Xiao HUANG

Assistant Professor Department of Computing HK Polytechnic University

I am leading the DEEP Lab at The Hong Kong Polytechnic University (Data Exploring and Extracting @ PolyU). We strive to develop effective machine learning algorithms for networked data to solve real-world prediction problems in social science, recommender systems, education, transportation, healthcare. DEEP Lab has 12 Ph.D. students, 1 postdoctoral fellow, and several master's students. [Open positions] I am actively recruiting Ph.D. students and postdocs (fully funded). Please feel free to drop me emails with your CV. Self-funded visiting students/scholars are welcome. My research interests span graph neural networks, knowledge graphs, attributed networks, anomaly detection, recommender systems, and question answering. [Call for papers]

News and Highlights

• [2023/04] We have one paper accepted by ICML 2023
  

• [2023/04] We have two papers accepted by SIGIR 2023
  

• [2023/03] We host "International Workshop on Learning with Knowledge Graphs: Construction, Embedding, and Reasoning" at WSDM 2023 on March 3
  

• [2023/01] We have one paper accepted by theWebConf 2023
  

• [2022/11] Our paper "Intelligent Instructional Design via Interactive Knowledge Graph Editing" has been selected as BEST PAPER presented at the International Conference On Web-Based Learning (ICWL 2022)
  

• [2022/09] Our Smart Traffic Fund project, Development of an Augmented Reality-Assisted Head-up Display (AR-HUD) mechanism for recommending driving strategy, has been funded
  

• [2022/07] Our ECS project, Error-Aware and Contrastive Knowledge Graph Learning, has been funded
  

• [2022/05] Our Smart Traffic Fund project, AI-enabled Parking Vacancy Prediction Framework using Multi-source Data, has been funded
  

Selected Publications

1. Zirui Liu, Shengyuan Chen, Kaixiong Zhou, Daochen Zha, Xiao Huang, Xia Hu, RSC: Accelerating Graph Neural Networks Training via Randomized Sparse Computation, ICML 2023

2. Huachi Zhou, Hao Chen, Junnan Dong, Daochen Zha, Chuang Zhou, Xiao Huang, Adaptive Popularity Debiasing Aggregator for Graph Collaborative Filtering, SIGIR 2023

3. Feiran Huang, Zefan Wang, Xiao Huang, Yufeng Qian, Zhetao Li, Hao Chen, Aligning Distillation For Cold-start Item Recommendation, SIGIR 2023

4. Junnan Dong, Qinggang Zhang, Xiao Huang, Keyu Duan, Qiaoyu Tan and Zhimeng Jiang, Hierarchy-Aware Multi-Hop Question Answering over Knowledge Graphs, TheWebConf 2023

5. Huachi Zhou, Shuang Zhou, Keyu Duan, Xiao Huang, Qiaoyu Tan, Zailiang Yu, Interest Driven Graph Structure Learning for Session-Based Recommendation, PAKDD 2023

6. Junnan Dong, Qinggang Zhang, Xiao Huang, Qiaoyu Tan, Daochen Zha, Zihao Zhao, Active Ensemble Learning for Knowledge Graph Error Detection, WSDM 2023

7. Qiaoyu Tan, Ninghao Liu, Xiao Huang, Soo-Hyun Choi, Li Li, Rui Chen, Xia Hu, S2GAE: Self-Supervised Graph Autoencoders Are Generalizable Learners with Graph Masking, WSDM 2023

8. Qinggang Zhang, Junnan Dong, Keyu Duan, Xiao Huang, Yezi Liu, Linchuan Xu, Contrastive Knowledge Graph Error Detection, CIKM 2022

9. Song Wang, Yushun Dong, Xiao Huang, Chen Chen, Jundong Li, FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs, IJCAI 2022

10. Zhiming Xu, Xiao Huang, Yue Zhao, Yushun Dong, Jundong Li, Contrastive Attributed Network Anomaly Detection with Data Augmentation, PAKDD 2022

11. Kaixiong Zhou, Xiao Huang, Daochen Zha, Rui Chen, Li Li, Soo-Hyun Choi, Xia Hu, Dirichlet Energy Constrained Learning for Deep Graph Neural Networks, NeurIPS 2021

12. Liqiao Xia, Pai Zheng, Xiao Huang, Chao Liu, A Novel Hypergraph Convolution Network-Based Approach for Predicting the Material Removal Rate in Chemical Mechanical Planarization, Journal of Intelligent Manufacturing, 2021

13. Kaixiong Zhou, Xiao Huang, Yuening Li, Daochen Zha, Rui Chen, Xia Hu, Towards Deeper Graph Neural Networks with Differentiable Group Normalization, NeurIPS 2020

14. Kaixiong Zhou, Qingquan Song, Xiao Huang, Daochen Zha, Na Zou, Xia Hu, Multi-Channel Graph Neural Networks, IJCAI 2020

15. Xiao Huang, Qingquan Song, Yuening Li, Xia Hu, Graph Recurrent Networks With Attributed Random Walks, KDD 2019 (Slides, Code)

16. Xiao Huang, Jingyuan Zhang, Dingcheng Li, Ping Li, Knowledge Graph Embedding Based Question Answering, WSDM 2019 (Slides, Code)

17. Xiao Huang, Qingquan Song, Fan Yang, Xia Hu, Large-Scale Heterogeneous Feature Embedding, AAAI 2019 (Slides, Code)

18. Xiao Huang, Jundong Li, Na Zou, Xia Hu, A General Embedding Framework for Heterogeneous Information Learning in Large-Scale Networks, TKDD, 2018 (Slides, Code)

19. Xiao Huang, Qingquan Song, Jundong Li, Xia Hu, Exploring Expert Cognition for Attributed Network Embedding, WSDM, pages 270–278, 2018 (Slides, Code)

20. Ninghao Liu, Xiao Huang, Jundong Li, Xia Hu, On Interpretation of Network Embedding via Taxonomy Induction, KDD 2018

21. Xiao Huang, Jundong Li, Xia Hu, Label Informed Attributed Network Embedding, WSDM 2017 (Slides, Code)

22. Qingquan Song, Xiao Huang, Hancheng Ge, James Caverlee, Xia Hu, Multi-Aspect Streaming Tensor Completion, KDD, pages 435–443, 2017

Service

• PC member: NeurIPS 2021-2023, AAAI 2021-2023, ICLR 2022-2023, KDD 2019-2023, TheWebConf 2022-2023, ICML 2021-2023, IJCAI 2020-2023, CIKM 2019-2022, WSDM 2021-2023, SDM 2022, ICKG 2020-2021

• Reviewer: IEEE TKDE, ACM TKDD, ACM Computing Surveys, ACM TOIS, IEEE Transactions on Cybernetics, IEEE Transactions on Big Data, IEEE TCSS, ACM TIST, International Journal of Machine Learning and Cybernetics - Springer

• Session Chair: WSDM 2023

• Editorial board member: Computers & Education: X Reality

• Topical Advisory Panel and Guest editor: Algorithms - MDPI

Awards and Patents

• BEST PAPER at the International Conference On Web-Based Learning (ICWL 2022), Tenerife, Spain

• United States Patent, KNOWLEDGE-GRAPH-EMBEDDING-BASED QUESTION ANSWERING,
  US 2020/0242444 A1

• 2019 INFORMS QSR Best Student Paper Finalist, Seattle, USA

• 2019 INFORMS QSR Best Refereed Paper Finalist, Seattle, USA

• SDM 2017 Doctoral Forum Best Poster Runner-Up Award, Houston, USA

Talks

• [2023/03] Keynote on Machine Learning on Graphs on WSDM 2023 workshop, Singapore

• [2022/08] Talk on Trustworthy Graph Neural Networks on SMP 2022 workshop

• [2019/08] Tutorial on learning from networks on KDD 2019, Alaska

Background

I received Ph.D. from Texas A&M University in 2020, under the supervision of Dr. Xia Hu. I received M.S. from Illinois Institute of Technology in 2015, and B.S. from Shanghai Jiao Tong University in 2012. Before my current position, I worked as a research intern at Microsoft Research and Baidu USA.