高强

新财经综合实验室

副教授、博士生导师

个人主页：https://qianggao.xyz

Email：qianggao@swufe.edu.cn

办公室：西南财经大学柳林校区经世楼D205

n 教师简介

高强，副教授，博士生导师(破格遴选)，管理学学士、工学博士，现任职于西南财经大学计算机与人工智能学院（新财经综合实验室）。数字经济与交叉科学创新研究院“可信人工智能研发与应用研究团队”负责人之一，新财经综合实验室空间智能与社会计算（GeoSoc）课题组负责人，四川省人工智能学会理事，喀什地区智库专家。2020年12月毕业于电子科技大学软件工程专业(硕博连读)，2019年-2020年国家公派联合培养博士生（导师为Goce Trajcevski和Diego Klabjan），于美国西北大学深度学习中心从事持续学习与神经网络架构优化研究。高强博士当前主要研究方向包括人群移动性表示学习、时空数据处理与深度学习以及区域经济等，已发表50余篇高水平学术论文，包括NeurIPS、IJCAI、AAAI、WWW、SIGIR、TKDE、TNNLS、TIST、ACM SIGSPATIAL等。目前主持国家自然科学基金青年基金项目、四川省自然科学基金青年基金项目和四川省科技厅中央引导地方自由探索项目，成都市“揭榜挂帅”项目合作单位负责人。参与国家自然科学基金项目4项、四川省科技计划3项等，合作出版教材1本，授权专利多项。高强博士目前担任多个国际领域会议程序委员和顶级期刊审稿人如TKDE、T-ITS、TNNLS、TKDD、TSAS、GeoInformatica、KBS、PR、NC、EAAI、KDD、ICME、ACM SIGSPATIAL和MDM等, IJCAI 2024 Session Chair。2021年获得ACM Chengdu Chapter优秀博士论文奖，2023年获得KSEM 唯一最佳论文奖。2023年、2024年本科生论文优秀指导老师。西南财经大学第四期“SWUFE研学计划”导师。

欢迎对深度学习、时空数据挖掘、区域经济等方向感兴趣的学生报考我的硕士、博士研究生（热爱科研，熟悉深度学习、PyTorch或者Tensorflow优先），欢迎博士后加入课题组。欢迎全校优秀本科生加入研学计划（专业不限）。

n 研究领域

人群移动性表示学习、时空数据处理与深度学习、区域经济

n 工作经历

2021.03-2022.11 西南财经大学 讲师，硕士生导师

2022.12-2023.11 西南财经大学 副教授，硕士生导师

2023.12-至今 西南财经大学 副教授，博（硕）士生导师

n 讲授课程

本科生：深度学习、人工智能实训

博士生：深度神经网络

n 研究成果

代表性学术论文(*通讯作者)

[1]. Qiang Gao, Zizheng Wang, Li Huang, Goce Trajcevski, Kunpeng Zhang, and Xueqin Chen. "Enhancing Dependency Dynamics in Traffic Flow Forecasting via Graph Risk Bootstrap ", The 32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2024). (GIS顶会)

[2]. Yujie Li, Xin Yang, Qiang Gao, Hao Wang, Junbo Zhang, Tianrui Li. "Cross-regional Fraud Detection via Continual Learning with Knowledge Transfer", IEEE Transactions on Knowledge and Data Engineering, 2024. (CCF A)

[3]. Nan Liu, Fengli Zhang, Qiang Gao and Xueqin Chen. "Contrastive Learning with Edge-wise Augmentation for Rumor Detection", International Journal of Intelligent Systems, 2024. (SCI二区)

[4]. Li Huang, Pei Li, Qiang Gao*, Guisong Liu, Zhipeng Luo, and Tianrui Li. "Diffusion Probabilistic Model for Bike-sharing Demand Recovery with Factual Knowledge Fusion", Neural Networks, 2024. (SCI一区)

[5]. Zhipeng Luo, Qiang Gao, Yazhou He, Hongjun Wang, Milos Hauskrecht, and Tianrui Li. "Hierarchical Active Learning with Label Proportions on Data Regions", IEEE Transactions on Knowledge and Data Engineering, 2024. (CCF A)

[6]. Qiang Gao*, Xinzhu Zhou, Li Huang, Kunpeng Zhang, Siyuan Liu, and Fan Zhou. "Relational Fusion-based Stock Selection with Neural Recursive Ordinary Differential Equation Networks", Information Fusion, 2024. (SCI一区)

[7]. Qiang Gao, Xiaolong Song, Li Huang, Goce Trajcevski, Fan Zhou, and Xueqin Chen. "Enhancing Fine-Grained Urban Flow Inference via Incremental Neural Operator", The 33rd International Joint Conference on Artificial Intelligence (IJCAI-24). (CCF A)

[8]. Kai Yang, Yi Yang, Qiang Gao, Ting Zhong, Yong Wang, and Fan Zhou. "Self-Explainable Next POI Recommendation", The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2024). (CCF A)

[9]. Jinyu Hong, Ping Kuang, Qiang Gao*, Fan Zhou. "Disentanglement-Guided Spatial-Temporal Graph Neural Network for Metro Flow Forecasting (Student Abstract)", The Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI 2024).

[10]. Hongzhu Fu, Fan Zhou, Qing Guo, Qiang Gao*. "Spatial-Temporal Augmentation for Crime Prediction (Student Abstract)", The Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI 2024).

[11]. Qiang Gao, Xiaojun Shan, Yuchen Zhang, and Fan Zhou. "Enhancing Knowledge Transfer for Task Incremental Learning with Data-free Subnetwork", Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS)，2023. (CCF A)

[12]. Qiang Gao, Jinyu Hong, Xovee Xu, Ping Kuang, Fan Zhou, and Goce Trajcevski. "Predicting Human Mobility via Self-supervised Disentanglement Learning", IEEE Transactions on Knowledge and Data Engineering, 2023. (CCF A)

[13]. Xovee Xu, Zhiyuan Wang, Qiang Gao, Ting Zhong, Bei Hui, Fan Zhou, and Goce Trajcevski. "Spatial-Temporal Contrasting for Fine-Grained Urban Flow Inference", IEEE Transactions on Big Data, 2023. (SCI)

[14]. Qiang Gao, Xiaohan Wang, Chaoran Liu, Goce Trajcevski, Li Huang, Fan Zhou. "Open Anomalous Trajectory Recognition via Probabilistic Metric Learning", The 32nd International Joint Conference on Artificial Intelligence (IJCAI), 2023. (CCF A)

[15]. Qiang Gao, Hongzhu Fu, Kunpeng Zhang, Goce Trajcevski, Xu Teng, and Fan Zhou. "Inferring Real Mobility in Presence of Fake Check-ins Data", ACM Transactions on Intelligent Systems and Technology, 2023. (SCI)

[16]. Li Huang, Kai Liu, Chaoran Liu, Qiang Gao*, Xiao Zhou, and Guisong Liu "HBay: Predicting Human Mobility via Hyperspherical Bayesian Learning", The 16th International Conference on Knowledge Science, Engineering and Management (KSEM), 2023. (CCF C，唯一最佳论文)

[17]. Qiang Gao, Hongzhu Fu, Yutao Wei, Li Huang, Xingmin Liu, and Guisong Liu. "Spatial-Temporal Diffusion Probabilistic Learning for Crime Prediction", The 16th International Conference on Knowledge Science, Engineering and Management (KSEM), 2023. (CCF C)

[18]. 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)

[19]. Xin Yang, Metoh Adler LOUA, Meijun Wu, Li Huang, Qiang Gao. "Multi-granularity Stock Prediction with Sequential Three-way Decisions", Information Sciences, 2023. (SCI一区)

[20]. 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)

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

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

[23]. 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)

[24]. 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)

[25]. 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)

[26]. 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)

[27]. 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)

[28]. 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)

[29]. 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)

[30]. 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)

[31]. 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)

[32]. 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)

[33]. 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顶会)

[34]. 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)

[35]. 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顶会)

[36]. Fan Zhou, Qiang Gao, Goce Trajcevski, Kunpeng Zhang, Ting Zhong, and Fengli Zhang, Trajectory-User Linking via Variational AutoEncoder, Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), 2018. (CCF A)

[37]. Qiang Gao, Fan Zhou, Kunpeng Zhang, Goce Trajcevski, Xuecheng Luo, Fengli Zhang, Identifying Human Mobility via Trajectory Embeddings, Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), 2017. (CCF A)

[38]. 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 T1)

主要科研项目

[1]. 国家自然科学基金青年基金项目，No.6210232，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，No.2023NSFSC141，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]. 四川省科技厅中央引导地方自由探索项目，No.2023ZYD0145，时空语义理解可解释机理研究，2023-7至2025-7，在研，主持

[4]. 成都市揭榜挂帅项目，No.2023ZYD0145，面向科技金融的人工智能评价系统开发及应用示范，2023-11至2025-11，在研，单位负责人

[5]. 中央高校基本科研业务费，No.JBK2406078增量时空知识图表示学习研究，2024.01-2024.12，在研，主持

[6]. 光华英才工程-青年教师成长计划项目，2022-2024，在研，主持

[7]. 西南财经大学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2022-12，已结题，主持

[8]. 四川省重点研发计划，类脑智能芯片低功耗学习方法研究, 2022-1至2023-12, 已结题，主研

[9]. 国家自然科学基金面上项目，基于脉冲编码的类脑联邦学习方法研究，2024-01-01至2027-12-31，在研，主研

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

[11]. 国家自然科学基金企业创新发展联合基金项目，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, 已结题, 参与

[12]. 国家自然科学基金面上项目，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