Weilin Li, PhD, is a senior data scientist with extensive expertise in policies and programs for young children. Weilin has over a decade of experience working with national, state, and local policymakers and applying research to inform decisions on child care subsidy policies, home-based child care, child care market and sustainability, and professional development of the early childhood workforce. She is also passionate about keeping children safe through AI regulation and risk assessment.

Weilin previously directed a study examining subsidized child care in California; for this project, she developed a clustered and stratified sampling plan to create a representative sample of the different types of care, geographic locations, and population characteristics. She also led the development of a supply and demand mapping tool that helps policymakers, community advocates and leaders, service providers, and others understand who could benefit from home visiting in California by providing county-level information about family characteristics associated with benefiting from home visiting, as well as available home visiting services.

As a data scientist, Weilin stands out for her proficiency in advanced computational methods. She has scraped massive amounts of data from Twitter, Yelp, 2-1-1, and many other websites and processed those data using big data techniques, including Spark and CUDA. In addition, Weilin has analyzed web analytic data and utilized natural language processing (NLP) techniques to help national and state service providers better understand their user behavior—and, as a result, better engage potential users and plan for community outreach. Most recently, she has led a federally funded project utilizing data from crowdsourcing platforms to examine child care market and public perceptions. Weilin has presented at the Open Data Science Conference and was an invited speaker at the Global Data Science Conference.