Led development of a robotic data analytics portal to enable the exploration and consumption of data generated by the surgical robot. Collaborated frequently and iteratively with internal stakeholders to define desired portal features, including aggregated metric definitions and data visualization requirements. Created performant and scalable analytics pipelines to ingest billions of rows of robotic data using Azure, Databricks, SQL, and Pyspark.Wrangled the extremely dense data set (2000+ columns logging at 1000Hz) into a usable relational data model. Backend and frontend development of self-service dashboards using Databricks and Tableau

Owned development of a Time-Series Data processing git repository which was used to collect, analyze, and report end-to-end robotic performance metrics such as robotic accuracy, latency, and crosstalk. Development included creating algorithms to measure performance, creating robotic trajectories via python, and integrating optical tracking instrumentation. Automated test data analysis by creating signal analysis modules using Python, SciPy, Pandas, and NumPy. Signal processing techniques included: cross-correlations, moving averages, band-pass filters, point-cloud matching, and reference frame transformations.Used statistical methods to quantify data quality and to assess conformance to performance metrics. Statistical methods included: capability analysis, distribution fitting, t-tests, ANOVA, and Gauge R&R.

Acquired by Johnson and Johnson

Analyzed product usage data to inform the new product development of a complex medical laser system.Successfully launched a laser catheter product which had a better than anticipated complaint rate.

Compared the effectiveness of two nutrient synergies on a cancer cell line.Demonstrated proper laboratory procedure in performing cell culturing and experiments including gelatinase-zymography, MTT assays, invasion assays, and an in-vivo experiment with mice.