Data Science at FINRA

I established the first options-based statistical outlier detection framework for deep learning models in the history of the organization. I set goal of engineering a set of features from options data whose statistical outliers correspond to instances of stock price manipulation, queried terabytes of data from multiple securities databases, transformed and combined data into cleansed dataframes of options and stock data. I analyzed big data: Performed statistical analyses and created visualizations of multidimensional slices of the dataframes, including visualizing the evolution of options with respect to expiration dates I collaborated with business partners: Worked closely with business partners to rule out false positives of price manipulation I engineered features: Engineered numerous new features that further quantified the true positive outliers

Machine Learning at Rome Airforce

I worked on the Mathematical Theory for Advances in Machine Learning and Pattern Recognition(click here for more information)