Writing SPARQL queries is notoriously difficult for non-experts due to its complex syntax and steep learning curve, not to mention the need to understand the underlying graph structure and relationships between entities.

To address this challenge, we developed an interactive dashboard that allows users to interact with the graph using either a predefined set of filters or natural language queries powered by Large Language Models (LLMs).

Corn is one of most widely grown crops in the world, and accurate phenotyping of corn ears is crucial for breeding programs and yield estimation, as well as disease detection and management.

I developed an end to end computer vision pipeline that can automatically detect corn ears from images, count individual kernels, and cluster kernels into spatial rows for structural analysis.

Knowledge graphs (KGs) are widely used to represent complex relationships between entities in various domains, such as healthcare, finance, and e-commerce

Heterogeneous KGs often contains hundreds of classes and thousands of properties, making it challenging for users to understand the structure and content of the graph.

To addres this, we developed a tool to automatically profile KGs and generate comprehensive reports that summarize key statistics and insights about the graph.