Madeline Lisaius is the Lead Data Scientist and Tom Ford Fellow on the Data Science team. She works on data science projects that advance the Rockefeller Foundation mission, particularly in the areas of food, climate and gender. She is passionate about the power of spatial analysis to reveal insights that can guide targeted approaches to, and execution of, robust and ethical work to support the most vulnerable populations around the globe.
Prior to joining The Rockefeller Foundation, Madeline worked with the Waorani Women’s Association of the Ecuadorian Amazon (AMWAE) on a sustainable cacao agroforestry program and later to map forest degradation with both satellite and anthropological methods as a National Geographic Young Explorer. As part of the Lobell Group at Stanford, Madeline used satellite imagery to identify crop varieties and estimate yields. She has also worked across Latin America and parts of South Asia, Polynesia, and East Africa. By training, Madeline is a data scientist and environmental scientist and holds both a B.S. with honors and M.S. from Stanford University in Earth Systems with a focus on land systems, spatial statistics, and computer science.
Jul 14 2020 Blog Post Announcing the Lacuna Fund: Closing Data Gaps to Enable Equitable Machine Learning Over a decade into the AI revolution bias remains a pernicious problem. From medical recommendation systems that allocate less care to Black people to human resource algorithms that are biased against women it can often feel like we’re trapped in a cycle of hype then harm. As scientists scramble to build machine learning tools to […]
Dec 02 2019 Blog Post AI Solutions Sourced Locally: Meet the Deep Learning Indaba Attendees Despite their exclusion from mainstream conferences and gatherings, artificial intelligence and machine learning communities in Africa are growing and focusing on some of the continent’s most pressing issues. In August 2019, 700 of Africa’s best machine learning researchers came together at Kenyatta University in Nairobi, Kenya for the third annual Deep Learning. It was […]