Wang Hairong

Wang Hairong is a Visiting Lecturer in the Department of Manufacturing & Mechanical Engineering Technology at Texas A&M University, affiliated with the School of Engineering Technology & Industrial Distribution. Their research focuses on integrating domain knowledge into machine learning algorithms for healthcare applications, including cancer diagnosis, Alzheimer’s disease detection, and glioma growth prediction. They hold a B.A. in Mathematics from the University of Oxford (2018).

Key research interests span methodology developments such as self-supervised learning, transfer learning, and uncertainty quantification, alongside applications in clinical decision-making and medical imaging analysis. Notable contributions include knowledge-informed neural networks for glioblastoma assessment and weakly supervised approaches for tumor segmentation.

• Education:
• B.A., Mathematics, University of Oxford (2018)

Recent publications highlight advancements in foundation models for dementia detection, biologically informed deep learning for tumor heterogeneity analysis, and optimization algorithms for healthcare data. Wang Hairong has received prestigious awards including the 2024 Student Data Analytics Competition Winner and the 2023 Best Paper Award at INFORMS DMDA Workshop.

Current work emphasizes precision medicine through data-inclusive machine learning and hybrid ordinal models for healthcare outcomes. Active collaborations involve multidisciplinary teams focusing on translational AI solutions for clinical challenges.