During the summer of 2022, I worked at National Grid as a gas strategy analyst intern. My achievements and experience can be summed up by the following:
  • Utilized Scikit-learn and Pandas to create a time and spatial analysis system which automatically matched projects to infractions, reducing work time from three months to five minutes.
• Successfully geocoded (converted street addresses to lat and long coordinates) 99% of 47,000+ infractions and 99% of 300,000+ projects
• Utilized Bing Maps Javascript API to create a real time interactive visualization of project and infraction data while achieving a stable performance by employing an adaptive clustering algorithm
• Extended the map feature of the open source software “Buddi Base” to dynamically assign colors to leaflet.js map markers

The year prior, when I worked at The Feinstein Institute for Medical Research, my experience went as follows:
• Utilized Python’s Panda library to derive pertinent cell features of a cell from existing ones whilst collecting features from multiple neighboring cells in order to extract regional features.
• Employed several Scikit-learn clustering algorithms (K-means, agglomerative etc.) in order to discover archetypes among cells
• Created a visualization tool using OpenCV and Matplotlib to dynamically display a variable number of cell images which allowed for rapid examination of the clustering algorithms output.

The year 2020 lacks an internship. I was originally accepted into an NSA summer internship program, but the program was cancelled during the covid outbreak.
 
Finally, in the summer of 2019, my experience at The Feinstein Institute for Medical Research went as follows:
• Created a physics engine as an extension of a C++ synthetic data generator utilizing the “liquid fun” particle physics library and successfully enhanced the realism of output
• Implemented a visualization of the physics engine output using HTML,CSS, and Javascript whilst achieving smooth performance by utilizing the canvas element