Research

Beyond Earth Science: Foundational AI

We develop AI and computer vision methods to extract knowledge and information from a range of different sources.

Back to Research

From fundamental AI to applications

Daqian Shi, a postdoc in the research group, has worked on novel methods to train AI models and on ancient character recognition
Daqian Shi, a postdoc in the research group, has worked on novel methods to train AI models and on ancient character recognition

Computer vision is a versatile tool, and some of our work expends beyond the confined of Earth Sciences. For instance, we develop novel ways to train machine learning model using knowledge distillation, or look at vision-language models and leverage semi-supervised learning to uncover the meaning of ancient written characters. And an image being an image, we can use our toolkit to solve problems in medical imaging, and beyond.

The point is that research is interdisciplinary in this day and age, and we welcome collaboration with fundamental AI teams, or applied group. This is why our research group initiated and lead the "Scale-Invariant Spatial Intelligence Hub [SI2-HUB]: Cross-Scale, Multimodal and Physics Aware Computer Vision Across Disciplines" team at the Huang Collaboration and Innovation Complex at OSU.