Listing of Professional Work

  • Using XGBoost to do predict power outages for customers.
  • On the grid there exists a vast amount of tabular data, that once cleaned and prepped can provide insight into determining if a true power outage has occurred and to send a resource out to fix it.

  • Analyzing multivariate time-series data to make a predictive model to detect single phase open neutrals on the grid
  • Without a reference to ground, determining a single phase open neutral condition is a fairly difficult task. For this project we produced experimental data in a lab to create a model that was then tuned and trained for live data from the grid.

  • Creating a AI ticket response system and pipeline that can accurately determine the correct resource to send for a given outage.
  • A complete end-to end pipeline that monitors incoming tickets, gathers all relevant data, and assigns the correct resource to fix the issue. This project was funded through a research grant earned through competing in a company wide competition.

  • Monitoring algorithm for determining trending events on the grid.
  • Algorithm that tracks all events on the grid and determines if there is an uptrend in some event class.

  • Filter system designed to remove all upstream noise for a given event on the grid.
  • On the grid, due to all the interdependencies of systems, noise generated from an unrelated event can produce a positive signal for a model. Due to this, a robust filter system must be made pulling and linking all data from all systems to ensure events are clean from extraneous noise.

    Listing of Personal Work

  • Threat Detection and Monitoring
  • Created a theoretical framework for detecting and monitoring threat in real time using an ensamble of AI models. An overview of this framework can be read here.

  • RoBERTa as a question and answer model to find the phrase that most accurately produces the target sentiment
  • Using RoBERTa I was able to produce a question and answer system that was able to find the indexes tagging the part of phrase that most accurately represented the predicted sentiment with a fairly high confidence. This project took advantage of PyTorch and TPUs to produce a quickly trained, complex, and robust model.

  • Reddit /r/WatchExchange Bot
  • Built a serverless AWS application that uses the reddit API to scan for keywords in /r/WatchExchange. The application then sends me a text if a keyword is found in a post that I have not seen before. This application uses dynamoDB as the backend database to store the unique ids of posts that it has already parsed. The full program and architechture can be found on by github linked here.