Projects

As a Scientist on the Insights team, I develop and scale sophisticated analytical solutions for client projects in spatial biology. I built a random forest classifier that combines Haralick texture analysis with user-provided labels to automatically identify cell types, generating precise cell type annotations and streamlining the analysis of functional markers. Working with our internal platform and utilizing both R and Python, I analyze complex imaging data and collaborate with users to optimize their research workflows.

During my PhD, I developed two complementary innovations in spatial transcriptomics. On the computational side, I created a machine learning approach that identifies minimal panels of RNA markers capable of distinguishing all cell types in single-cell RNA sequencing data. On the molecular side, I developed a method to covalently modify RNA so they become anchored in a durable hydrogel, enabling multiplexed visualization while preserving cellular morphology in 3D. By combining these approaches, researchers can affordably map sequencing data into intact tissue with single-cell resolution, as I demonstrated using the Tabula Muris lung dataset.

As a research technician, I engineered novel fluorescent biosensors for studying brain metabolism, focusing on developing a generalizable scaffold based on dimerization-dependent fluorescence. I initiated and led a collaborative project with Harvard's Weitz lab to implement microfluidic screening approaches, which advanced the project's scope and resulted in a peer-reviewed publication.