Skilled in the analysis and evaluation of data privacy-preserving algorithms performance over large governmental and industrial datasets (Differential Privacy, Noise-resistant Decision Metrics), assessing data quality, standardizing quality checks, peer reviewing data and algorithms. Utilize machine learning for a privacy creating software.Act as a liaison to the US Census Bureau for training and testing.Utilize Python, Pandas and Jupyter Notebooks for data munging, data mining, and data visualization across a variety of datasets.Support research analysis and prototyping of candidate privacy-preserving synthetic data generation algorithms, and software engineering for algorithm 40 + versions of evaluation frameworks.Providing technical support and diagnostics for the synthetic data project, by writing user guides + updates, documentation, and presentations.Utilize machine learning techniques applied to statistical edit & imputation, classification & codification to create privacy-preserving synthetic public utility microdata, validate the data, and evaluate it.Execute pre-designed experiment plans, record and analyze results.Perform integration, stress, and performance testing for code quality accuracy reviews, and debugging on over 20 versions of the privacy preserving framework.Explore and assess data quality in Pandas to clean, get summary statistics, and visualize correlations and distributions in order to generate actionable insights for processing data in the privacy preserving frameworkAgile team.NIST Differential Privacy Temporal Map Challenge In a group of NIST experts that coached teams to improve the robustness and documentation of their code, creating easy-to-use implementations of sophisticated differential privacy algorithms. The goal was to create a privacy-preserving dashboard map that shows changes across different map segments over time. Did analysis and visualizations using R - SynthPop and regressions in Pandas for each submission.

As a workshop instructor, I teach the introductory aspects of data science. Topics include python fundamentals, visualization, and introductory machine learning focusing on predictive models using decision trees, random forests, linear regression, and success metrics.