About Me

I am an AI researcher and applied scientist specializing in NLP, LLMs, and intelligent agent systems, with a focus on turning cutting-edge AI research into real-world products. My work spans LLM post-training, retrieval-augmented systems, self-evolving agents, and evaluation, aimed at building AI systems that are reliable, scalable, and continuously improving. Across roles at Amazon and Apple, I’ve built infrastructure and algorithms for agent improvement, knowledge extraction, hallucination mitigation, and human-in-the-loop evaluation. With a PhD in Information Science and publications across both AI and HCI, I bring a cross-disciplinary, product-oriented perspective to building intelligent systems that are technically rigorous, practically useful, and built for real-world impact.

News

Our paper In-Context Sampling Strategy for Reliable LLM Prompting (Arxiv version will be updated soon) was accepted to NAACL 2024!

Our paper FLEEK: Factual Error Detection and Correction with Evidence Retrieved from External Knowledge has been accepted to EMNLP2023! [PDF].

Our paper Malicious Selling Strategies in E-Commerce Livestream: A Case Study of Alibaba’s Taobao and ByteDance’s Douyin has been accepted to TOCHI! [PDF].

Happy to have my first student workshop paper! Our paper Machine Narrative Comprehension in a Fictional Characters Personality Prediction Task has been accepted to NAACL SRW 2022!

Our survey A Survey of Machine Narrative Reading Comprehension Assessments has been accepted to the IJCAI-ECAI2022 Survey Track (acceptance rate 18%) [PDF].

Our paper TVShowGuess: Character Comprehension in Stories as Speaker Guessing [PDF, Github] has been accepted to NAACL 2022!