Led a project that analyzed survey data using machine learning techniques to identify key insights and trends in the customer satisfaction index. Preprocessed data, performed sentiment analysis on customer comments, and generated visualizations and reports to communicate findings to executives.Managed multiple projects to quickly identify and resolve on ‑ board Wi ‑ Fi troubleshoot technical problems.

Conducted extensive biostatistical research on clinical outcomes (e.g., mortality, hospital readmission) in a real‐world dataset with 1.9 million Deep Vein Thrombosis (DVT) encounters.Led a project that identified Inari’s DVT removal device as associated with a statistically lower mortality rate, lower occurrence of patients needing potentially harmful thrombolytic drugs, and a lower post‐index ICU stay when compared to competing devices, using real‐world data.Presented project findings to department management and contributed to the development of a white paper summarizing research results.Executed a range of biostatistics tasks, from data wrangling to regression modeling in R and Python, to summarizing research findings in presentations to the department management.

• Authored: Tataru C., Eaton A., David M. GMEmbeddings: an R package to apply embedding tech- niques to microbiome data. (submitted)• Filtered and processed large amounts of genetic sequencing data from gut microbes collected from over 15,000 people. Applied word embedding algorithms (GloVe) and PCA transformation to ASV data in order to determine which microbes co-occur in the human gut together.• Developed an open-source R package for utilization of embeddings in order to determine which microbes have the potential to interact synergistically. Package transforms 16s microbiome data into an embedding space that captures information about taxa co-occurrence or co-abundance patterns.

Worked as a union laborer on a concrete crew constructing "Serena," the new Merchandising building at the Nike World Headquarters.