Yongshun Gong is a Professor in the School of Software, Shandong University. He received his Ph.D. degree from University of Technology Sydney in 2020. He has got the support by Natural Science Foundation of Shandong Province for Excellent Young Scholars. His principal research interest covers data science, representation learning and machine learning, particularly in the areas of representation learning, spatio-temporal data mining, predictive model, multi-model enhencement, recommender systems, matrix factorization, and sequential pattern mining. He has published above 50 papers in top journals and refereed conference proceedings for Artificial Intelligence, Pattern Recognition, IEEE T-PAMI, IEEE T-CYB, IEEE T-KDE, IEEE T-NNLS, IEEE T-MM, NeruIPS, IJCAI, AAAI, KDD, CVPR, ACM MM, CIKM, ICME, etc.  

Google scholar:https://scholar.google.com/citations?user=WIHqungAAAAJ&hl=en 

What's new:

02/2024: One paper is accepted to IEEE TKDE (CCF-A)

02/2024: One paper is accepted to Knowledge Based Systems (SCI-1)

01/2024: One paper is accepted to FSE/ESEC 2024 (CCF-A)

01/2024: One paper is accepted to Artificial Intelligence (CCF-A)

12/2023: One paper is accepted to ICASSP 2024 (CCF-B)

12/2023: Two papers are accepted to AAAI 2024 (CCF-A)

10/2023: One paper is accepted to Knowledge Based Systems (SCI-1)

09/2023: One paper is accepted to ICDM 2023 (CCF-B)

08/2023: One paper is accepted to CIKM 2023 (CCF-B)

07/2023: One paper is accepted to ACM MM 2023 (CCF-A)

07/2023: One paper is accepted to Information Sciences (SCI-1)

06/2023: One paper is accepted to IEEE TMM.

03/2023: One paper is accepted to IEEE TPAMI.

03/2023: One paper is accepted to IEEE TSUSC.

01/2023: One paper is accepted to TOIS.

10/2022: One paper is accepted to IEEE TKDE.

08/2022: One paper is accepted to IEEE TKDE.

03/2022, Two papers are accepted to ICME 2022.

03/2022, One paper is accepted to CVPR 2022.

02/2022, I serve as the Technical Program Committee in the International Conference on Big Data and Artificial Intelligence (ICBDAI 2022), and welcome to submit papers.

01/2022, We held a Workshop about Spatio-temporal Data Mining in the International Conference on Machine Learning, Cloud Computing and Intelligent Mining, and welcome to submit papers.

11/2021, One paper is accepted to IEEE TMM.

08/2021, One paper is accepted to ACM-MM 2021 Industrial track (main Proceeding).

08/2021, One paper is accepted to Briefings in Bioinformatics.

04/2021, One paper is accepted to IEEE TKDE.

03/2021, One paper is accepted to IEEE TMM.

02/2021, One Paper is accepted to IEEE TNNLS.

12/2020, One Paper is accepted to IEEE TNNLS.

Selected Publications：

1. 1. Yongshun Gong, Zhibin Li, Jian Zhang, Wei Liu, Yilong Yin, Yu Zheng. Missing Value Imputation for Multi-view Urban Statistical Data via Spatial Correlation Learning. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021. (JCR Q1，IF=6.977，CCF-A)

2. 2. Yongshun Gong, Zhibin Li, Wei Liu, Xiankai Lu, Xinwang Liu, Ivor W. Tsang, Yilong Yin. Missingness-pattern-adaptive Learning with Incomplete Data. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023. (IF=24.31，CCF-A)

3. 3. Yongshun Gong, Zhibin Li, Jian Zhang, Wei Liu, Yu Zheng. Online Spatiotemporal Crowd Flow Distribution Prediction for Complex Metro System. IEEE Transactions on Knowledge and Data Engineering (TKDE), Early access, 2020. (JCR Q1，IF=6.977，CCF-A)

4. 4. Yongshun Gong, Jinfeng Yi, Dong-dong Chen, Jian Zhang, Jiayu Zhou, Zhi-Hua Zhou. Inferring the Importance of Product Appearance with Semi-supervised Multi-modal Enhancement: A Step Towards the Screenless Retailing. ACM-MM-21. (CCF-A).

5. 5. Dong, Xiangjun, Yongshun Gong*, and Longbing Cao. e-RNSP: An efficient method for mining repetition negative sequential patterns. IEEE transactions on cybernetics (TCYB), 2020, 50(5): 2084-2096. (JCR Q1，IF=11.448，CCF-B)

6. 6. Yongshun Gong, Zhibin Li, Jian Zhang, Wei Liu, Bei Chen, Xiangjun Dong. A Spatial Missing Value Imputation Method for Multi-view Urban Statistical Data. in Proceedings of the International Joint Conferences on Artificial Intelligence (IJCAI-20). 2020, 1310-1316. （CCF-A）

7. 7. Yongshun Gong, Zhibin Li, Jian Zhang, Wei Liu, Jinfeng Yi. Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development. in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-20). 2020, 4020-4027. （CCF-A）

8. 8. Yongshun Gong, Zhibin Li, Jian Zhang, Wei Liu, Yu Zheng, Christina Kirsch. Network-wide Crowd Flow Prediction of Sydney Trains via customized Online Nonnegative Matrix Factorization. in Proceedings of the Conference on Information and Knowledge Management (CIKM-18), 2018, 1243-1252.（CCF-B）

9. 9. Xinming Gao^#, Yongshun Gong^#, Tiantian Xu, Jinhu Lv, etc. Towards to a Better Structure and Looser Constraint to Mine Negative Sequential Patterns. IEEE transactions on Neural Networks and Learning Systems (TNNLS), 2020, Accepted. (JCR Q1，IF=10.451，CCF-B)

10. 10. Ping Qiu^#, Yongshun Gong^#, Yuanhai Zhao, Longbing Cao, Chengqi Zhang, Xiangjun Dong. An Efficient Method for Modeling Non-occurring Behaviors by Negative Sequential Patterns with Loose Constraints.  IEEE transactions on Neural Networks and Learning Systems (TNNLS), 2021, Accepted. (JCR Q1，IF=10.451，CCF-B)

11. 11. Yu Cheng, Yongshun Gong, Yuansheng Liu*, Bosheng Song, Quan Zou, Molecular design in drug discovery: a comprehensive review of deep generative models, Briefings in Bioinformatics, 2021, in press. (JCR Q1，IF=11.622，CCF-B).

12. 12. Lu Zhang, Jingsong Xu, Yongshun Gong, Jian Zhang. Unsupervised Image and Text Fusion for Travel Information Enhancement. IEEE Transactions on Multimedia. 2021. (JCR Q1，IF=6.513，CCF-B)

13. 13. Dong, Xiangjun, Yongshun Gong, and Longbing Cao. F-NSP+: A fast negative sequential patterns mining method with self-adaptive data storage. Pattern Recognition (PR), 2018, 84: 13-27. JCR Q1，IF=7.741，CCF-B)

14. 14. Yongshun Gong, Tiantian Xu, etc. E-NSPFI: Ecient Mining Negative Sequential Pattern from both Frequent and Infrequent Positive Sequential Patterns. International Journal of Pattern Recognition and Artificial Intelligence, 2017, 31(02): 1-20. (IF=1.375，CCF-C)

15. 15. Zhibin Li, Jian Zhang, Qiang Wu, Yongshun Gong, Jinfeng Yi, Christina Kirsch. Sample Adaptive Multiple Kernel Learning for Failure Prediction of Railway Points. In Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (SIGKDD-19), 2019: 2848-2856. （CCF-A）

16. 16. Zhibin Li, Jian Zhang, Yongshun Gong, Yazhou Yao, Qiang Wu. Field-wise Learning for Multi-field Categorical Data, NeurIPS-20, 2020, 1-10（CCF-A）