※教师简介

高强，现任4066金沙(中国)有限公司官网（新财经综合实验室）副教授、硕士生导师。2020年12月毕业于电子科技大学，获得工学博士学位，美国西北大学联合培养博士生。高强博士主要研究方向涉及机器学习、深度学习、时空数据挖掘和财经科技等，在国际顶级期刊、顶级学术会议等发表30余篇学术论文，包括IJCAI、AAAI、WWW、TNNLS、Information Fusion和ACM SIGSPATIAL等。主持国家自然科学基金青年基金1项、四川省自然科学基金青年基金1项，参与国家自然科学基金项目3项、四川省科技计划3项等，参编教材1部，申请专利多项。高强博士目前是中国计算机学会员工分会工作组成员。2022年获得ACM Chengdu Chapter优秀博士论文奖。

欢迎对深度学习、时空数据挖掘、金融科技等方向感兴趣的青年才俊报考我的研究生，我会尽快回复邮件并安排面谈。欢迎优秀本科生加入我的课题小组，目前课题小组与爱荷华州立大学、马里兰大学、代尔夫特理工大学等高校建立了长期合作关系。

※研究领域

深度学习

数据挖掘

时空数据处理

财经科技

※讲授课程

本科生： 数据挖掘、机器学习导论、高级机器学习、人工智能实训

※研究成果

代表性学术论文(*通讯作者)：

[1] Li Huang, Hongmei Wu, Qiang Gao*, and Guisong Liu, "ATTENTION LOCALNESS IN SHARED ENCODER-DECODER MODEL FOR TEXT SUMMARIZATION", IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023. (CCF B类会议，财大B类)

[2] Miaomiao Li, Jiaqi Zhu, Xin Yang, Yi Yang, Qiang Gao and Hongan Wang, "CL-WSTC: Continual Learning for Weakly Supervised Text Classification on the Internet", The Web Conference (WWW), 2023. (CCF A类会议，财大A类)

[3] Jinyu Hong, Fan Zhou, Qiang Gao*, Ping Kuang, and Kunpeng Zhang. "Mobility Prediction via Sequential Trajectory Disentanglement", The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2023.

[4] Yujie Li, Yuxuan Yang, Qiang Gao*, and Xin Yang. "Cross-regional Fraud Detection via Continual Learning", The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2023.

[5] Xin Yang, Metoh Adler LOUA, Meijun Wu, Li Huang, and Qiang Gao. "Multi-granularity Stock Prediction with Sequential Three-way Decisions", Knowledge-Based Systems,2023. (SCI一区，财大A类)

[6] Qiang Gao, Wei Wang, Li Huang, Xin Yang, Tianrui Li and Hamido Fujita. "Dual-grained Human Mobility Learning for Location-aware Trip Recommendation with Spatial-temporal Graph Knowledge Fusion", Information Fusion, 2023. (SCI一区，Top期刊，财大A类)

[7] Qiang Gao, Fan Zhou, Xin Yang, and Guisong Liu. "When Friendship Meets Sequential Human Check-ins: Inferring Social Circles with Variational Mobility", Neurocomputing, 2023. (SCI二区，财大A类)

[8] Qiang Gao, Fan Zhou, Ting Zhong, Goce Trajcevski, Xin Yang, and Tianrui Li. "Contextual Spatio-Temporal Graph Representation Learning for Reinforced Human Mobility Mining", Information Sciences, 2022. (SCI一区，财大A类)

[9] Qiang Gao, Wei Wang, Kunpeng Zhang, Xin Yang, Congcong Miao, and Tianrui Li. "Self-supervised Representation Learning for Trip Recommendation", Knowledge-Based Systems, 2022. (SCI一区，财大A类)

[10] Joojo Walker, Ting Zhong, Fengli Zhang, Qiang Gao, and Fan Zhou. "Recommendation via Collaborative Diffusion Generative Model", The 15th International Conference on Knowledge Science, Engineering and Management, KSEM 2022. (CCF C类会议)

[11] Qiang Gao, Zhipeng Luo, Diego Klabjan, and Fengli Zhang. "Efficient Architecture Search for Continual Learning", IEEE Transactions on Neural Networks and Learning Systems, 2022. (SCI一区，Top期刊，CCF B类，财大A类)

[12] Fan Zhou, Rongfan Li, Qiang Gao, Goce Trajcevski, Kunpeng Zhang, and Ting Zhong. Dynamic Manifold Learning for Land Deformation Forecasting, Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), 2022 (CCF A类会议)

[13] Fan Zhou, Yurou Dai, Qiang Gao, Pengyu Wang, and Ting Zhong. Self-Supervised Human Mobility Learning for Next Location Prediction and Trajectory Classification, Knowledge-Based Systems, 2021. (SCI一区，财大A类)

[14] Qiang Gao, Fan Zhou, Goce Trajcevski, Kunpeng Zhang, Ting Zhong, and Fengli Zhang, Adversarial Human Trajectory Learning for Trip Recommendation, IEEE Transactions on Neural Networks and Learning Systems, 2021. (SCI一区，Top期刊，CCF B类)

[15] Qiang Gao, Fan Zhou, Goce Trajcevski, Fengli Zhang, and Xucheng Luo, Adversity-based Social Circles Inference via Context-Aware Mobility, 2020 IEEE Global Communications Conference (GlobeCom), 2020. (CCF C类，通信领域旗舰会议)

[16] Qiang Gao, Goce Trajcevski, Fan Zhou, Kunpeng Zhang, Ting Zhong, and Fengli Zhang. DeepTrip: Adversarially Understanding Human Mobility for Trip Recommendation. Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL), 2019. (GIS顶级会议)

[17] Qiang Gao, Fan Zhou, Goce Trajcevski, Kunpeng Zhang, Ting Zhong, and Fengli Zhang, Predicting Human mobility via Variational Attention, The World Wide Web Conference (WWW) 2019. (CCF A类会议，进入最佳论文评审)

[18] Qiang Gao, Goce Trajcevski, Fan Zhou, Kunpeng Zhang, Ting Zhong, and Fengli Zhang, Trajectory-based Social Circle Inference, Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL) 2018. (GIS顶级会议)

[19] Fan Zhou, Qiang Gao, Goce Trajcevski, Kunpeng Zhang, Ting Zhong, and Fengli Zhang, Trajectory-User Linking via Variational AutoEncoder, IJCAI 2018. (CCF A类会议)

[20] Qiang Gao, Fan Zhou, Kunpeng Zhang, Goce Trajcevski, Xuecheng Luo, Fengli Zhang, Identifying Human Mobility via Trajectory Embeddings, IJCAI 2017.(CCF A类会议)

[21] 高强, 张凤荔, 王瑞锦, & 周帆. (2017). 轨迹大数据: 数据处理关键技术研究综述. 软件学报, 28(4), 959-992. (中文CCF A类)

主要科研项目：

[1]国家自然科学基金青年基金项目，62102326，面向人类移动性理解的深度轨迹表示学习研究，2022-1至2024-12, 在研，主持

[2]四川省自然科学基金青年基金项目，2023NSFSC1411，开放世界环境下的人群移动性表示学习研究，2023-1至2024-12, 在研，主持

[3]光华英才工程-青年教师成长计划项目，2022，在研，主持

[4]4066金沙引进人才科研启动资助项目（重点项目），移动性表征动态持续学习研究，2021-1至2021-12，已结题，主持

[5]四川省重点研发计划，2022YFG0314, 类脑智能芯片低功耗学习方法研究, 2022-1至2023-12, 在研，主研（排名第二）

[6]国家自然科学基金面上项目，62072077，图学习中的可解释性研究，2021-01至2024-12，主研，在研

[7]国家自然科学基金企业创新发展联合基金项目，U19B2028，面向复杂场景认知推理的知识图谱构建与计算方法研究，2020-01至2023-12, 在研, 参与

[8]国家自然科学基金面上项目，61772117，基于逻辑规则和表示学习的知识图谱关系推理方法与应用研究，2018-01至2021-12，已结题，参与

※学术活动

l Program Committee Member：ACM SIGSPATIAL 2021/2022/2023、IEEE MDM 2023、KSEM 2022/2023、EAI CollaborateCom 2020、etc.

l Reviewer: TKDE、TNNLS、GeoInformatica、KDD、ACM Transactions on Spatial Algorithms and Systems、etc.