Wenrui FanView profile
Researcher
Wenrui Fan serves as an AI Research Engineer (AIRE) and PhD Student at the University of Sheffield under Professor Haiping Lu, actively contributing to the AIRE Group's mission of advancing multimodal artificial intelligence. His technical expertise spans deep learning algorithm development and industrial anomaly detection systems. His educational background includes: MSc in Robotics, University of Sheffield, UK (2022) BSc in Aerospace Engineering, Beijing Institute of Technology, China (2021) Fan's research integrates computer vision, meta-learning, and causal inference to solve critical medical challenges. His work emphasizes multimodal fusion for low-cost diagnostics, particularly in cardiac and pulmonary disease prediction. He develops interpretable models that bridge clinical features with AI outputs, enabling practical healthcare applications through frameworks like MeDSLIP and CardioVAE. His publication trajectory reveals a strategic shift from robotics (2021) to medical AI (2024), where he pioneers fine-grained vision-language alignment and cost-effective multimodal diagnostics using accessible data streams like chest X-rays and ECGs. This evolution demonstrates growing sophistication in handling medical domain constraints while maintaining clinical relevance. As a core contributor to PyKale and the AIRE Group, Fan collaborates extensively with medical researchers including Samer Alabed and Chen Chen. His current work focuses on scaling multimodal architectures for Parkinson's disease analysis and cardiothoracic instability prediction, with strong emphasis on model interpretability for clinical adoption.
