Stroke is the fourth leading cause of death in the UK and single largest cause of complex disability in adults, with annual costs projected to rise to £75bn within 20 years. 100,000 people have strokes each year in the UK. The NHS England (NHSE) Long Term Plan is committed to saving 150,000 lives from cardiovascular disease over the next 10 years. Stroke survivors, clinicians, and policymakers have consistently called for better quality data on the long-term consequences of stroke, to better inform their clinical decision making.

We are commited to unlocking the potential of data - reshaping health and care for stroke survivors with data. Providing data-driven solutions for stroke patients, health providers, researchers and policymakers, with an improved understanding of patient-centred health data and the use of cut-edging data analytics such as statistics, data mining, and AI technologies. More info is at: http://strokecare.uk.

"A healthy person has a thousand wishes, a sick man has but one - Berger R, 1986"

The Paediatric patient is a special cohort of patients with a wide age range from birth through to aged 16. They need careful care and specialized treatment and intervention due to their complexity. Due to developmental disease, some children need to be referred and transported to receive a higher level of care in a children's hospital. During transport, patients are likely to experience health deterioration and poor outcomes. Our work aims to early detect patients who have a deteriorating health condition in a proactive fashion using continuously monitored patient vital signs and advanced artificial intelligence. More importantly, this will help dedicated intensivists make better decision-making whilst the whole course of transport, medication and intervention to improve the health care and outcomes of patients.

Fault diagnosis of the multistage centrifuge air pump is of great significance to ensure a reliable and safe environment for the indsutry. This project aims to monitor and diagnose the health condition of the industrial-level equipment so that incipient faults can be identified and troubleshoot as early as possible. My role is to design and deploy condition monitoring system on the machinery. Develop data-driven fault diagnosis methods (i.e., data processing, data analysis, and decision making) to identify potential faults and distinguish between various machine health states. Involve project planning and technology choosing.

Noise map is a 4-year research project funded by Guangdong Province, China. It aims to develop a new crowdsensing-based platform that allows users to measure sensor data using smartphones and upload data to the cloud. Through data analysis, processed data information can be used to visualize the average noise pollution levels in a given area.

My role is to design and develop the noise mapping system, including Android client, Web server, and Web client for data collection, upload, and analysis. Also, I developed some algorithms for assessing the quality of sensor data uploaded by various users, such as inferring the user context about where, when and how sensor data is measured. One of the algorithms has been licensed as a Chinese invention patent.

Industrial-level middleware platform project aims to solve the heterogeneous problem in sensor networks and integrate different sensor systems using unified semantic interpretation and user interfaces.

My role is to design and develop a cloud-based data-intensive framework for fault diagnosis of industrial machinery systems in petrochemical plants. The system incorporates three levels: sensing system level, data storage and processing level, and publication-subscription based user interface level.