Graduate mentor's supervisor: Grant Weddell
Research Problem:
Data lakes contain massive amounts of structured tables and unstructured images. Currently, database systems cannot connect them automatically; a table of store products and a folder of product images remain completely isolated. This research bridges that gap using vision-language AI models to map text, data columns, and images into a shared space. Solving this unlocks "dark data," allowing organizations to fully search, analyze, and use all their visual and text data together for the first time.
Importance:
Instead of rigid, exact matches (like matching ID numbers), you will build a Probabilistic Entity-Relationship Diagram (pERD). This AI-driven system connects tables and images based on concepts and visual similarity (A connects to B with 85% confidence). It’s an exciting mix of databases and modern multimodal AI!
Working in a team of 3–4, you will split up tasks like:
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Short-term: Use pre-trained AI models to generate embeddings for a dataset of tables and images.
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Medium-term: Build a graph structure to map and visualize these probabilistic AI links.
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Longer-term: Create a simple interface to let users review and refine the AI's connections.