Dr Bing Zhai

Assistant Professor

Department: Computer and Information Sciences

I am a Lecturer in Computer Science. My research agenda is to develop practical AI tools to solve time-series data challenges in real-world applications. I am particularly interested in time series data analysis, e.g., biosignal analysis, computational behaviour analysis and healthcare applications. I am also interested in AI for good, computer vision and audio/speech analysis.

In essence, it is to model the practical problems using mathematical languages and develop machine learning algorithms for the optimal solution, bridging the gap between signal/data and human-understandable knowledge. In particular, I have experience developing ML/DL algorithms for biosignal data-based applications in physical behaviour assessment and health and well-being monitoring.

I was a research associate at the School of Computing at Newcastle University, working on the IDEA-FAST project (€40 million) to identify digital endpoints and biomarkers of sleep disturbance and fatigue. During this time, I obtained my PhD in data science for healthcare from the School of Computing, Newcastle University.

At Northumbria University, I currently conduct sleepiness and fatigue research using machine learning methods and collaborate with more than a dozen research institutions on the IDEA-FAST project.

Website: https://bzhai.github.io/

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Key Publications

Please visit the Pure Research Information Portal for further information
DSleepNet: Disentanglement Learning for Personal Attribute-agnostic Three-stage Sleep Classification Using Wearable Sensing Data, Zhai, B., Duan, H., Guan, Y., Phan, H., Woo, W. 13 Feb 2025, In: IEEE Journal of Biomedical and Health Informatics
Parameter Efficient Fine-Tuning for Multi-modal Generative Vision Models with Möbius-Inspired Transformation, Zhai, B., Duan, H., Shao, S., Shah, T., Han, J., Ranjan, R. 14 Feb 2025, In: International Journal of Computer Vision
Challenges and opportunities of deep learning for wearable-based objective sleep assessment, Zhai, B., Elder, G., Godfrey, A. 4 Apr 2024, In: npj Digital Medicine
Enhancing Cardiovascular Risk Prediction: Development of an Advanced Xgboost Model with Hospital-Level Random Effects, Dong, T., Oronti, I., Sinha, S., Freitas, A., Zhai, B., Chan, J., Fudulu, D., Caputo, M., Angelini, G. 18 Oct 2024, In: Bioengineering
Performance Drift in Machine Learning Models for Cardiac Surgery Risk Prediction: Retrospective Analysis, Dong, T., Sinha, S., Zhai, B., Fudulu, D., Chan, J., Narayan, P., Judge, A., Caputo, M., Dimagli, A., Benedetto, U., Angelini, G. 12 Jun 2024, In: JMIRx med
Real-World Measures of Cardiorespiratory Function Can Stratify Primary Sjogren’s Syndrome Participants with Persistent Fatigue, Hinchliffe, C., Zhai, B., Macrae, V., Walton, J., Ng, W., Del Din, S. 15 Jul 2024, 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Piscataway, US, IEEE
Cardiac surgery risk prediction using ensemble machine learning to incorporate legacy risk scores: A benchmarking study, Dong, T., Sinha, S., Zhai, B., Fudulu, D., Chan, J., Narayan, P., Judge, A., Caputo, M., Dimagli, A., Benedetto, U., Angelini, G. 2023, In: Digital Health
ConvBoost: Boosting ConvNets for Sensor-based Activity Recognition, Shao, S., Guan, Y., Zhai, B., Missier, P., Plötz, T. 12 Jun 2023, In: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Development and Evaluation of a Natural Language Processing System for Curating a Trans-Thoracic Echocardiogram (TTE) Database, Dong, T., Sunderland, N., Nightingale, A., Fudulu, D., Chan, J., Zhai, B., Freitas, A., Caputo, M., Dimagli, A., Mires, S., Wyatt, M., Benedetto, U., Angelini, G. 10 Nov 2023, In: Bioengineering
Temporal Neighborhood based Self-supervised Pre-training Model for Sleep Stages Classification, Wang, Y., Liang, H., Zhai, B. 26 May 2023, ICBBT '23: Proceedings of the 2023 15th International Conference on Bioinformatics and Biomedical Technology, New York, NY, USA, ACM

Current and Recent Projects

Please visit the Pure Research Information Portal for further information
'Navigating Urban Ecologies: Mapping the Habitation Patterns and Socio-ecological Dynamics of Kittiwakes along the River Tyne, Jin, J. (Principal Investigator), Zhai, B. (Co Investigator), Arts & Humanities Research Council, 01/02/25 - 31/01/26, £102,445.00

PGR Supervision

Sarah Alshahrani AI model for Improved Patients’ Clinical Prediction and Decision Support. Start Date: 20/06/2024

Qualifications

Computer Science PhD

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