I am a Lecturer in Computer Science. My research interests are machine learning and its various applications such as behaviour analysis, wearable and ubiquitous computing, etc.

My research agenda is to develop practical AI tools for solving the challenges of real-world applications. 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. I have extensive experience working with time-series data, such as biosignals, which have broad applications in physical behaviour assessment, health, and well-being monitoring, etc. I am also developed mechanisms for increasing the transparency/interpretability of complex AI models, which is crucial for many applications such as automated medical diagnosis.

Area of expertise: Machine Learning, Activity Recognition, Automated Health Assessment, Wearable/Ubiquitous Computing.

Google Scholar: Click here

I am a Lecturer in Computer Science. My research interests are machine learning and its various applications such as behaviour analysis, wearable and ubiquitous computing, etc.

My research agenda is to develop practical AI tools for solving the challenges of real-world applications. 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. I have extensive experience working with time-series data, such as biosignals, which have broad applications in physical behaviour assessment, health, and well-being monitoring, etc. I am also developed mechanisms for increasing the transparency/interpretability of complex AI models, which is crucial for many applications such as automated medical diagnosis.

Area of expertise: Machine Learning, Activity Recognition, Automated Health Assessment, Wearable/Ubiquitous Computing.

Google Scholar: Click here

• 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
• 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. 30 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

Computer Science PhD

• Please visit the Pure Research Information Portal for further information
• My past research projects can be seen