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 80 papers in top journals and refereed conference proceedings for Artificial Intelligence, Pattern Recognition, IEEE T-PAMI, AIJ, 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, 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，中科院SCI-1区，CCF-A)

2. [2]. Xiaoyu Li, Yongshun Gong*, Wei Liu, Yilong Yin, Yu Zheng, Liqiang Nie. Dual-track Spatio-temporal Learning for Urban Flow Prediction with Adaptive Normalization. Artificial Intelligence (AIJ), 2024. (IF=14.4，CCF-A)

3. [3]. Yongshun Gong, Tiantian He, Meng Chen, Bin Wang, Liqiang Nie, Yilong Yin. Spatio-Temporal Enhanced Contrastive and Contextual Learning for Weather Forecasting. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2024. (IF=9.235，CCF-A)

4. [4] Xiaoyu Li, Yitian Zhang, Guodong Long, Yupeng Hu, Wenpeng Lu, Meng Chen, Chengqi Zhang, and Yongshun Gong*. Adaptive Traffic Forecasting on Daily Basis: A Spatio-Temporal Context Learning Approach. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2025. (IF=9.235，CCF-A)

5. [5]. An Yang, Zhibin Li, Xiaoyu Li, Wei Liu, Xinghao Yang, Haoliang Sun, Meng Chen, Yu Zheng, Yongshun Gong*. Spatio-Temporal Multivariate Probabilistic Modeling for Traffic Prediction. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2025. (IF=9.235，CCF-A)

6. [6]. Min Yang, Xiaoyu Li, Bin Xu, Xiushan Nie, Muming Zhao, Chengqi Zhang, Yu Zheng, Yongshun Gong*. STDA: Spatio-Temporal Deviation Alignment Learning for Cross-city Fine-grained Urban Flow Inference. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2025. (IF=9.235，CCF-A)

7. [7]. Hao Qu, Yongshun Gong*, Meng Chen, Junbo Zhang, Yu Zheng, Yilong Yin. Forecasting Fine-grained Urban Flow via Spatio-temporal Contrastive Self-Supervision. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022. (IF=9.235，CCF-A)

8. [8]. 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. (IF=9.235，CCF-A)

9. [9]. 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), 2020. (IF=9.235，CCF-A)

10. [10]. Zheng, Xiao, Xiaoshui Huang, Guofeng Mei, Yuenan Hou, Zhaoyang Lyu, Bo Dai, Wanli Ouyang, and Yongshun Gong*. Point Cloud Pre-training with Diffusion Models. CVPR 2024. （CCF-A）

11. [11]. Jin Huang, Yongshun Gong*, Lu Zhang, Jian Zhang, Liqiang Nie, Yilong Yin. Modeling Multiple Aesthetic Views for Series Photo Selection. IEEE Transactions on Multimedia. 2023. (中科院SCI-1区，IF=8.18)

12. [12]. Yongshun Gong, Xue Dong, Jian Zhang, Meng Chen. Latent Evolution Model for Change Point Detection in Time-varying Networks. Information Sciences. 2023. (中科院SCI-1区，IF=8.1)

13. [13]. 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)

14. [14]. 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. (中科院SCI-1区，IF=19.118，CCF-B)

15. [15]. 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）

16. [16]. 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）

17. [17]. 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）

18. [18]. 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. (共同一作，中科院SCI-1区，IF=14.255，CCF-B)

19. [19]. 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. (共同一作，中科院SCI-1区，IF=14.255，CCF-B)

20. [20]. 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）