
I study how information can be represented, retrieved, and analyzed effectively across large, distributed, and privacy-sensitive environments. My current work brings together information retrieval, small and local language models, multi-agent systems, and information-theoretic methods. Recent projects examine evidence design for retrieval-augmented question answering, agentic scholarly communication, privacy-preserving library analytics, and LIT/DLITE measures for search and machine learning.
Download CV in PDFPhD in Information Science, 2010
UNC Chapel Hill
Masters in Information Science, 2006
Indiana University Bloomington
Bachelors in Chemical Engineering, 1998
East China University of Science and Technology





Information retrieval, responsible AI, and information theory

The definition and measurement of information is fundamental to methods for information retrieval, text mining, and machine learning.

A local small-model system for interpretable analysis of library survey data while keeping sensitive institutional data under local control.

Machine learning, deep reinforcement learning, training and fine tuning with DLITE loss.

Decentralized search and retrieval on the web scale. Efficiency, effectiveness, and scalability.
Selected peer-reviewed research
Industry to Academia