Dr Bo Yuan

Dr Bo Yuan is a Lecturer in Computer Science at the University of Leicester. Prior to this role, he has became a Lecturer in Data Science at the University of Derby since 2019 after 2 years of working in the industry as a data scientist. 

Bo is a professional educator and is keen on cultivating scaffolding approaches and active learning in the classroom and fostering students' creativity and problem-solving skills through hands-on activities. Apart from teaching, Bo also supervise a number of final year projects and MSc projects which mainly concentrate on how to use data science / machine learning / AI to solve real-world analytical problems. Besides, Bo is also a personal tutor who provides pastoral care and support for our students.

Bo is an also an early-career researcher and his research is at the forefront of fields in AI and Edge Computing specifically in the context of designing scalable data intensive systems, and creating efficient AI algorithms and tools for real-time data analytics. He has substantial experience on knowledge exchange activities and projects to work with businesses and external partners in the domain of data science. Bo is broadly into applications of AI and machine learning, such as Driverless Cars, Game AI, Business Intelligence, Virtual Reality/Augmented Reality, and Digital Twins.

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.

Opportunities (open for paper submission):

I am the Chair of MLHI workshop (December 6-9, 2022 - Portland, Oregon, USA): 2^ndInternational Workshop on Machine Learning and Health Informatics (MLHI2022). Open for submission by 15 October 2022 (extended).

Guest Editor of JSAN Special Issue: "Federated Learning for Internet of Things" The deadline for manuscript submissions is 31 March 2023.

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. 

Recent Publications (a selection):

• M.S.H. Zada, B. Yuan, W.A. Khan, A. Anjum, S. Reiff-Marganiec, and R. Saleem, 2022. A unified graph model based on molecular data binning for disease subtyping. Journal of Biomedical Informatics, p.104187.
• R. Saleem, B. Yuan, F. Kurugollu, A. Anjum, and L. Liu, 2022. Explaining Deep Neural Networks: A Survey on the Global Interpretation Methods. Neurocomputing.
• J. Gu., A. Anjum, Y. Wu, L. Liu, J. Panneerselvam, Y. Lu, and B. Yuan, 2022. The least-used key selection method for information retrieval in large-scale Cloud-based service repositories. Journal of Cloud Computing, 11(1), pp.1-19.
• 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.
• 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.
• 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.
• 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. 
• 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. 
• 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. 
• 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.
• 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. 
• 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. 

PhD Students

• Ruhul Kabir Howlader, Topic: "Federated AI for Driverless Cars";
• Rabia Saleem (starting soon), Topic: "Explaining probabilistic AI models by Discretising Deep Neural Networks"

MPhil Students

• Ebrahim Lambat, Topic: "Advanced Data Analytics for Health and Wellbeing"

Module Convenor for the following modules:

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