Educator, advocate, and media-maker turned data scientist. Areas of focus have included violence intervention and prevention, critical media literacy, racial and gender equity, mental health and wellness, substance misuse prevention, and positive youth development.
I find joy in the crossovers of data, storytelling, and art, and see data science as a tool for more informed and effective problem-solving, strategizing, interventions, and evaluation.
Interested in working together? Get in touch!
Seeking / Others interested in data ethics, equity, and emergent strategy
Doing / Working on the Code for Boston Clean Slate Project
Learning / Database Design and Implementation for Business, Boston University
Reading / My Riot: Agnostic Front, Grit, Guts & Glory by Roger Miret
Last updated 10/31/19. See my previous activities.
Technical / Python - Data Wrangling - Web Scraping - Data Analysis - Data Visualization - Machine Learning - Natural Language Processing - Tableau - SQL - HTML/CSS - Git/GitHub
Additional / Project Management - Participatory & Human-Centered Design - Advocacy - Curriculum Development - Facilitation - Research - Graphic Design
Reflections on the one year anniversary of starting the program
Understanding the data means learning from unexpected places
Connecting the dots following the release of GPT-2 and the Digital Defense Playbook
From the perspective of a small zine distro
Identifying predictors of an active timebank using linear regression
Using natural language processing to identify articles
Thoughts on appropriation and AI
How art and wanting to change the world led me to data science
What are the best predictors of an active timebank?
Summary / Timebanking is a time-based currency that can help individual and community needs be met without relying on money, markets, or the state. The goal of this project is to build a regression model to identify the best predictors of the average number of daily exchanges in timebanks on TimeBanks.org.
Technologies Used / Python, Beautiful Soup, datetime, Matplotlib, NumPy, Pandas, PyLab, regex, Requests, SciPy, Seaborn, scikit-learn, VADER Sentiment Intensity Analyser
Classifying articles from r/TheOnion and r/nottheonion.
Summary / While "fake news" has become a hot topic in recent years, fake news is nothing new. The subreddits r/nottheonion and r/AteTheOnion point both to the interest in "strange but true" news and the challenge of separating fact from fiction. This project uses natural language processing to predict whether an article is from r/TheOnion (fake news) or from r/nottheonion (real news).
Technologies Used / Python, datetime, Matplotlib, Natural Language Toolkit, NumPy, Pandas, PIL, regex, Requests, Seaborn, scikit-learn, unidecode, WordCloud
Using Twitter to detect power outages.
Summary / The goal of this collaborative project is to utilize news feeds and/or posts on social media to identify "hot spots" of concern and areas suffering from power outages for a nonprofit client. Following an event, the tool will scan relevant news or social media websites to identify localities likely to suffer from power outage.
Technologies Used / Python, Bokeh, collections, datetime, Matplotlib, Natural Language Toolkit, NumPy, Pandas, regex, scikit-learn, Tweepy, Twitterscraper
Volunteer / Code for Boston, Safe Drinking Water & Clean Slate Projects / Dec 2018 - Present
Contributing Writer / Towards Data Science magazine / Nov 2018 - Present
Cohort Member / Mel King Institute Certificate Program / Oct 2017 - Present
Volunteer / Open Data Science Conference East at Hynes Convention Center, Boston, MA / Apr - May 2019
Volunteer / Data for Black Lives II conference at MIT Media Lab, Cambridge, MA / Jan 2019
Presenter / "Infrastructuring Civic Action: Social Network Participatory Design with Youth" workshop, Digital Media & Learning Conference, Los Angeles, CA / Jun 2015