Ph.D. Candidate in Computer Science · Reliable RAG
Liangliang Zhang 张靓靓
My Ph.D. research asks a practical question: when a large language model uses external evidence, how can we make its answer reliable? I study retrieval, planning, knowledge graphs, and evaluation to build RAG systems that are grounded, efficient, and trustworthy.
Curious by nature. Serious about reliable AI.
Research Goal
I work to make RAG systems reliable enough to trust.
I target the gap between retrieving information and using it responsibly: helping LLMs plan retrieval, connect evidence across knowledge sources, and demonstrate that their answers are grounded in sufficient, relevant evidence.
Expertise
Reliable RAG and supporting areas
Reliable RAG
Retrieval planning, grounded reasoning, and evidence-aware generation for more dependable LLM behavior.
Knowledge Graphs
Knowledge grounding, structured retrieval, and benchmark design for robust AI systems.
Graph Machine Learning
Graph neural networks, graph condensation, and scalable representation learning.
Evaluation and Data
Dataset diagnosis, benchmarking, and evaluation methods that make RAG claims more reliable and actionable.
Internships
Industry research experience
Returning Summer Research Intern · Summer 2026
Yorktown Heights. With the same IBM Research collaboration team, developed AGENTICTT for adaptive table-text QA.
Output: Adaptive Agentic Retrieval and Reasoning for Table-Text QA, submitted to AAAI 2027; arXiv preprint forthcoming.
Summer Research Intern · Summer 2025
Yorktown Heights. With the same IBM Research collaboration team, developed a benchmark for data product discovery over tables and text.
Output: From Factoid Questions to Data Product Requests: Benchmarking Data Product Discovery over Tables and Text, arXiv:2510.21737.
Ph.D. Candidate · Ongoing
Rensselaer Polytechnic Institute. Reliable RAG, knowledge graphs, graph learning, and trustworthy evaluation in the DAMI Lab.
Publications
Selected publications
Selected first-author work on reliable RAG, data discovery, and graph learning. See the full publication list for all papers and academic service.
From Factoid Questions to Data Product Requests: Benchmarking Data Product Discovery over Tables and Text
Liangliang Zhang, Nandana Mihindukulasooriya, Niharika S. D'Souza, Sola Shirai, Sarthak Dash, Yao Ma, Horst Samulowitz
PreprintDiagnosing and Addressing Pitfalls in KG-RAG Datasets: Toward More Reliable Benchmarking
Liangliang Zhang, Zhuorui Jiang, Hongliang Chi, Haoyang Chen, Mohammed Elkoumy, Fali Wang, Qiong Wu, Zhengyi Zhou, Shirui Pan, Suhang Wang, Yao Ma
PaperExtending Graph Condensation to Multi-Label Datasets: A Benchmark Study
Liangliang Zhang, Haoran Bao, Yao Ma
PaperContact
Let's connect
Email me at zhangl41@rpi.edu for research collaborations, research scientist, applied scientist, and machine learning opportunities.
Learn more on the About, Publications, and DAMI Lab pages.