Education
MS, Tufts University
Computer Science
2018 - 2021, 4.00
Completed Part-Time
BS, Tufts University
Computer Science, Minor in Philosophy
2012 - 2016, 3.87
Summa Cum Laude
Skills Hover bubbles for more info
> 6m
> 1yr
> 2yr
> 4yr
HTML
React
Vue
Create React App
Nuxt.js
Next.js
Bootstrap
CSS
Tailwind
SASS
JavaScript
Node.js
Python
HL7's FHIR
GitHub Pages
Vercel
Docker
CI/CD Automation
Eslint/Prettier
Balsamiq
Experience
MITRE Corporation
Lead Web Dev, 2021 - NowSenior Web Dev, 2018 - 2021Web Dev, 2016 - 2018
Technical lead, developer, intern lead, dept. presentation coordinator
MITRE Corporation
Technical lead, developer, intern lead, dept. presentation coordinator
Lead Web Dev, 2021 - NowSenior Web Dev, 2018 - 2021Web Dev, 2016 - 2018
- Current technical lead & developer on MITRE's oncology moonshot, accelerating cancer data-standards adoption with Open Source applications for data capture, transformation, and visualization
- Prior technical lead & developer for 3+ software teams in charge of Open Source web applications, CLIs, APIs, visualizations, rich-text editors, multipage forms, and more
- Developer for 12+ MITRE projects in oncology, social justice, benefits delivery, and COVID-19
- Runs department presentation series, executed 25+ presentations and project feedback sessions
- Co-leads department internship program, mentoring 12+ undergraduate and graduate interns
Personal Projects
Explore RCV's impact on representativeness
Balsamiq
React
Bootstrap
Python
- Implemented a React web application and Python Flask API for running RCV election simulations
- Designed mockups for defining input parameters and visualizing election outcomes
- Created Flask API for running and aggregating 4 types of RCV simulation
- Visualize outcomes across simulation types, showing RCV's impact on how voters are represented
- Built in collaboration with Moon Duchin and Tufts' Metric Geometry & Gerrymandering Group (MGGG)
Explain frustrating voting patterns with graph theory
React
Bootstrap
SASS
- Designed and implemented a React one-pager using Graph Theory to explore common frustrations in election outcomes
- Leveraged Tournaments and Ranked Voting to discuss Condorcet Winners, candidates who beat all others 1-on-1, and Condorcet Paradoxes, elections where every candidate loses to someone
- Visualized graphs of candidates using react-digraph & election outcomes using HTML tables
Use graph theory & simulations to identify gerrymandering
Python
matplotlib
argparse
- Used markov chain monte carlo simulation to quantify how likely a voting district was gerrymandered
- Visualize frequency plot of simulated graphs' 'eccentricity' using matplotlib, showing outliership