Extended open-source agent-based modeling & simulation software in C++ to add capability for tracking cell lineages; designed data processing & analysis pipelines in Python for downstream analysis of simulations.

Developed high-performance multiscreen workflow in Bash and Python to process large-scale genomic data in parallel; contributed extensively to open-source R package microshades; managed cloud-based software environments.

Assisted in the development of semi-supervised machine learning models in Python to predict chemical reaction rate constants for applications in environmental chemistry.

(Summer 2022 & Summer 2023)

Analyzed and benchmarked statistical models for bias correction in high-throughput next-generation sequencing data of low-biomass microbiomes to improve data accuracy and clinical interpretability.