My research areas include Natural Language Processing (NLP), Large Language Models (LLMs), Knowledge Graphs, Dialogue Systems, Machine Learning, Deep Learning, Reinforcement Learning, and Causality. I have published over 20 papers in top-tier conferences and journals such as ACL, SIGIR, NAACL, EMNLP, IJCAI, AAAI, ISWC, and JWS. I also served as a program committee member and reviewer for conferences and journals, including EMNLP, ACL, AAAI, and JWS.
Dr. Devin Yuncheng Hua is a Postdoctoral Fellow working in the School of Computer Science and Engineering (CSE) at UNSW. He holds a PhD in Artificial Intelligence from Monash University and a PhD in Computer Science and Technology from Southeast University (China). Dr. Hua previously served as a Research Fellow at Monash University. His research areas include Natural Language Processing (NLP), Large Language Models (LLMs), Knowledge Graphs,...view more
Dr. Devin Yuncheng Hua is a Postdoctoral Fellow working in the School of Computer Science and Engineering (CSE) at UNSW. He holds a PhD in Artificial Intelligence from Monash University and a PhD in Computer Science and Technology from Southeast University (China). Dr. Hua previously served as a Research Fellow at Monash University. His research areas include Natural Language Processing (NLP), Large Language Models (LLMs), Knowledge Graphs, Dialogue Systems, Machine Learning, Deep Learning, Reinforcement Learning, and Causality. Dr. Hua has published over 20 papers in top-tier conferences and journals such as ACL, SIGIR, NAACL, EMNLP, IJCAI, AAAI, ISWC, and JWS. He has also served as a program committee member and reviewer for conferences and journals, including EMNLP, ACL, AAAI, and JWS.
My Research Activities
Generative AI Simulation (GenAISim) Project
- The GenAISim project is funded by the ARC Centre of Excellence for Automated Decision Making and Society (ADM+S), Australia. This project will investigate a hybrid decision- making system, leveraging cooperative knowledge from multiple stakeholders through socio-technical observations, and knowledge priors in Large Language Models (LLMs) and open datasets. It will develop a novel suite of generative and data-driven simulations, useful for depicting current and future urban scenarios, including in mobility, urban policymaking, and health domains.
