Researcher at Computational Health Sciences of University of Minnesota Twin Cities. Incoming PhD student in Computer Science at UMass Amherst.
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:
Health 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
(04/14/24): RT: A Retrieving and Chain-of-Thought Framework for Few-Shot Medical Named Entity Recognition, was accepted by Journal of the American Medical Informatics Association.JAMIA.
(02/19/24): Our latest work on zero shot link prediction: A Condensed Transition Graph Framework for Zero-shot Link Prediction with Large Language Models.
(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.