Min Jing

Dr. Min Jing is a Research Fellow at Ulster University's School of Engineering within the Faculty of Computing, Engineering and the Built Environment. Her research focuses on medical image analysis and computer vision applications in healthcare, with particular expertise in cardiovascular diagnostics and neurodegenerative disorders. She maintains active collaborations with healthcare professionals across the UK, USA, Ireland, Germany, and Italy.

Her academic background includes:

• MSc in Digital Signal Processing with distinction from King's College London
• PhD in Electronic Engineering from Cardiff University, researching epileptic seizure predictability via EEG and fMRI fusion

Dr. Jing's research spans multiple cutting-edge areas including Diffusion Tensor Imaging for neurodegeneration, surveillance video retrieval systems, and social media image/video analysis for emergency management. During the COVID-19 pandemic, she contributed to infectious disease modeling and user experience analysis for antibody rapid tests. Her current work focuses on ultrasound image analysis with deep learning for cardiovascular healthcare and applying Large Language Models for risk register data analysis in medical contexts.

Analysis of her recent publications reveals a clear trend toward cardiovascular imaging analysis, particularly using ultrasound techniques to assess carotid intima-media thickness as a biomarker for coronary artery disease. She has pioneered methods combining conjunctival microvascular analysis with traditional biomarkers for improved cardiovascular screening. Her work increasingly integrates deep learning and recurrent neural networks to extract temporal features from medical imaging data, demonstrating the growing importance of artificial intelligence in medical diagnostics.

Dr. Jing has secured research funding from prestigious organizations including the European Commission FP7, Invest NI, DEL NI, and EU's INTERREG VA Programme. Her scholarly output includes 50 research publications with 183 citations and an h-index of 8 according to Scopus data, demonstrating consistent research productivity from 2005 through 2024.