Shirui Pan is an ARC Future Fellow (awarded in 2021) and Senior Lecturer (equiv. Associate Professor in US) with the Department of Data Science & AI, Faculty of Information Technology, Monash University. He received his Ph.D degree in computer science from UTS, Australia.
Shirui has made contributions to advance graph machine learning methods for solving hard AI problems for real-life applications, including graph classification, anomaly detection, recommender systems, and multivariate time series forecasting. His research has been published in top conferences and journals including NeurIPS, ICML, KDD, TPAMI, TNNLS, and TKDE. He is recognised as one of the AI 2000 AAAI/IJCAI Most Influential Scholars in Australia (2021). He is an awardee of a prestigious Future Fellowship (2021-2025), one of the most competitive grants from the Australian Research Council (ARC).
Multiple PhD positions are available! I am looking for self-motivated Ph.D students. Applicants in Australia are especially welcome. See more information here.
For Monash students, I can only supervise 1-2 honours/minor thesis students each year. Please see the information about my reserach group in this post before you apply.
PhD in Computer Science, 2015
University of Technology Sydney
Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction towards cognition and human-level intelligence. In this survey, we provide a comprehensive review on knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference and logical rule reasoning are reviewed. We further explore several emerging topics including meta relational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of datasets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions.
While numerous approaches have been developed to embed graphs into either Euclidean or hyperbolic spaces, they do not fully utilize the information available in graphs, or lack the flexibility to model intrinsic complex graph geometry. To utilize the strength of both Euclidean and hyperbolic geometries, we develop a novel Geometry Interaction Learning (GIL) method for graphs, a well-suited and efficient alternative for learning abundant geometric properties in graph. GIL captures a more informative internal structural features with low dimensions while maintaining conformal invariance of each space. Furthermore, our method endows each node the freedom to determine the importance of each geometry space via a flexible dual feature interaction learning and probability assembling mechanism. Promising experimental results are presented for five benchmark datasets on node classification and link prediction tasks.
IEEE TPAMI, TNNLS, TKDE, TCYB; ICML, NeurIPS, KDD, WSDM, ICDM, AAAI, IJCAI
[Award]: Enabling Automatic Graph Learning Pipelines with Limited Human Knowledge ($800,000 from ARC and $470,000 from Monash University) - 2021-2025
[Award]: 2021 FIT Dean’s Award for Excellence in Research by an Early Career Researcher
[Award]: [AI 2000 AAAI/IJCAI Most Influential Scholars Honorable Mention] (../../post/ai-2000-certificate.png) (only five recipients in Australia) (08/04/2021).
[Award]: [3 Papers are Selected as Most Influential Papers in IJCAI] (https://www.paperdigest.org/2021/03/most-influential-ijcai-papers-2021-03/) (08/03/2021).
[Award]: [1 Paper is Selected as Most Influential Paper in KDD] (https://www.paperdigest.org/2021/03/most-influential-kdd-papers-2021-03/) (08/03/2021).
[Award]: [1 Paper is Selected as Most Influential Paper in AAAI] (https://www.paperdigest.org/2021/03/most-influential-aaai-papers-2021-03/) (08/03/2021).
[Award]: [1 Paper is Selected as Most Influential Paper in CIKM] (https://www.paperdigest.org/2021/03/most-influential-cikm-papers-2021-03/) (08/03/2021).
Best Student Paper Award for ICDM-2020 (CORE A* conference)
The Vannevar Bush Best Paper Honorable Mention for JCDL-2020 (CORE-2018 A* conference)
Anomaly Detection in Social Networks ($11,000) - 2019-2020
Cyberbullying Detection on Social Networks ($20,000) - 2016-2017