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    宫永顺

    • 副研究员 硕士生导师
    • 任职 : 山东省人工智能学会理事
    • 性别:男
    • 出生日期:1990-10-28
    • 毕业院校:悉尼科技大学 (UTS)
    • 学历:研究生(博士)毕业
    • 学位:工学博士学位
    • 在职信息:在职
    • 所在单位:软件学院
    • 办公地点:中国济南高新技术产业开发区舜华路1500号
    • 联系方式:ysgong@sdu.edu.cn
    • 电子邮箱:ysgong@sdu.edu.cn

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    个人简介

    宫永顺,博士,山东大学软件学院副研究员,硕士研究生导师,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(软科2021世界一流学科排名:计算机科学与工程世界第11位)。近三年主持国家自然科学基金、山东省优秀青年科学基金等项目5项。研究兴趣主要包含时空数据分析与挖掘、深度学习方法、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 TKDE(IF=9.235)、SIGKDD、IJCAI、AAAI、NeurIPS、ACM MM、CVPR和中科院JCR-1区期刊IEEE T-CYB(IF=19.118)、IEEE T-NNLS(IF=14.255)、IEEE T-MM(IF=8.182)、Pattern Recognition(IF=8.518)等发表论文三十余篇。

     [Google Scholar]: https://scholar.google.com/citations?user=WIHqungAAAAJ&hl


    学术交流:

    (1)20189-20193月于京东AI研究院担任Research Fellow,机器学习与推荐系统方向;

    (2)201911-20206月于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(http://www.sai.sdu.edu.cn/

    山东大学机器学习与数据挖掘实验室(https://time.sdu.edu.cn


    招生信息:

    招生类别

    招生专业(2023年度人工智能学硕、专硕可提前联系)

    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

    推荐系统;跨模态/迁移学习;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。


    主持或参与科研项目:

      

      1. 2023.01-2025.12,国家自然科学基金青年项目,主持。

      2. 2022.04-2025.04,山东省优秀青年科学基金,主持。 

      3. 2022.01-2024.12,山东省自然科学基金青年项目,主持。  

      4. 2022.01-2023.12,山东省科技型中小企业创新能力提升工程—面向开放复杂场景的智慧安防机器人研究,主持。

      5. 2022.01-2026.12,山东大学青年学者未来计划项目,山东大学人才计划,主持。

      6. 2022.11-2024.11,交通数据分析与挖掘北京市重点实验室开放课题,主持。

      7. 2022.01-2024.12,山东省自然科学基金重大基础研究项目,参与。


    What's new:

    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.


    代表性论文:

    *通讯作者;#共同一作

    [1]. 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=9.235CCF-A)

    [2]. 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=9.235CCF-A)

    [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), 2020. (IF=9.235CCF-A)

    [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]. 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JCR 1区,IF=19.118CCF-B)

    [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]. 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]. 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]. 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JCR 1区,IF=14.255CCF-B)

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

    [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. (中科院一区,IF=11.622CCF-B).

    [12]. Lu Zhang, Jingsong Xu, Yongshun Gong, Jian Zhang. Unsupervised Image and Text Fusion for Travel Information Enhancement. IEEE Transactions on Multimedia. 2021. (中科院JCR1区,IF=6.513CCF-B)

    [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. (中科院JCR 1区,IF=7.741CCF-B)

    [14]. 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

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