Olga Khristodulo
Papers
1
Total Citations
6
H-Index
1
About
Olga Khristodulo is a researcher at the intersection of computational archaeology and artificial intelligence, specializing in the application of deep learning and geometric data processing for cultural heritage preservation. Her work focuses on developing novel methods to detect, map, and analyze archaeological sites using dynamic graph convolutional neural networks and iterative closest point algorithms, enabling more accurate and efficient remote sensing of buried structures. Khristodulo’s most cited paper, “Application of Dynamic Graph CNN and FICP for Detection and Research Archaeology Sites” (2024), has garnered 6 citations, reflecting early recognition of her innovative approach to integrating 3D point cloud analysis with machine learning for archaeological prospection. Her contributions are particularly notable for addressing the challenges of fragmented and irregular spatial data common in field archaeology, offering a robust framework for automated site identification. By bridging computer vision and heritage science, Khristodulo is advancing non-invasive survey techniques that promise to transform how researchers locate and document ancient landscapes, making her a rising voice in digital archaeology.
Research Focus
Key Achievements
Top Papers
- 1