I am a master's student at the University of British Columbia (UBC) working on applications of deep learning in medicine. Specifically, I am developing novel techniques to make deep learning models more generalizable on pathology images and improve the process of cancer diagnosis. My research interests lie in deep learning, computer vision, and medical images.
Previously, I helped develop a medical device prototype for treating Peyronie's disease (patent application in progress) and worked in pharmaceutical research and production after my bachelor's degree in Chemical and Biological Engineering at UBC.
You can learn more about my career history on my resume.
In my first, first-author paper, we developed a strategy that makes diagnosing digital pathology images with machine learning more generalizable, even when data is sourced from multiple centers.
I am the chief author and editor of a publication on Medium dedicated to short articles summarizing important concepts and papers related to machine learning.
I created a 3-minute video communicating a high-level overview of my research into color normalization for improving deep-learning based classification of histopathology images, which was featured at UBC's Biomedical Imaging and Artificial Intelligence Fall Research Showcase 2021.
I participated in a Kaggle competition involving predicting whether or not a glioblastoma patient has a certain genetic sequence in their tumor based on 3D MRI DICOM brain scans. This involved working with MRI data (like writing functions to resample data from the sagittal or coronal plane to the axial plane) and creating custom PyTorch models.