This research uses computational photography and machine learning to monitor electricity quality through the flickering patterns of everyday lights. By analyzing images captured in cities such as Kampala and Nairobi, the work offers a low-cost method for measuring voltage instability and improving power-grid planning in underserved communities lacking reliable electricity infrastructure.

This research uses low-cost air quality sensors to monitor pollution exposure in underserved communities in Philadelphia. It reveals unexpected indoor and temporal pollution patterns and highlights disparities in exposure. By involving residents as citizen scientists, the study demonstrates how accessible data can inform policy and improve public health outcomes.