Published 'MoR: Better Handling Diverse Queries with a Mixture of Sparse, Dense, and Human Retrievers' at EMNLP 2025
Published 'TeamCMU at Touché: Adversarial Co-Evolution for Advertisement Integration and Detection in Conversational Search' at CLEF 2025 Touché Lab (Best Paper Award)
Published 'LTRR: Learning To Rank Retrievers for LLMs' at SIGIR 2025 LiveRAG Workshop
Contributed to international evaluation campaigns including TREC and NTCIR, especially the Tip-of-the-Tongue retrieval track
Participated in NeurIPS 2022 IGLU Competition on asking clarifying questions
Background
Third-year PhD student at the Language Technologies Institute (LTI), Carnegie Mellon University
Research focuses on retrieval-enhanced machine learning (REML) and retrieval-augmented generation (RAG)
Addresses real-world AI system challenges such as data provider attribution and advertisement strategies in conversational search
Works on evaluation and benchmarking of AI systems, including user and query simulators
Aims to make AI systems more efficient, reliable, and responsible in real-world settings