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. (欢迎优秀博士生/后、访问博士生/后加入!)

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

  • 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)
  • 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: Jun. 14, 2024