. 
He has received various awards for his research and community contributions. He has received the Outstanding Reviewer Award four times at ICWSM (2016, 2018, 2019, 2021). His research was awarded the Facebook/WOAH Shared Task on Hateful Memes Prize 2021; the Best Student Paper Award at the Web Conference 2020; the Best Paper Award at eCrime'19; the Honourable Mention Award at the Web Conference 2018 (best paper in track); and the Best Presentation Award at INFOCOM'18. While at Queen Mary University of London, he received the Faculty Research Excellence Award in 2021 and the Brendan Murphy Memorial Young Researcher Prize in 2013.
Personal Website:
Research interests
1. Internet Measurements 
2. Web
3. Social Computing
4. Cybersafety
Research Projects
Example Project 1:Moderating malicious behavior has become a major challenge for organizations, such as Facebook, YouTube and Instagram. The arrival of always-connected sensing (e.g. smartphones) has further exacerbated this problem. For instance, the ability for any user to upload material at any time (e.g. text, videos, audio) means that these platforms can be exploited for various types of harmful activities, e.g. disseminating hate speech, stalking and cyberbullying. Due to this, huge teams of people are employed to manually monitor user activities and check if they breach platform policies. This is inefficient and non-scalable. This project involves (1) Collecting and analyzing large-scale web and IoT datasets that reveal how platforms perform existing harm moderation; and; then (2) Building on these datasets to develop new (machine learning) techniques for automated moderation. 
Example Project 2:Our lives are becoming increasingly connected through the deployment of numerous devices that integrate seamlessly into our environments, e.g. smartphones, watches, cameras and other forms of interactive sensors/actuators. This opens up a range of new privacy risks, with the potential for large-scale data collection and analysis (e.g. by international corporations). This data can subsequently be (mis)used for various purposes, which users may not give explicit informed consent for. This project involves (1) Developing data-driven methodologies to identify privacy risks within IoT services (e.g. smart cameras); and (2) Developing techniques to automatically filter the extrication of private data from devices in a fashion that enables privacy-enhanced IoT applications.
Recent select publications include:
  • Jinyang Li, Zhenyu Li, Gareth Tyson and Gaogang Xie. Characterising Usage Patterns and Privacy Risks of a Home Security Camera Service. In IEEE Transactions on Mobile Computing (TMC) (2021).
  • Anaobi Ishaku Hassan, Aravindh Raman, Ignacio Castro, Haris Bin Zia, Emiliano De Cristofaro, Nishanth Sastry and Gareth Tyson. Exploring Content Moderation in the Decentralised Web: The Pleroma Case. In 17th ACM International Conference on emerging Networking EXperiments and Technologies (CoNEXT), Munich, Germany (2021).
  • Yuheng Huang, Haoyu Wang, Lei Wu, Gareth Tyson, Xiapu Luo, Run Zhang, Xuanzhe Liu, Gang Huang and Xuxian Jiang. Understanding (Mis)Behavior on the EOSIO Blockchain. In ACM SIGMETRICS, Boston, MA (2020).
  • Yangyu Hu, Haoyu Wang, Ren He, Li Li, Gareth Tyson, Ignacio Castro, Yao Guo, Lei Wu and Guoai Xu. Mobile App Squatting. In 29th Web Conference (WWW), Taipei, Taiwan (2020).
  • Aravindh Raman, Sagar Joglekar, Emiliano De Cristofaro, Nishanth Sastry and Gareth Tyson. Challenges in the Decentralised Web: The Mastodon Case. In 19th ACM Internet Measurement Conference (IMC), Amsterdam, Netherlands (2019).
  • Damilola Ibosiola, Ignacio Castro, Gianluca Stringhini, Steve Uhlig and Gareth Tyson. Who Watches the Watchmen: Exploring Complaints on the Web. In 28th Web Conference (WWW), San Francisco (2019).
Admission Requirements
1. PhD Student applicants should have a passion for developing fundamentally new approaches to solving real-world problems. Our work is largely empirical, and we tend to follow a two-phase methodology. 
2. First, we gather real-world datasets that allow us to formalize and quantify specific problems. Second, we then develop new techniques that can address those newfound problems. For example, our recent research into Facebook Live involved gathering large-scale data on how people access such mobile video streaming services, before devising new techniques to improve user quality of experience. 
3. Thus, strong programming and data analysis skills are critical. A robust understanding of statistics and machine learning are a bonus. Applicants will be expected to have a high GPA from a recognized institute and possess a creative mindset. The lab collaborates internationally, and our working language is English. Hence, being open to work with a diversity of people and disciplines is vital.
If you’d like to apply for the PhD program in IoT Thrust and join Prof. Tyson’s team, you may contact him at [email protected] and submit the application via HKUST Online Application System. 
For more information, please visit:pg.ust.hk/gz 
Welcome to join!

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