I am a PhD student at University of Massachusetts Amherst, advised by Prof. Hong Yu.
My core research pursuits center around the intersection of knowledge graphs and natural language processing. I am particularly fascinated by areas including information extraction, knowledge graph completion, question answering, zero-shot learning, and beyond. I embody a self-driven and innovative approach to rapid learning, coupled with a deep enthusiasm for NLP and knowledge graphs. My current research interests include:
Biomedical and clinical NLP: Creating NLP models to tackle real-world Medical challenges.
Information extraction with weak supervision: Information extraction, knowledge-enpowered information extraction, zero shot learning, few shot learning.
Question answering: Complex question answering over knowledge graph, zero shot question answering or few shot question answering.
Natural language generation and inference
Retrieval/Knowledge-base Large Language Model
News and Highlights
(09/06/24): A Condensed Transition Graph Framework for Zero-shot Link Prediction with Large Language Models was accepted by ICDM 2024.
(05/16/24) Three papers RT for NER, LEAP and RL for healthcare were accepted by JAMIA.
(04/04/23): Understand the Dynamic World: An End-to-End Knowledge Informed Framework for Open Domain Entity State Tracking was accepted by SIGIR 2023.
(10/06/22): A Hierarchical N-Gram Framework for Zero-Shot Link Prediction was accepted by EMNLP 2022.
(08/17/22): Semantic Structure based Query Graph Prediction for Question Answering over Knowledge Graph was accepted by COLING 2022.