Molecular phenotypes, or subtypes, can describe cancer as distinct diseases within primary tissues-of-origin. Machine learning (ML) can be applied to this molecular taxon [...]

My research investigates how components of the contact activation system regulate endothelial function. First, I characterized the molecular mechanism of prekallikrein (P [...]

Generalization, the ability of machine learning models to perform well on new data, remains a major challenge in sensitive fields such as medical settings. This work addr [...]

Biological nanoparticles present in bodily fluids carry diagnostically relevant biomarkers with potential for non-invasive disease detection through liquid biopsy. Howeve [...]

This dissertation provides a deep exploration of how mammary epithelial cells respond to microenvironmental signals. Through a progression of increasingly complex models [...]

The musculoskeletal system includes important structures like muscles and bones that help us move. While minor injuries often heal fully, serious damage can lead to long- [...]

Cell-cell co-occurrence patterns are critical in shaping biological and clinical phenotypes, but existing tools for analyzing single-cell RNA sequencing (scRNA-seq) data [...]

This thesis presents a suite of deep learning frameworks designed to address key challenges in the analysis of Electron Microscopy (EM) images of cancer cells, which are [...]

Tumors overexpressing the HER2 oncogene account for around 20% of breast cancer cases and are associated with aggressive disease. These tumors generally originate from a [...]

Triple Negative Breast Cancer (TNBC) is a complex malignancy subtype which commonly forms aggressive tumors with substantial stromal involvement. Unlike the Luminal and H [...]