Dan Runfola

Dan Runfola is a Professor and Chair of the Department of Data Science at William & Mary, where he leads innovative research at the intersection of deep learning and satellite imagery analysis. He directs the geoLab, a research group dedicated to improving human wellbeing through geospatial data science and open-source data dissemination.

His research focuses on leveraging novel neural network architectures to extract meaningful insights from satellite data, with applications in humanitarian aid, development, and global baseline data provision. He collaborates extensively with federal agencies and international NGOs to develop secure, scalable methods for measuring socio-economic conditions across diverse geographical scales.

Dan is deeply committed to mentoring students, supporting both undergraduate and Ph.D. researchers in the geoLab. He actively promotes scientific replicability by contributing to open-source projects like the geoBoundaries dataset, which provides standardized administrative boundary data globally.

He is currently seeking fully funded Ph.D. students with strong Python skills and interests in computer vision and satellite data analysis. His work environment combines academic rigor with practical, real-world impact, fostering innovation in geospatial artificial intelligence.

Dan is also a GitHub Developer Program Member, reflecting his active engagement in software development and computational research. His hands-on approach includes coding, publishing research ideas, and continuously refining methodologies through experimentation.