Liana Patel

I'm a fourth year CS PhD student at Stanford University, where I'm very fortunate to be advised by Matei Zaharia and Carlos Guestrin.

My research interest are in building scalable and efficient systems that support knowledge-intensive AI applications, which leverage large amounts of structured or unstructured data.

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Selected Publications

LOTUS is a query engine for LLM-powered data processing over. LOTUS introduces the semantic operator model, a declarative programming model for AI-based data transformations. LOTUS allows programmers to write state-of-the-art AI pipelines in a few lines of code for a wide array of applications, including fact-checking, biomedical extraction, complex search and ranking, and research analysis.

Table-Augmented Generation (TAG) is a unified and general-purpose paradigm for answering natural language questions over databases. TAG generalizes and outperforms prior methods, such as RAG and Text2SQL on a benchmark of complex NL questions.

ACORN is an index for state-of-the-art search over vector embeddings and structured data. ACORN introduces predicate-agnostic index construction and search methods, which allows it to serve a wide range of queries with arbitrary predicates, while also outperforming prior methods.

Compass is a semantic search system over encrypted data that offers high accuracy, comparable to state-of-the-art plaintext search algorithms, while protecting data, queries and search results from a fully compromised server.

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