activity
20242026
most citedTowards Accurate and Efficient Document Analytics with Large Language Models

3 citations · 3 across the 4 of their papers we have counts for

collaborators

5 papers

cs.DB2026

Robust Hierarchical Structures for Agentic Document Analysis

Ruiying Ma, Yiming Lin, Aditya G. Parameswaran

Large Language Models (LLMs) enable us to better understand text documents, including PDFs and Word documents. However, LLMs, as well as more modern LLM agents, i.e., those with to…

cs.DB2026

Can AI Agents Answer Your Data Questions? A Benchmark for Data Agents

Ruiying Ma, Shreya Shankar, Ruiqi Chen +7

However, building reliable data agents remains difficult because real enterprise data is fragmented across many heterogeneous database systems, with duplicated and inconsistent dat…

cs.SE2025

Let the Barbarians In: How AI Can Accelerate Systems Performance Research

Audrey Cheng, Shu Liu, Melissa Pan +18

Artificial Intelligence (AI) is beginning to transform the research process by automating the discovery of new solutions. This shift depends on the availability of reliable verifie…

cs.AI2025

MetaMuse: Algorithm Generation via Creative Ideation

Ruiying Ma, Chieh-Jan Mike Liang, Yanjie Gao +1

Designing system algorithms remains challenging, where the discontinuous nature of the solution space often forces system engineers to rely on generic heuristics at the expense of…

cs.DB2024★ 3 cited

Towards Accurate and Efficient Document Analytics with Large Language Models

Yiming Lin, Madelon Hulsebos, Ruiying Ma +4

Unstructured data formats account for over 80% of the data currently stored, and extracting value from such formats remains a considerable challenge. In particular, current approac…