Nathan Gaw is pursuing a Ph.D. in Industrial Engineering at Arizona State University and does research in machine learning and biomedical informatics at the ASU-Mayo Clinic Imaging Innovation Center. The focus of his dissertation is to develop novel semi-supervised learning models to balance data inclusivity and usability in healthcare applications. Projects Nathan has worked on are summarized below:
• Machine Learning + Mathematical Modelling for cell density map prediction in brain cancer
• A multi-modality imaging-based diagnostic decision support system to handle heterogeneous, high-dimensional imaging features and output interpretable models
• Feature and instance selection semi-supervised learning (s2SSL) for smartphone-based telemonitoring of Parkinson's Disease