MrEric Yang

My research interests lie at the intersection of artificial intelligence, computational genomics, and precision medicine. I am particularly interested in developing advanced machine learning methods to understand complex relationships between genetic variation, environmental factors, and human health.

My key research areas include:

• AI for Genomics: Applying deep learning, transfer learning, and foundation models to large-scale genomic datasets.
• Disease Risk Prediction: Developing genomic prediction models for complex diseases, particularly ischemic stroke, cardiovascular diseases, and metabolic disorders.
• Multimodal Machine Learning: Integrating genomic, clinical, imaging, and environmental data for personalised disease risk assessment.
• Transfer Learning and Generalisation: Improving the robustness and transferability of AI models across populations, datasets, and disease domains.
• Women's Health and Precision Medicine: Exploring AI-driven approaches to understanding menopause-related health changes and predicting long-term disease risks.