Shuai Li

Contact information

Main Contact:
Cambridge University Engineering Department (CUED)
Trumpington Street, Central Cambridge, Cambridge
United Kingdom


Shuai Li is from The University of Cambridge, serves on the Editorial Board as an Associate Editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence, with the highest impact factor 17.861 in all fields of Computer Science and Technology, as well as a member of the Cambridge University Strategic Research Initiative in Big Data, also known as the Cambridge University's Centre for Data-Driven Discovery. He completed his postdoctoral experience in Engineering at the Academic Division of Mechanics, Materials and Design, Department of Engineering, University of Cambridge, and completed his doctoral experience in Informatics and Computational Mathematics at the Department of Theoretical and Applied Sciences, University of Insubria, respectively. He has an interdisciplinary background and his major directions of research expertise are in Artificial Intelligence, Machine Learning, Information Retrieval, and Data Mining, etc. He has more than ten years of professional experience across the Asia Pacific, Europe, North America, and the Middle East. He gave invited talks or lectures for Cambridge, Oxford, and Stanford, as well as other well-known enterprises, and he received invites to serve as the CTO for London based companies, etc.

Research interests

Artificial Intelligence, Big Data, Machine Learning


Paper to Appear in the International Conference on Supercomputing 2020
W/ S. Li, W. Chen, & K. Leung, “Improved Algorithm on Online Clustering of Bandits”, Proceedings of the 28th International Joint Conference on Artificial Intelligence, Main track, pp. 2923-2929, Macao, CN, 2019
W/ C. Gentile, & A. Karatzoglou, “Graph Clustering Bandits for Recommendation”, arXiv preprint arXiv:1605.00596 2018
W/ C. Gentile, A. Karatzoglou, P. Kar, E. Howard, & G. Zappella, “On Context-Dependent Clustering of Bandits”, Proceedings of the 34th International Conference on Machine Learning, Journal of Machine Learning Research, pp. 1253-1262, Sydney, New South Wales, AU, 2017
S. Li, “The Art of Clustering Bandits”, Dissertation, Università degli Studi dell’Insubria, 2016
W/ A. Karatzoglou, & C. Gentile, “Collaborative Filtering Bandits”, Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 539-548, Pisa, Tuscany, IT, 2016
W/ N. Korda, & B. Szörényi, “Distributed Clustering of Linear Bandits in Peer to Peer Networks”, Proceedings of the 33rd International Conference on Machine Learning, Journal of Machine Learning Research, pp. 1301-1309, New York City, New York, US, 2016
W/ P. Kar, H. Narasimhan, S. Chawla, & F. Sebastiani, “Online Optimization Methods for the Quantification Problem”, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1625-1634, San Francisco, California, US, 2016
W/ F. Hao, D. Park, & H. Lee, “Mining λ-Maximal Cliques from a Fuzzy Graph”, Advanced IT based Future Sustainable Computing, Journal of Sustainability, 8(6), pp. 553; doi: 10.3390/su8060553, 2016
W/ F. Hao, G. Min, H. Kim, S. Yau, & L. Yang, “An Efficient Approach to Generating Location-Sensitive Recommendations in Ad-hoc Social Network Environments”, IEEE Transactions on Services Computing, ISSN: 1939-1374, DOI: 10.1109, Issue:99, pp. 520-533, IEEE Computer Society, 2015
W/ C. Gentile, & G. Zappella, “Online Clustering of Bandits”, Proceedings of the 31st International Conference on Machine Learning, Journal of Machine Learning Research, pp. 757-765, Beijing, CN, 2014
W/ H. Guo, Y. Feng, F. Hao, & S. Zhong, “Dynamic Fuzzy Logic Control of Genetic Algorithm Probabilities”, Journal of Computers, DOI: 10.4304/jcp. 9.1.22- 27, Vol. 9, No. 1, pp. 22-27, 2013
W/ F. Hao, & H. Kim, “Social Media Review Mining for Living Items with Probabilistic Approach”, Smart Media Journal, Media, Information Systems, ISSN:2287- 1322, Vol. 2, No. 2, pp. 20-27, 2013
W/ F. Hao, M. Li, & H. Kim, “Medicine Rating Prediction and Recommendation in Mobile Social Networks”, Proceedings of the International Conference on Green and Pervasive Computing, Vol. 7861, pp. 216-223, Seoul, Korea, 2013
W/ F. Hao, M. Li, & H. Kim, (Best Paper Award) “Machine Learning for Personalized Medicine”, Proceedings of Korea Institute of Smart Media Fall Conference, Vol. 1, No. 2, pp. 25-27, Korea, 2012

About us

The Cambridge Centre for Data-Driven Discovery (C2D3) brings together researchers and expertise from across the academic departments and industry to drive research into the analysis, understanding and use of data science.

  • Supports and connects the growing data science research community 
  • Builds research capacity in data science to tackle complex issues 
  • Drives new research challenges through collaborative research projects 
  • Promotes and provides opportunities for knowledge transfer 
  • Identifies and provides training courses for students, academics, industry and the third sector 
  • Acts as a gateway for external organisations 

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