Wenhao Zhang

Dr. Wenhao Zhang serves as Associate Professor of Computer Vision and Machine Learning at the University of the West of England's School of Engineering, where he co-directs the Centre for Machine Vision at Bristol Robotics Laboratory. His academic foundation includes a BEng in Automation, MEng in Control, MSc in Electronics, and PhD in 3D computer vision analysis of human demographics and behaviours.

His research bridges computer vision and machine learning with critical applications in Agri-tech and Health-tech, specializing in face recognition, 3D imaging, eye tracking, and human-computer interaction. Current projects address dementia diagnosis through eye movement analysis, livestock lameness detection, and renal fibrosis assessment using deep learning techniques. His work consistently translates theoretical advances into practical solutions for agricultural and medical challenges.

Analysis of his publication record reveals a strong interdisciplinary trajectory, with recent work focusing on convolutional neural networks for medical diagnostics and agricultural automation. The majority of his output combines computer vision fundamentals with domain-specific implementations, particularly in healthcare monitoring and precision farming systems.

Dr. Zhang has received notable recognition including the UWE 30 to watch award and Fellow of the Higher Education Academy status. His contributions extend to curriculum development through leadership of the Machine Vision and Advanced AI, Computer Vision, and Cyber Security modules.

As an active supervisor, he guides PhD research in 3D object detection, document analysis, and neurodegeneration assessment. His grant portfolio features:

• Eye tracking for dementia diagnosis (BRACE, 2023-2025)
• Hoofcount Vision Detection for dairy cattle (Innovate UK, 2022-2024)
• Deep Learning for Renal Fibrosis Assessment (Royal Society, 2021-2024)
• Pig feeding behaviour monitoring (BBSRC/GCRF, 2020-2021)

Through the Centre for Machine Vision, Dr. Zhang fosters industry-academic collaborations that advance robotics applications in healthcare diagnostics and sustainable agriculture, with ongoing projects targeting real-time vision systems for complex environmental monitoring.