Dr Bo Yuan

Dr Bo Yuan is a Lecturer in Computer Science. He is keen on teaching students at all levels to use data analytics to deal with real-world problems and gain a competitive advantage of employability skill that goes beyond HE qualifications. His research is at the forefront of fields in artificial intelligence (AI) and distributed computing specifically in the context of designing bespoke data science workflow and creating advanced AI tools for big data analytics (social and healthcare domain mainly).

I have a broad interest in efficient AI and machine learning for real-time data analytics. Recently I particularly looked at the following Research themes:

• Federated Learning framework for the Internet of autonomous vehicles, which aims to connect and exchange critical environmental information over the Internet according to agreed standards to support more transparent faster and safer on-vehicle decisions. The research is linked with the school’s project DriverLeics.
• Federated Learning for Healthcare, which primarily focuses on a rigorous design of a federated learning framework for digital healthcare and the development of new collaborative AI algorithms to support decision-making across various institutions without compromising patients’ privacy.

Completed research projects:

• KTP project on Data Warehouse and Edge Computing ( FP Solutions & University of Derby), funded by the Innovate UK, 2019-2021
• KTP project on Data Science for Virtual Reality (Bloc Digital & University of Derby, funded by the Innovate UK, 2019-2021

Other Academic Services:

• Workshop Chair for MLHI 2021-2022 in UCC/BDCAT 2021-2022 conference, Workshop co-chair of ScalCom2019.
• Proceeding chair of UCC2016 Organising Committee of UCC2014-2016 Publicity Chair of SOSE2016.
• Technical Program Committee of MSN2021 ICMR2021 BDSIC2021 CyberLife2019-2020 ScalCom2019 DaMnet2019 RTDPCC2016-2021 BDCAT2016-2021. 

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1. R. Zhu, L. Liu, X. Liu, and B. Yuan, A Blockchain-Based Two-Stage Secure Spectrum Intelligent Sensing and Sharing Auction Mechanism, IEEE Transactions on Industrial Informatics, 2021.

2. Y. Lu, J. Gu, L. Liu, J. Pannearselvam, and B. Yuan, EA-DFPSO: An Intelligent Energy-efficient Scheduling Algorithm for Mobile Edge Networks, Digital Communications and Networks, 2021.

3. J. Gu, Y. Wu, A. Anjum, J. Panneerselvam, Y. Lu, and B. Yuan, Optimization of Service Addition in Multilevel Index Model for Edge Computing, Concurrency and Computation: Practice and Experience, 2021.

4. B. Yuan, J. Panneerselvam, L. Liu, N. Antonopoulos, and Y. Lu, An inductive content-augmented network embedding model for edge artificial intelligence, IEEE Transactions on Industrial Informatics, vol. 15, no. 7, pp. 4295-4305, 2019. 

5. Y. Lu, L. Liu, J. Panneerselvam, B. Yuan, J. Gu, and N. Antonopoulos, A gru-based prediction framework for intelligent resource management at cloud data centres in the age of 5g, IEEE Transactions on Cognitive Communications and Networking, vol. 6, no. 2, pp. 486-498, 2019. 

6. L. Jiang, L. Shi, L. Liu, J. Yao, B. Yuan, and Y. Zheng, An efficient evolutionary user interest community discovery model in dynamic social networks for internet of people, IEEE Internet of Things Journal, vol. 6, no. 6, pp. 9226-9236, 2019. 

7. B. Yuan, L. Liu, and N. Antonopoulos, Efficient service discovery in decentralized online social networks, Future Generation Computer Systems, vol. 86, pp. 775-791, 2018.

8. J. Panneerselvam, J. Hardy, L. Liu, B. Yuan, and N. Antonopoulos, Mobilouds: An energy efficient MCC collaborative framework with extended mobile participation for next generation networks, IEEE Access, vol. 4, pp. 9129-9144, 2016. 

9. M. Ahmed, L. Liu, J. Hardy, B. Yuan, and N. Antonopoulos, An efficient algorithm for partially matched services in internet of services, Personal and Ubiquitous Computing, vol. 20, no. 3, pp. 283-293, 2016. 

10. B. Yuan, A. Anjum, J. Panneerselvam, and L. Liu, "Exploring Network Embedding for Efficient Message Routing in Opportunistic Mobile Social Networks," in 2019 International Conference on Data Mining Workshops (ICDMW), 2019, pp. 497-504: IEEE Computer Society.

 

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Module Convenor for the following modules:

• CO1109 Business and Financial Computing (Spring)
• CO3219 Internet and Cloud Computing (Autumn)

Artificial Intelligence (AI); Machine Learning; Data Science; Big Data Analytics; Business Intelligence