His research is dedicated to developing innovative approaches for graph-related tasks, with a keen interest in graph neural networks and large language models applied to graph-level tasks, including graph similarity, graph matching, and HLS design modeling. Yunsheng's work has been featured in prestigious conferences across various domains, from data mining to artificial intelligence and machine learning, such as WSDM, KDD, IJCAI, AAAI, ICML, and NeurIPS.