Rate My Professor Daokun Zhang

Daokun Zhang is an Adjunct Research Fellow in the Department of Data Science and Artificial Intelligence, Faculty of Information Technology at Monash University, a position he assumed in January 2024. Previously, he held the role of Research Fellow and Lecturer in the same department from April 2021 to January 2024. Prior to Monash, Dr. Zhang served as a Postdoctoral Research Associate at The University of Sydney from June 2019 to April 2021. He received his PhD degree in data science from the University of Technology Sydney in November 2019, with a thesis entitled Augmented Network Embedding in Attributed Graphs.

Dr. Zhang's research focuses on graph machine learning, including graph representation learning, graph structure learning, node classification, link prediction, weakly supervised learning with sparse or noisy labels, and uncertainty quantification in machine learning predictions. His interests also encompass network analysis, knowledge graphs, and applications in knowledge graph completion and alignment, combinatorial optimization, computational drug discovery and materials science, particle system simulation, and geographic data forecasting. He has authored numerous publications in leading venues. Key works include Network representation learning: A survey (IEEE Transactions on Big Data, 2020), Towards unsupervised deep graph structure learning (The Web Conference, WWW 2022), Link prediction with contextualized self-supervision (IEEE Transactions on Knowledge and Data Engineering, 2023), GraphormerDTI: A graph transformer-based approach for drug-target interaction prediction (Computers in Biology and Medicine, 2024), Graph Neural Network-Based Molecular Property Prediction with Patch Aggregation (Journal of Chemical Theory and Computation, 2024), and Towards complex dynamic physics system simulation with graph neural ordinary equations (Neural Networks, 2024). Additional contributions appear in conferences such as AAAI, ICDM, PAKDD, and journals including Expert Systems with Applications, Engineering Geology, and Data Mining and Knowledge Discovery.