Professor Yonghong Peng

Professor Yonghong Peng is the Professor of Artificial Intelligence. He pioneers research in Artificial Intelligence (AI), machine learning and data science, particularly concerning algorithmic explainability, transparency, and model security, aiming to improve AI safety and enable human-centred AI. His research includes technological advancements and application innovation across medical and health, environment and sustainability.

LinkedIn: https://www.linkedin.com/in/yonghpeng/

Google Scholar: https://scholar.google.co.uk/citations?user=gcSNeHkAAAAJ&hl=en

ORCID: https://orcid.org/0000-0002-5508-1819

Email: [email protected]

Background

Before his current role, Yonghong was the Professor of Artificial Intelligence, and served as Director of the University Centre for Advanced Computational Science at Manchester Metropolitan University (MMU), where he provided strategic leadership in fostering AI-powered interdisciplinary research and innovation and establishing strategic partnerships. He led the MMU's membership in the Turing University Network. He led his team in delivering a comprehensive assessment on the Cyber Security Risk to Artificial Intelligence for the UK Department for Science, Innovation and Technology (DSIT).

He joined ARU in June 2024 with aspiration to advance AI technologies and AI-powered interdisciplinary research and innovation to effectively leverage Cambridge's ecosystems in science, health, and deep tech and beyond.

Spoken Languages

English and Chinese

Research interests

Yonghong’s research aims to advance AI technology and architectures to tackle the fundamental challenges of AI in our rapidly evolving world.

In this rapidly evolving world, the transformative power of AI is set to revolutionise every industrial sector and public service, and the rapid evolution of AI is widening the gap between human capability and AI power, raising profoundly impact on individuals and society as a whole.

Yonghong’s research is developing a new framework, namely Human-AI Cooperative Architectures, to bridge the human-AI divide holistically by leveraging responsible AI and explainable machine learning, as well as enhancing ethical AI governance.

In this paradigm, users will have the opportunity to contribute personal context to the developing of AI systems, allowing AI to better understand individual needs, for developing personalised AI models to enhance user experiences. The research includes advancing machine learning to effectively learn from multimodal data and improving explainability of machine learning.

Overall, Yonghong’s research involves two main areas:

Technology Advancement:

• Advancing Machine Learning and AI Architectures: Focusing on machine learning of multimodal data to enhance robustness and performance, with an emphasis on the explainability of machine learning on multimodal data such as video, images, sensor data, scientific research data, and geospatial data.
• Safety of AI Models and Systems: Developing enabling technologies to address vulnerabilities and cybersecurity throughout the AI lifecycle.
• Human-AI Cooperative Models and Systems: Creating new architectures for behaviour understanding, cognitive machine learning, and enhancing algorithm and model explainability and transparency.

Applications and impacts of AI:

• Medical and Health Data Science
• Data Science in Environment and Sustainability
• AI's Impact on Society and Wellbeing

Areas of expertise

• AI for Medicine and Health, Bioinformatics
• AI for Environment and sustainability
• AI in industries, urban living, and future cities
• AI impact on society and wellbeing.

Areas of research supervision

Prof Peng welcomes high motivated researchers to join our AI interdisciplinary research. He is interested in supervising MPhil, PhD, Postdoctoral Researchers and Visiting Researchers who are excited in fundamental research and / or applied research in the following areas:

• AI, Machine Learning on multi-modal data, and algorithmic explainability and transparency, model robustness and security
• Generative AI, and security and risk of foundation models
• Medical and Health Data Science, particular on the multimorbidity, and ageing health
• AI impact on agriculture and food security, environment, and sustainability
• Emerging areas that AI can make significant impacts such as human creativity, and wellbeing

Qualifications

PhD, South China University of Technology

Fellow, HEA

Memberships, editorial boards

• UKRI - Economic and Social Research Council (ESRC), Member of Peer Review College
• IEEE Member
• Chair of Big Data Task Force, IEEE Computational Intelligence Society
• Associate Editor, IEEE Access
• Academic Editor, PeerJ Journal
• Academic Editor, PeerJ Computer Science

Research grants, consultancy, knowledge exchange

Grant Thornton UK LLP (GTUK) Partnership - AI Innovation and Incubation

Visiting Professor (AI for Health), Northern Care Alliance NHS Foundation Trust (Oct 2021-)

Academic Honorary Member, Office of Health Improvement and Disparities (OHID) of Department of Health and Social Care (2021-)

Visiting Professor (AI for Health), Salford Royal NHS Foundation Trust (Oct 2020-)

Visiting Professor, Xiangya Hospital of Central South University (May 2020-)

Academic Honorary Member, Public Health England (Feb 2018-)

Selected recent publications

Yue, H., Qing, L., Zhang, Z., Wang, Z., Guo, L., Peng, Y. (2024) 'MSE-Net: A novel master–slave encoding network for remote sensing scene classification.' Engineering Applications of Artificial Intelligence, 132 https://doi.org/10.1016/j.engappai.2024.107909

Li, T., Dong, X., Lin, J., Peng, Y. (2024) 'A transformer-CNN parallel network for image guided depth completion.' Pattern Recognition, 150 https://doi.org/10.1016/j.patcog.2024.110305

Qing, L., Wen, H., Chen, H., Jin, R., Cheng, Y., Peng, Y. (2024) 'DVC-Net: a new dual-view context-aware network for emotion recognition in the wild.' Neural Computing and Applications, 36(2) pp. 653-665. https://doi.org/10.1007/s00521-023-09040-8

Huang, C., Hong, D., Yang, C., Cai, C., Tao, S., Clawson, K., Peng, Y. (2023) 'A new unsupervised pseudo-siamese network with two filling strategies for image denoising and quality enhancement.' Neural Computing and Applications, 35(31) pp. 22855-22863. https://doi.org/10.1007/s00521-021-06699-9

Heald, A., Qin, R., Williams, R., Warner-Levy, J., Narayanan, R.P., Fernandez, I., Peng, Y., Gibson, J.M., McCay, K., Anderson, S.G., Ollier, W. (2023) 'A Longitudinal Clinical Trajectory Analysis Examining the Accumulation of Co-morbidity in People with Type 2 Diabetes (T2D) Compared with Non-T2D Individuals.' Diabetes Therapy, 14(11) pp. 1903-1913. https://doi.org/10.1007/s13300-023-01463-9

Billows, N., Phelan, J.E., Xia, D., Peng, Y., Clark, T.G., Chang, Y.M. (2023) 'Feature weighted models to address lineage dependency in drug-resistance prediction from Mycobacterium tuberculosis genome sequences.' Bioinformatics, 39(7) https://doi.org/10.1093/bioinformatics/btad428

Zeng, N., Li, H., Peng, Y. (2023) 'A new deep belief network-based multi-task learning for diagnosis of Alzheimer’s disease.' Neural Computing and Applications, 35(16) pp. 11599-11610. https://doi.org/10.1007/s00521-021-06149-6

Huang, J., Qing, L., Han, L., Liao, J., Guo, L., Peng, Y. (2023) 'A collaborative perception method of human-urban environment based on machine learning and its application to the case area.' Engineering Applications of Artificial Intelligence, 119. https://doi.org/10.1016/j.engappai.2022.105746

Tang, W., Qing, L., Gou, H., Guo, L., Peng, Y. (2023) 'Unveiling Social Relations: Leveraging Interpersonal Similarity Learning for Social Relation Recognition.' IEEE Signal Processing Letters, 30pp. 1142-1146. https://doi.org/10.1109/LSP.2023.3306152

Heald, A.H., Jenkins, D.A., Williams, R., Mudaliar, R.N., Naseem, A., Davies, K.A.B., Gibson, J.M., Peng, Y., Ollier, W. (2022) 'COVID-19 Vaccination and Diabetes Mellitus: How Much Has It Made a Difference to Outcomes Following Confirmed COVID-19 Infection?.' Diabetes Therapy, 14pp. 193-204. https://doi.org/10.1007/s13300-022-01338-5

Li, L., Qing, L., Wang, Y., Su, J., Cheng, Y., Peng, Y. (2022) 'HF-SRGR: a new hybrid feature-driven social relation graph reasoning model.' Visual Computer, 38(11) pp. 3979-3992. https://doi.org/10.1007/s00371-021-02244-w

Li, L., Qing, L., Guo, L., Peng, Y. (2022) 'Relationship existence recognition-based social group detection in urban public spaces.' Neurocomputing, 516pp. 92-105. https://doi.org/10.1016/j.neucom.2022.10.042

Heald, A.H., Jenkins, D.A., Williams, R., Sperrin, M., Mudaliar, R.N., Syed, A., Naseem, A., Bowden Davies, K.A., Peng, Y., Peek, N., Ollier, W., Anderson, S.G., Delanerolle, G., Gibson, J.M. (2022) 'Mortality in People with Type 2 Diabetes Following SARS-CoV-2 Infection: A Population Level Analysis of Potential Risk Factors.' Diabetes Therapy, 13(5) pp. 1037-1051. https://doi.org/10.1007/s13300-022-01259-3

Heald, A.H., Jenkins, D.A., Williams, R., Sperrin, M., Fachim, H., Mudaliar, R.N., Syed, A., Naseem, A., Gibson, J.M., Bowden Davies, K.A., Peek, N., Anderson, S.G., Peng, Y., Ollier, W. (2022) 'The risk factors potentially influencing hospital admission in people with diabetes, following SARS-CoV-2 infection: a population-level analysis.' Diabetes Therapy, 13(5) pp. 1007-10021. https://doi.org/10.1007/s13300-022-01230-2

Huang, Y., Qing, L., Xu, S., Wang, L., Peng, Y. (2022) 'HybNet: a hybrid network structure for pain intensity estimation.' Visual Computer, 38(3) pp. 871-882. https://doi.org/10.1007/s00371-021-02056-y

Heald, A.H., Chang, K., Jia, T., Sun, H., Zheng, Q., Wang, X., Xia, J., Stedman, M., Fachim, H., Gibson, M., Zhou, X., Anderson, S.G., Peng, Y., Ollier, W. (2021) 'Longitudinal clinical trajectory analysis of individuals before and after diagnosis of Type 2 Diabetes Mellitus (T2DM) indicates that vascular problems start early.' International Journal of Clinical Practice, 75(11) https://doi.org/10.1111/ijcp.14695