Short Biography

I am currently an assistant professor at School of Computer, National University of Defense Technology. Before joining NUDT, I obtained my PhD degree from University of New South Wales (UNSW), Australia under the supervision of Prof. Liming Zhu , Dr. Chen Wang and Dr. Jessie Yin . I am currently working on optimizations for storage, OS in HPC systems. I am also interested in improving the interpretability and robustness of machine learning models.

• January 2020: I joined School of Computer, National University of Defense Technology as an assistant professor.
• Feburary 2020: Our paper on interpretable deep learning is accepted by ACM Transactions on the Web.
• June 2023: Two papers on GNNs for heterophilic graphs are accepted by ECML/PKDD and FITEE, respectively.
• July 2023: Our paper on optimizing HPC I/O performance via learning approaches is accepted by IEEE Cluster 2023.

• Zhangyu Liu, Cheng Zhang, Huijun Wu, Jianbin Fang, Lin Peng, Guixin Ye, Zhanyong Tang. Optimizing HPC I/O Performance with Regression Analysis and Ensemble Learning. The 25th IEEE International Conference on Cluster Computing (Cluster 2023). Sante Fe ,New Mexico, USA. October 31-November 3, 2023.

• Xugang Wu, Huijun Wu, Ruibo Wang, Duanyu Li, Xu Zhou and Kai Lu. Leveraging Free Labels to Power Up Heterophilic Graph Learning in Weakly-Supervised Settings: An Empirical Study. The 22rd European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2023). Turin, Italy. September 18 -22 2023.

• Xugang Wu, Huijun Wu, Ruibo Wang, Xu Zhou and Kai Lu. Towards Adaptive Graph Neural Networks via Solving Prior-Data Conflicts. Frontiers of Information Technology & Electronic Engineering (FITEE). 2023. Accepted.

• Xugang Wu, Huijun Wu, Xu Zhou, Xiang Zhao and Kai Lu. Towards Defense against Adversarial Attacks on Graph Neural Networks via Calibrated Co-training. Journal of Computer Science and Technoligy (JCST), 37, p1161–1175 (2022).

• Huijun Wu, Chen Wang, Richard Nock, Wei Wang, Jie Yin, Kai Lu and Liming Zhu. SMINT: Towards Interpretable and Robust Model Sharing for Deep Neural Networks. ACM Transactions on the Web (TWEB). Vol 14, Issue 3. July 2020.

• Rimmal Nadeem, Huijun Wu, Hye-Young Paik and Chen Wang. A Case based Deep Neural Network Interpretability Framework and its User Study. The 20th International Conference on Web Information Systems Engineering (WISE'19), November 2019, Hong Kong, China.

• Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu and Liming Zhu. “Adversarial Examples on Graph Data: Deep Insights into Attack and Defense”. The 28th International Joint Conference on Artificial Intelligence (IJCAI'19), August 2019, Macao, China.

• Dongyao Wu, Sherif Sakr, Liming Zhu, Sung Une Lee and Huijun Wu. “HDM-MC in-Action: A Framework for Big Data Analytics across Multiple Clusters”. The 38th IEEE International Conference on Distributed Computing Systems (ICDCS'18), July 2018, Vienna, Austria.

• Huijun Wu, Chen Wang, Kai Lu, Yinjin Fu, Liming Zhu. One Size Does Not Fit All: The Case For Chunking Configuration in Backup Deduplication. The 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid (CCGrid'18). May 2018, Washington, D.C., USA.

• Huijun Wu, Chen Wang, Yinjin Fu, Sherif Sakr, Kai Lu and Liming Zhu. A Differentiated Caching Mechanism to Enable Primary Storage Deduplication in Clouds. IEEE Transactions on Parallel and Distributed Systems (TPDS).

• Huijun Wu, Chen Wang, Jessie Yin, Kai Lu and Liming Zhu. Sharing Deep Neural Network Models with Interpretation. The 27th ACM International Conference on World Wide Web (WWW'18). April 2018, Lyon, France.

• Dongyao Wu, Sherif Sakr, Liming Zhu, Huijun Wu. Towards Big Data Analytics across Multiple Clusters. The 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid (CCGrid'17). May 2017, Madrid, Spain.

• Huijun Wu, Chen Wang, Yinjin Fu, Sherif Sakr, Liming Zhu and Kai Lu. HPDedup: A Hybrid Prioritized Data Deduplication Mechanism for Primary Storage in the Cloud. The 33rd IEEE International Symposium on Mass Storage Systems and Technologies (MSST'17). May 2017, Santa Clara, USA.

• Journal Reviewer: IEEE Transactions on Knowledge and Data Engineering, IEEE Internet of Things Journal, Journal of Cluster Computing, Journal of Ambient Intelligence and Humanized Computing, Security & Privacy
• PC member and Conference Reviewer: ICONIP'18, ICONIP'19

• Hybrid Prioritized Data Deduplication in Primary Storage

  Presentation at the 33rd Mass Storage System and Technology (MSST'17) Conference. Santa Clara, USA. May 2017.

• Differentiated Fingerprint Caching Design for Primary Storage Deduplication.

  Invited Talk at the Aliyun-CCF Excellent Paper Workshop on Storage Systems, Shanghai Jiaotong University. Shanghai, China. April 2018.

• Interpretable Deep Learning Model Sharing.

  Invited Talk. Institute of Software Systems, University of Augsburg, Germany. Augsburg, Germany. April 2018.

• Sharing Deep Learning Model with Interpretation.

  Presentation at the 27th World Wide Web (WWW'18) conference. Lyon, France. April 2018.

• Chunking Configuration in Backup Deduplication.

  Presentation at the 18th International Conference on Cluster, Cloud and Grid (CCGrid'18). Washington D.C., USA. May 2018.