Data science/ML leader and problem solver bridging the worlds of AI, data, and biotechnology by leveraging machine learning tools honed over a foundation of over 20 years experience running biological, genetic, and synthetic biology research projects. Proven abilities in deep learning, handling massive datasets, project leadership, people management, organization, and dissemination of results.

I pride myself on being analytical, inventive, resourceful, detail-oriented, and easy to work with.

My varied interests intersect with the fields of biotechnology, medtech, genomics, public health, sustainability, new materials, and energy.

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Data Science Project Portfolio

1. Beating the Scam: Identifying Medicare Fraud Through Machine Learning - March 2021 - Article - Repository

   • Developed a framework using machine learning to accurately detect fraudulent Medicare claims
   • Demonstrated the utility of the model using actual cost savings metrics

2. A Thirty Thousand Foot View of Air Travel Post-COVID - February 2021 - Article - Repository

   • Analyzed airline routes and predicted travel trend changes to occur post-COVID
   • Identified cities and airports that are undervalued in the old business model where an increased presence can yield returns
   • Leveraged growing vacation travel demand to gain market share and provide a counter-cyclic profit driver

3. Predicting House Prices by the Application of Machine Learning Models - January 2021 - Article - Repository

   • Implemented multiple supervised machine learning models to develop a house price predictor

4. Fish Picks: An Exploration of Seafood Watch Data - November 2020 - Article - Repository

   • Extracted data from Monterey Bay Aquarium's Seafood Watch website to make it more user-friendly for consumers and businesses
   • Issued recommendations as to the most and least sustainable seafood choices

5. Towards a Solar Energy Future - October 2020 - Article - Repository

   • Interactive online data visualization to demonstrate the potential of solar energy use in Madrid as a means to decrease dependence on fossil fuels
   • Extrapolated findings to predict electricity generation from widespread solar photovoltaic installation in the residential sector