Dr Bing Zhai

Lecturer

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/

Google Scholar: Click here

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Key Publications

Please visit the Pure Research Information Portal for further information
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
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
Ubi-SleepNet: Advanced Multimodal Fusion Techniques for Three-stage Sleep Classification Using Ubiquitous Sensing, Zhai, B., Guan, Y., Catt, M., Ploetz, T. 27 Dec 2021, In: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Making sense of sleep: Multimodal sleep stage classification in a large, diverse population using movement and cardiac sensing, Zhai, B. 15 Jun 2020
The future of sleep health: a data-driven revolution in sleep science and medicine, Zhai, B. 23 Mar 2020

PGR Supervision

Sarah Alshahrani Creation of RepresentativeSynthetic Behavior-Based and Movement-Based Data for Various Health Applications Start Date: 20/06/2024

Qualifications

Computer Science PhD

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