I was the lead software engineer on a team using Generative AI to build software for the nonprofit sector.

I worked extensively on integrating advanced AI tools, including large-language models, such as ChatGPT and Claude, into applications designed for nonprofits and journalists. One of my key projects involved developing full-stack web applications that incorporated AI to enhance data analysis and decision-making. I operationalized the organization’s experimental LLM development and data management into a robust data infrastructure to build an AI-based product that helps foundation staff to engage with internal documents and publicly-available datasets.

I spent four years building the data engineering capacity within LSC, the largest funder of civil legal aid in the United States.

In my previous position at the Legal Services Corporation (LSC), I led the Civil Court Data Initiative, an effort to collect, curate, and analyze millions of civil court records to inform legal aid providers about legal needs in their local communities to combat issues such as eviction and debt collection. I built LSC’s first in-house cloud-based database to centralize large-scale datasets to improve how LSC supported legal aid providers. I worked closely with legal aid providers and their court partners to design data systems to ensure providers had timely data to address local eviction changes during COVID-19.

Immediately after college, I worked as a policy fellow helping Federal agencies understand and communicate science and technology to Congress and the public.

I conducted mixed methods data collection on research gaps in emerging drinking water contaminants and geothermal energy to inform the development of Federal roadmaps. I also facilitated knowledge-gathering from diverse stakeholders through structured surveys, interviews, and committee meetings to support Federal S&T policymaking.