Constance Delannoy’s expertise lies in developing and deploying data science models for federal, state, and nongovernment clients. For example, in 2020, she and her collaborators used cloud-based modeling and local expertise to assess households’ most pressing hardships in five countries in Africa. She also led a team to develop a tool to gauge bias in algorithms, and she has conducted predictive analyses related to quality measurement in the health care sector, most recently for the Center for Medicare and Medicaid Innovation.
Delannoy received her master of science in applied mathematics from the University of Colorado, Boulder, with a thesis focused on using neural networks to detect rare events with spatial data. She received her bachelor of arts in international studies from the University of Chicago; in her thesis, she analyzed survey data related to women’s financial empowerment in Uganda.
Taking a look at initial survey data from five African countries reveals interesting insights regarding how COVID-19 is shaping access to services and mobility restrictions, coping mechanisms families are using, and early impacts on food security and income.
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