Frequently Asked Questions

We are an interdisciplinary research group that fuses AI with Earth Observation data to study global development. Our work spans poverty mapping, conflict analysis, and sustainability measurement. We operate across three institutions: UT Austin, Linköping/Chalmers, and Harvard.

While many groups use satellite imagery for prediction, we focus on causal inference—understanding not just what is happening, but why. We develop methods for planetary-scale causal analysis that help identify what drives change and what interventions work.

Our satellite imagery analysis extends back to 1984, leveraging the Landsat archive. This allows us to study long-term trends in poverty, conflict, and environmental change—not just current conditions.

Yes, our datasets are available for academic research. Some are openly accessible, while others require a data use agreement. Visit our Data page for details on each dataset and how to request access.

Each software package includes citation information in its README file and documentation. Please cite both the software and the relevant methodological paper. Links to both are provided on the Code page.

We provide documentation and respond to GitHub issues. For complex questions about methodology or implementation, contact us directly. We also welcome contributions and bug reports from the community.

We welcome collaborations that extend our methods to new domains or provide access to novel data sources. If you see potential for joint work, email us with a brief description of your research and the collaboration you have in mind.

Yes, we collaborate with international organizations, NGOs, and government agencies on applied research projects. Our work is designed to be policy-relevant while maintaining academic rigor.

Apply through the graduate programs at UT Austin (Government), Linköping (IAS), or Harvard (Statistics). Mention your interest in the AI & Global Development Lab in your application materials. See our Work With Us page for links.

We value strong methodological training in statistics, machine learning, or econometrics, combined with genuine interest in global development applications. Experience with satellite imagery or survey data is helpful but not required.

Yes, we hire undergraduate and graduate research assistants on a project basis. Email us with your CV and a brief statement of your interests. RA positions typically involve data processing, software development, or literature reviews.