Imaging medulloblastoma tumors presents several challenges, including tissue autofluorescence, low signal-to-noise ratios, and the complexity of distinguishing immune cells within densely packed tumor regions. Additionally, the sparsity of key immune markers across different tumor subtypes complicates accurate cell typing. My project aims to develop a machine learning-based gating algorithm to accurately classify immune cell populations and their spatial relationships and generate high-resolution, spatially informed insights into immune dynamics in medulloblastoma.

Through this SuperUROP, I hope to gain hands-on experience applying machine learning and image processing techniques to biological data, which will complement my lab experience in experimental biology. I’m eager to enhance my understanding of advanced statistical analyses and imaging pipelines, which will be crucial for my long-term interests in disease research and public health.