Hi, I'm Yash

My doctoral research develops IKnowFlow, a framework for making the flow of knowledge interpretable and auditable in AI systems through interpretable retrieval, explainable evidence selection, and grounded source attribution.

Yash Saxena

About Me

I am a Ph.D. student in Computer Science at the University of Maryland, Baltimore County (UMBC). My research focuses on making AI systems trustworthy for sensitive domains. I formalize IKnowFlow or Interpretable Knowledge Flow, as a framework for making knowledge flow auditable across stages: why a document was retrieved, why it was selected as evidence, and whether a generated claim can be traced back to that evidence. My work spans interpretable dense retrieval with IMRNNs, rationale-driven evidence selection with METEORA, attribution benchmarking with REASONS, and provenance-aware verification using structured scholarly knowledge graphs.

Works Published In

Recent News

A linked timeline of current research updates, talks, and coverage.

All publications
Summer 2026
Visiting Research Intern at TIB Hannover

Working with Prof. Sören Auer on provenance and attribution methods for the Open Research Knowledge Graph.

Jun 2026
IJCAI-ECAI Doctoral Consortium acceptance

IKnowFlow was accepted to the IJCAI-ECAI 2026 Doctoral Consortium in Bremen, Germany, with travel grant support.

May 2026
METEORA accepted at ICML 2026

METEORA reframes re-ranking as rationale-driven evidence selection for safer and more interpretable RAG in sensitive domains.

Explore My Work