Qiang Gao (高强)

Qiang Gao 

Contact:

NiceLab, Jingshi Building
Liutai Avenue, Wenjiang
Chengdu, Sichuan, CHINA, 611130

[中文页面]

Email: qianggao AT swufe DOT edu DOT cn

Biography

I am currently an Associate Professor, PhD supervisor at the Complex Laboratory of New Finance and Economics (NiceLab) and School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics (SWUFE). I (supervised by Prof. Fengli Zhang and Prof. Fan Zhou) received the Ph.D. degree in Software Engineering from University of Electronic Science and Technology of China (UESTC), Chengdu, China. I was a visiting scholar, supervised by Prof. Diego Klabjan and Prof. Goce Trajchevski, at Northwestern University from 2019 to 2020.

I now lead the GeoText group at NiceLab. My research focuses on Deep Learning+Mobility Mining (e.g., spatio-temporal data processing and location-based services (LBS)). Additionally, I am also partly interested in Regional/Urban Economics.

Github: Goole Scholar:

I am looking for motivated MSc, PhD, and Postdoc to join my research group, please feel free to contact me if you are interested in my research area. (课题组急需时空数据处理方向博士后2名,可以直接邮件给我!待遇优厚,具体请见(西南财经大学光华博士后):链接

Funds

  • the Fundamental Research Funds for the Central Universities. Grant No.JBK2406078 (PI, 2024.6-2025.6)
  • Science and Technology Program of Sichuan Province,Grant No.2023ZYD0145 (PI, 2023.12-2024.12)
  • Chengdu Science and Technology Program, Grant No.2023-JB00-00016-GX (PI, 2023-2025)
  • National Natural Science Foundation of China, Grant No.62102326 (PI, 2022.01-2024.12)
  • Natural Science Foundation of Sichuan Province, Grant No.2023NSFSC141 (PI, 2023.01-2024.12)
  • the Fundamental Research Funds for the Central Universities (PI, Key Grant, 2021)
  • Guanghua Talent Project of SWUFE (PI, 2023-2025)

Selected Publications (*Corresponding Author)

Journal Papers

  • 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. (Accepted)
  • 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. (Accepted)
  • 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. [Paper].
  • 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. [Paper].
  • 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. [Paper]
  • 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. [Paper].
  • Xin Yang, Metoh Adler LOUA, Meijun Wu, Li Huang, Qiang Gao. "Multi-granularity Stock Prediction with Sequential Three-way Decisions", Information Sciences, 2023. [Paper]
  • 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. [Paper]
  • Qiang Gao, Fan Zhou, Xin Yang and Guisong Liu. "When Friendship Meets Sequential Human Check-ins: Inferring Social Circles with Variational Mobility", Neurocomputing, 2023. [Paper]
  • 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. [Paper]
  • Qiang Gao, Wei Wang, Kunpeng Zhang, Xin Yang, Congcong Miao and Tianrui Li. "Self-supervised Representation Learning for Trip Recommendation", Knowledge-Based Systems, 2022. [Paper]
  • Qiang Gao, Zhipeng Luo, Diego Klabjan and Fengli Zhang. "Efficient Architecture Search for Continual Learning", IEEE Transactions on Neural Networks and Learning Systems, 2022. [Paper]
  • 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. [Paper]
  • 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. [Paper]
  • Qiang Gao, Fengli Zhang, Fuming Yao, Ailing Li, Lin Mei and Fan Zhou. "Adversarial Mobility Learning for Human Trajectory Classification", IEEE Access, 2020. [Paper]
  • 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. [Paper]

Conference Papers

  • 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). (Accepted)
  • 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). (Accepted)
  • 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). [Paper]
  • 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). [Paper]
  • 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). [Paper]
  • 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). [Paper]
  • 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). [Paper]
  • 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). [Paper] (Best Paper Award)
  • Li Huang, Hongmei Wu, Qiang Gao*, Guisong Liu. "ATTENTION LOCALNESS IN SHARED ENCODER-DECODER MODEL FOR TEXT SUMMARIZATION", 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023). [Paper]
  • Miaomiao Li, Jiaqi Zhu, Xin Yang, Yi Yang, Qiang Gao, Hongan Wang. "CL-WSTC: Continual Learning for Weakly Supervised Text Classification on the Internet", The 2023 ACM Web Conference, (WWW 2023). [Paper]
  • Jinyu Hong, Fan Zhou, Qiang Gao*, Kuang Ping, Kunpeng Zhang. "Mobility Prediction via Sequential Trajectory Disentanglement (Student Abstract)", The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI 2023). [Paper]
  • Yujie Li, Yuxuan Yang, Qiang Gao*, Xin Yang. "Cross-regional Fraud Detection via Continual Learning (Student Abstract)", The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI 2023). [Paper]
  • 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). [Paper]
  • Fan Zhou, Rongfan Li, Qiang Gao, Goce Trajcevski, Kunpeng Zhang, Ting Zhong. "Dynamic Manifold Learning for Land Deformation Forecasting", Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI),2022. [Paper]
  • Qiang Gao, Fan Zhou, Goce Trajcevski, Fengli Zhang, Xucheng Luo. "Adversity-based Social Circles Inference via Context-Aware Mobility", 2020 IEEE Global Communications Conference. [Paper]
  • 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). [Paper]
  • 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). [Paper][Code]
  • 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). [Paper][Code]
  • Fan Zhou, Qiang Gao, Goce Trajcevski, Kunpeng Zhang, Ting Zhong, Fengli Zhang, "Trajectory-User Linking via Variational AutoEncoder", IJCAI 2018. [Paper]
  • Qiang Gao, Fan Zhou, Kunpeng Zhang, Goce Trajcevski, Xuecheng Luo, Fengli Zhang, "Identifying Human Mobility via Trajectory Embeddings", IJCAI 2017. [Paper][Code]

Activities & Services

Conference & Journal Organization:

  • Program Committee Member, MDM, 2023/2024
  • Program Committee Member, KSEM, 2022-2024
  • Program Committee Member, SISAP, 2023/2024
  • Program Committee Member, ACM SIGSPATIAL, 2021-2024
  • Program Committee Member, EAI CollaborateCom, 2020
  • etc.

Reviewing for:

  • IEEE Transactions on Knowledge and Data Engineering
  • IEEE Transactions on Neural Networks and Learning Systems
  • IEEE Transactions on Systems Man Cybernetics-Systems
  • IEEE Transactions on Intelligent Transportation Systems
  • ACM Transactions on Knowledge Discovery from Data
  • Knowledge-Based Systems
  • Engineering Applications of Artificial Intelligence
  • Pattern Recognition
  • GeoInformatica
  • ACM Transactions on Asian and Low-Resource Language Information Processing
  • ACM Transactions on Spatial Algorithms and Systems
  • ISPRS International Journal of Geo-Information
  • Human-Centric Intelligent Systems
  • KDD (2018-2021/2024/2025)
  • ICME (2024)
  • IEEE BigData (2019-2022)
  • EAI CollaborateCom (2021)
  • Scientific Reports
  • etc.

Teaching:

Current Courses:

  • Deep Neural Networks for PhD students, Spring 2025 (Scheduled)
  • Deep Learning for Undergraduates, Fall 2024 (Scheduled)
  • Previous Courses:

  • Training Program of Artificial Intelligence for Undergraduates, Spring 2024
  • Advanced Machine Learning for Undergraduates, Fall 2022/2023
  • Training Program of Artificial Intelligence for Undergraduates, Fall 2022/2023
  • Machine Learning for Undergraduates, Spring 2022/2023
  • Artificial Intelligence for PhD students, Fall 2021
  • Data Mining for Undergraduates, Spring 2021, Fall 2021/2022

  • Update Time: Jul. 12, 2024