MACHINE LEARNING, VISUALIZATION, WEB DEVELOPMENT, RESEARCH, PUBLIC GOOD

FOR YEARS I'VE WORKED TO SOLVE SOCIAL ISSUES THROUGH DATA

YOU CAN SEE SOME OF MY WORK HERE AND CONNECT WITH ME HERE

RECENT WORK

U.S. REFUGEE RESETTLEMENT TOOL

Refugees are a vital and important part of the United States. Using the data we have collected at New American Economy on refugees, their countries of origin, and when they arrived in America, I built a data narrative and interactive data visualization tool so that our partners and others can explore the breakdown of refugees resettled in the top 550 cities that have resettled the most refugees since 2002.

Learn more

AT-RISK COMMUNITY BACKLASH PREDICTION

Tensions between native born citizens and immigrants are the highest they have been in the United States in quite some time. To ensure the safety of both citizens and immigrants we are working to predict communities that are most at risk for immigrant backlashes and conflicts. Using the GDELT database and Google Big Query, I have pulled a million articles referencing immigration, scraped the text from each URL, and am building a custom Natural Language Processing sentiment algorithm to quantify conflict. and the probability of a backlash throughout each community in America.

MY MISSION

THROUGH MY SKILLS IN DATA, DESIGN, AND AI, I'M GOING TO CONTINUE TO BUILD CUTTING-EDGE SOLUTIONS THAT BRING THE PRIVATE, PUBLIC, AND GOVERNMENT SECTORS TOGETHER TO SOLVE REAL WORLD PROBLEMS.

SEE THE PATH THAT LED ME HERE

Whether it is as a freelance graphic designer working on marketing and branding material for companies around the world to pay my way through college, or as an entrepreneur trying to unite corporations, non-profits, and people following the "Occupy Wall Street" movement, or as a data scientist trying to solve difficult social issues using data and machine learning, I have had a worthwhile journey and have mastered many useful skills.

VIEW MY RESUME

TWITTER IMMIGRATION SENTIMENT DETECTION

I originally pulled approximately 1 million tweets between the early election cycle in 2016 into about halfway through Trump's presidency in 2018. The goal was to be able to determine how public sentiment has changed on immigration over time and whether or not it is related to large scale events like elections or mass deportations. It became abundantly clear that trying to determine sentiment on a political subject is a difficult task, especially when you are trying to parse whether someone is "for" or "against" a particular political stance. So, I had to create a custom sentiment model by training on about 30,000 tweets we hand labeled at New American Economy.