Worked in Python (backend) and Typescript (frontend) for development on Benchling apps as a full-stack engineer.

DRI on app canvas effort, building out an interactive canvas composed of UI block (React) components, with extensive API, database, backend, and UI work.

Conducted product demo interviews to prospective engineering candidates.

Worked in Python to build and use T&E pipelines to evaluate vendor models for machine learning applications including Computer Vision, Machine Translation, and Time Series Forecasting.

Developed software tools for applications including computer vision models for AI-assisted labeling, image quality analysis, data augmentation, and data pre and post processing.

Acted as a resource for new hires, with onboarding, mentorship, and technical help.

Worked in Python to develop a framework to incorporate knowledge bases in the form of graph structures to Relation Extraction and Aspect Based Sentiment Analysis tasks. Performed entity linking between datasets and corresponding knowledge bases, constructed n-hop candidate graphs of shared entities between relevant nodes for task, tested various graph-based learning models.

Worked in Matlab to test mantle plume induced subduction as formation model for Venusian coronae. Qualitatively observed features of Venusian coronae using radar data, selected coronae with features most conducive for subduction, flexurally fit coronal trenches to constrain subduction, compared with previous published results.