宫永顺（1990），博士，山东大学齐鲁青年学者特聘教授，博士生导师，泰山学者青年专家，山东省优青，入选山东大学青年学者未来计划。博士毕业于悉尼科技大学，计算机科学与工程。主持国家自然科学基金（面上、青年）、山东省优秀青年科学基金等项目10余项。研究兴趣主要包含时空数据分析与挖掘、图像处理、智慧交通、智慧城市计算、深度学习、机器学习理论、自然语言处理等。迄今已在中国计算机学会CCF-A类推荐期刊/会议IEEE TPAMI、Artificial Intelligence (山东大学首篇)、IEEE TKDE、SIGKDD、IJCAI、AAAI、NeurIPS、ACM MM、CVPR和中科院SCI-1区期刊IEEE T-CYB、IEEE T-NNLS、IEEE T-MM、Pattern Recognition等发表论文八十余篇。长期担任TKDE、TNNLS、TCYB、TMM等CCF-A类/SCI-1 区期刊审稿人，近年来连续担任KDD、ICML、NeurIPS、ICLR、IJCAI、AAAI、CVPR、ICCV 等CCF-A 类会议的程序委员会委员。获得青岛市科技进步二等奖、山东省人工智能科学技术进步一等奖；担任《计算机科学与探索》青年编委；担任CCF-人工智能与模式识别专委会执委、秘书；CCF-智慧交通分会执委；中国人工智能学会智能服务专委会委员；山东省人工智能学会常务理事。

【Google Scholar】

工作经历：

2025.01 - 至今：软件学院，齐鲁青年学者特聘教授

2023.09 - 2024.12：软件学院，教授（破格）

2021.02-2023.08：软件学院，副研究员

学术交流：

2018.09 - 2019.03: 京东AI研究院担任Research Fellow，机器学习、时空预测与推荐系统方向；

2019.11 - 2020.06: 微软亚洲研究院担任Research Fellow，研究方向自然语义分析。

所在团队：

山东大学人工智能研究中心

山东大学机器学习与数据挖掘实验室

招生信息：（2025年入学生可通过邮件联系）

招生类别	招生专业
学术型硕士	软件工程、人工智能 (*)
专业型硕士	软件工程、人工智能 (*)
优先录取	（1）有很强的自主学习能力和科研热情；（2）具有扎实的数学基础和英文写作能力；（3）精通至少一门编程语言: Python、C++、JAVA、MATLAB

*另: 招收若干本科生科研助手，有意向在本科就读阶段接触研究生科研工作、发表高水平学术论文或竞赛指导可咨询联系。

 

主要研究内容：

时空数据挖掘	时空流量预测、时空表征学习、时空异常检测等
城市计算	城市交通预测；城市感知计算；目标检测、跟踪等
深度学习	图像处理；3D点云；大语言模型；推荐系统；多媒体计算等
机器学习方法	低质数据分类、聚类；张量分解；跨模态/迁移学习等

*课题组在主要研究内容与方向中均发表过高水平论文。

主要主持或参与科研项目：

  

  1. 2025-2028：《面向开放数据场景的XX时空XX研究》，国家自然科学基金面上项目，主持

  2. 2023-2025：《面向城市流量预测的时空XXXX》，国家自然科学基金青年项目，主持

  3. 2024-2027：《开放场景下XXXX数据XX和方法研究》，山东省重大基础研究项目，山大联合主持

  4. 2025-2029：山东大学齐鲁青年学者项目，山东大学人才计划，主持

  5. 2023-2025：泰山学者青年专家项目，主持

  6. 2022-2025：《XXX交通流量预测》，山东省优秀青年科学基金，主持

  7. 2022-2024：《复杂时空网络下的XXXX交通流量分布预测研究》，山东省自然科学基金青年项目，主持 

  8. 2022-2023：《面向开放复杂场景的XXXX机器人研究》，山东省科技中小企业创新能力提升，主持

  9. 2024-2025：《动静态交通XXXX关键技术研究与应用》，青岛市科技计划关键技术攻关项目，主持

  10. 2022-2026：山东大学青年学者未来计划项目，山东大学人才计划，主持

  11. 2022-2024：交通数据分析与挖掘北京市重点实验室开放课题，主持

  12. 2022-2025：国家重点研发计划课题，骨干

  13. 2022-2024：山东省自然科学基金重大基础研究项目，骨干

  14. 2023-2026：山东省重点研发计划 (重大科技创新工程)，骨干

What's new:

01/2025: One paper is accepted to IEEE TKDE (CCF-A)

12/2024: Two papers are accepted to AAAI 2025 (CCF-A)

10/2024: One paper is accepted to Expert Systems with Applications (SCI-1)

07/2024: Two papers are accepted to CIKM 2024 (CCF-B)

05/2024: Two papers are accepted to KDD 2024 (CCF-A)

04/2024: Three papers are accepted to IJCAI 2024 (CCF-A)

03/2024: One paper is accepted to IEEE TMM (SCI-1)

03/2024: One paper is accepted to DASFAA 2024 (CCF-B)

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

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，山东大学首篇，期刊每年仅发表百余篇论文)

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 (SCI-1)

03/2023: One paper is accepted to IEEE TPAMI (CCF-A)

03/2023: One paper is accepted to IEEE TSUSC (SCI-2)

01/2023: One paper is accepted to TOIS (CCF-A)

10/2022: One paper is accepted to IEEE TKDE (CCF-A)

08/2022: One paper is accepted to IEEE TKDE (CCF-A)

03/2022: Two papers are accepted to ICME 2022 (CCF-B)

03/2022: One paper is accepted to CVPR 2022 (CCF-A)

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 (SCI-1)

08/2021: One paper is accepted to ACM-MM 2021 Industrial track (main Proceeding) (CCF-A)

08/2021: One paper is accepted to Briefings in Bioinformatics (SCI-1)

04/2021: One paper is accepted to IEEE TKDE (CCF-A)

03/2021: One paper is accepted to IEEE TMM (SCI-1)

02/2021: One Paper is accepted to IEEE TNNLS (SCI-1)

代表性论文：

*通讯作者；#共同一作

[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]. 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]. 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]. 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)

[5]. 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)

[6]. 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)

[7]. 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)

[8]. 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）

[9]. 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)

[10]. 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)

[11]. 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)

[12]. 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)

[13]. 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）

[14]. 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）

[15]. 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）

[16]. 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)

[17]. 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)

[18]. Xu Zhang^#, Yongshun Gong^#, Chengqi Zhang, Xiaoming Wu, etc. Spatio-temporal fusion and contrastive learning for urban flow prediction. Knowledge-Based Systems. 2023. (共同一作，中科院SCI-1区, IF=8.8)

[19]. Xu Zhang^#, Yongshun Gong^#, Xinxin Zhang, etc.  Mask-and Contrast-Enhanced Spatio-Temporal Learning for Urban Flow Prediction. in Proceedings of the Conference on Information and Knowledge Management (CIKM-23), 2023（CCF-B）

[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）

邮箱 : ysgong@sdu.edu.cn