Dr. DI is a Research Assistant Professor in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology. He was a postdoctoral researcher at HKUST working with Prof. Xiaofang ZHOU. Prior to that, he obtained his bachelor's degree in Mathematics from Hunan University, and subsequently completed his master's (2017) and doctoral degrees in Computer Science and Engineering from HKUST (2022), under the supervision of Prof. Lei CHEN. He did an internship at 4Paradigm Inc., and was mentored by Dr. Quanming YAO.
The past research of Dr. DI focuses on graph representation learning, especially knowledge graph embedding and graph neural network. Heterogenous graph, as a data structure composed of multiple types of nodes and edges, is currently an important way to store and organize human knowledge and human behavior due to its ability to describe diverse correlations between multiple data objects. Heterogeneous graphs are ubiquitous in many subject areas, such as protein interaction networks in natural sciences and social networks in social sciences. In biological information modeling, heterogeneous graphs can be used to model the interactions between genes, proteins, compounds and other biomolecules, thereby helping researchers to deeply explore the structure and function of biological systems; in guiding social opinions, heterogeneous graphs can establish relationships between people, tweets/short videos, and comments, and can influence public cognition and behavior by modifying the information dissemination of social networks.
His past works mainly include graph representation model design and graph representation learning&training. Orienting to heterogeneous graphs in different scenarios, the graph representation learning model maps nodes, edges, and subgraphs from the discretized state to the continuous vector space for serving downstream tasks. Graph representation learning&training study the possible impact of learned graph representations on the different scenarios, so as to modify the graph representation to adapt to the domain-specific tasks and scenarios.