We work on projects in these three areas of research:

Understanding Behaviors during Social Shocks.

Natural disasters, economic crises or violent events are some of the shocks that affect many communities every year. Under such stresses, communities are forced to change their routine behaviors, and adapt to the new conditions. Our work in this area focuses on modeling patterns of displacements, resilience and communication during social shocks using digital sources of human behavior such as cell phone data or social media. The aim of this research area is to help decision makers in the design of prevention and response policies.

Funding sources: NSF RIPS and NSF CAREER

Smart Transportation.

According to the United Nations, 68% of the population worldwide is projected to live in urban areas, cities and megacities by 2050. As cities become more densely populated, decision makers face challenges in understanding the complex ways in which residents interact with the city in general, and with the transportation infrastructure in particular. Having that knowledge would allow policymakers in transportation to understand how to improve the infrastructure and public spaces as well as to identify novel types of services. Our work in this area uses data from ubiquitous technologies -such as cell phones or GPS trackers- to characterize urban dynamics and the role that various social and built-environment features play in people's mobility experiences. Some projects in this area include modeling commuting patterns or understanding cycling safety.

Funding sources: NSF CNS and MTI

Socio-Economic Development.

We also have extensive experience in the use of human behavioral insights -extracted from cell phone metadata- to inform decision making processes that promote development in low-income countries and low-income communities. For example, we have worked on the automatic computation of socio-economic maps, which addresses the urgent need that many low-income countries have to access, in an affordable manner, up-to-date socioeconomic data that can help policymakers in the design and implementation of more effective interventions. We are also working on the development of smart city solutions that support the needs or low-income communities in Baltimore.

Funding sources: NSF SCC and The World Bank