I earned a degree in Music at Princeton in 2018 and soon after discovered programming through data journalism. For two years, I worked for electric utilities, buiding models that increase efficiency on the grid; then for two years, I returned to Princeton as a programmer on staff, writing software for professors and students. I moved to Boston in 2022 and now work at AWS, helping build a cloud-based file system called FSx.
Partnered with rural utilities, national labs, and the Department of Energy, I studied how deep learning can be used to predict energy consumption. Increasingly accurate energy predictions can help utilities save a lot of money through a process called "peak shaving." Read about my deep learning research.
After utilities showed more interest in my research, I partnered with Burt County Public Power and NRECA to build a web app that automatically draws in weather data and load data every day to generate a forecast. Check out the product we built.
- kmcelwee/fortune-100-blm-report: an analysis of the Fortune 100's response on Twitter to the BLM protests in the summer of 2020
- kmcelwee/WhoPaysWriters: an analysis of the website WhoPaysWriters (read the article in the Columbia Journalism Review)
- kmcelwee/scrabble: an analysis of what Scrabble tile values make the game more fair (read the article in Nautilus)
- Read more on my website...
- kmcelwee/reddit-says-aww: A bot that posts the top-rated content from the r/aww subreddit to Twitter using GitHub Actions
- kmcelwee/mondrianify-twitter: A wrapper for my mondrianify pipeline.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent