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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 also have extensive research experience bringing multimodal fusion techniques and explainable AI into real-world solutions. 

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.

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

Google Scholar: Click here

Bing Zhai

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

Computer Science PhD


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