We are broadly interested in further improving the capabilities of foundation models (FMs) for a range of data management tasks such as data discovery, metadata enrichment, data access and retrieval with querying, and automated data-driven insights.
Topics of interest include research on interactive orchestration of data workflows such as natural language to data insights spanning multiple tools and functions, knowledge-driven data discovery and querying with graphs and mutli-modal FMs, step-by-step planning and reasoning for complex data workflows , and low-computational cost inference techniques for FMs to efficiently automate or assist users with data tasks.
We are looking for interns with skills and tasks of interest include:
- [LLM for code generation] Research for effective use of foundational models for code generation pipelines specific to data tasks such as SQL for data retrieval
- [Agents and Reasoning] Research for developing novel autonomous agentic systems to compete with Text-to-SQL on public leaderboards like BIRD and Spider 2.0
- [Knowledge Graphs, Multi-Modal FMs] Research for novel ways to combine foundational models, knowledge graphs, and multi-modal data for improving tasks such as data discovery and automated text-to-sql
- [FM Inference] Research for improving foundation models inference in terms of both answer generation and computational cost.
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