Research Statement
My research focuses on information retrieval, retrieval-augmented generation, LLM-based agents, and agentic memory. I am particularly interested in building robust information-seeking agents that can adapt to unseen domains through feedback-driven alignment and long-term experience accumulation.
My work studies how retrieval systems can use corpus-level statistics, retrieval evidence, complementary sparse-dense signals, retrieval-state feedback, and memory mechanisms to improve data generation, query refinement, hybrid retrieval, reasoning-time decision making, and long-horizon agent behavior.
I also study how memories should be generated from past trajectories, retrieved for future tasks, updated as new evidence arrives, and managed efficiently under practical inference constraints. My broader goal is to develop self-improving retrieval agents that remain reliable, efficient, and grounded in external evidence while accumulating useful experience over time.