We analyze human mobility through mobile phone calling patterns, disease incidence reports, and other epidemiological factors to predict the spread of infectious diseases. We enable public health officials to effectively plan disease prevention and measure the impact of their interventions.

We combine mobile phone data and satellite imagery that identify potential early warning signs of food insecurity with a high degree of accuracy through a machine learning approach. We also develop recommender systems to smallholder farmers to provide the right information to the right person at the right moment.

We leverage mobile phone data in our data products to monitor real-time usage and penetration of mobile money services. By tracking the reach, depth, and impact of financial inclusion initiatives, we assist our partners in prioritizing the most crucial interventions.

We can predict with over 85% accuracy the gender and economic empowerment indicators of a mobile phone user based on their call behavior patterns. Tracking gender indicators and defining accurate gender labels with machine learning help local organizations assess the gender data gap across multiple dimensions and design products and services targeted at women's specific use patterns and needs.

We identify the most vulnerable populations and estimate multi-dimensional individual and aggregated poverty indices to offer actionable insights on the economic recovery of a population to relevant stakeholders. Such an approach can be embedded into a financial inclusion or gender framework.

We build data infrastructures to collect granular information on the solar energy market and mobile money data for actionable data on investment, interventions, advocacy, and policy making on renewable energy solutions and financial inclusion interventions.