Dr. Qiang Gao 高强

Book Chapter

• Qiang Gao. "Deep Learning and Semantic Dynamics of Human Mobility", Encyclopedia of GIS, Third edition, Springer, 2025. [chapter]

Conference Papers

• Yanzhe Xie, Tuohang Li, Heng Wang, Yujun Ge, Li Huang, Qiang Gao*. "Frequency-Aware Dynamic Graph Learning via Pseudo-Spectral Decomposition for Metro Flow Forecasting", ICASSP 2026. Accepted
• Li Huang, Yujie Wu, Xiaolong Song, Qiang Gao*, Goce Trajcevski, and Xueqin Chen. "Enhancing Urban Region Representation via Adaptive Risk-aware Consensus Learning", ACM SIGSPATIAL 2025. Accepted
• Qiang Gao, Zizheng Wang, Li Huang, Goce Trajcevski, Guisong Liu, and Xueqin Chen. "Responsive Dynamic Graph Disentanglement for Metro Flow Forecasting", AAAI 2025. [Paper] Oral
• Qiang Gao, Zizheng Wang, Li Huang, Goce Trajcevski, Kunpeng Zhang, and Xueqin Chen. "Enhancing Dependency Dynamics in Traffic Flow Forecasting via Graph Risk Bootstrap", ACM SIGSPATIAL 2024. [Paper]
• Qiang Gao, Xiaolong Song, Li Huang, Goce Trajcevski, Fan Zhou, and Xueqin Chen. "Enhancing Fine-Grained Urban Flow Inference via Incremental Neural Operator", IJCAI 2024. [Paper]
• Kai Yang, Yi Yang, Qiang Gao, Ting Zhong, Yong Wang, and Fan Zhou. "Self-Explainable Next POI Recommendation", SIGIR 2024. [Paper]
• Jinyu Hong, Ping Kuang, Qiang Gao*, Fan Zhou. "Disentanglement-Guided Spatial-Temporal Graph Neural Network for Metro Flow Forecasting (Student Abstract)", AAAI 2024. [Paper]
• Hongzhu Fu, Fan Zhou, Qing Guo, Qiang Gao*. "Spatial-Temporal Augmentation for Crime Prediction (Student Abstract)", AAAI 2024. [Paper]
• Qiang Gao, Xiaohan Wang, Chaoran Liu, Goce Trajcevski, Li Huang, Fan Zhou. "Open Anomalous Trajectory Recognition via Probabilistic Metric Learning", IJCAI 2023. [Paper]
• Qiang Gao, Hongzhu Fu, Yutao Wei, Li Huang, Xingmin Liu, and Guisong Liu. "Spatial-Temporal Diffusion Probabilistic Learning for Crime Prediction", KSEM 2023. [Paper]
• Li Huang, Kai Liu, Chaoran Liu, Qiang Gao*, Xiao Zhou, and Guisong Liu. "HBay: Predicting Human Mobility via Hyperspherical Bayesian Learning", KSEM 2023. [Paper] Best Paper
• Jinyu Hong, Fan Zhou, Qiang Gao*, Kuang Ping, Kunpeng Zhang. "Mobility Prediction via Sequential Trajectory Disentanglement (Student Abstract)", AAAI 2023. [Paper]
• Joojo Walker, Ting Zhong, Fengli Zhang, Qiang Gao and Fan Zhou. "Recommendation via Collaborative Diffusion Generative Model", KSEM 2022. [Paper]
• Fan Zhou, Rongfan Li, Qiang Gao, Goce Trajcevski, Kunpeng Zhang, Ting Zhong. "Dynamic Manifold Learning for Land Deformation Forecasting", AAAI 2022. [Paper]
• Qiang Gao, Fan Zhou, Goce Trajcevski, Fengli Zhang, Xucheng Luo. "Adversity-based Social Circles Inference via Context-Aware Mobility", IEEE GLOBECOM 2020. [Paper]
• Qiang Gao, Goce Trajcevski, Fan Zhou, Kunpeng Zhang, Ting Zhong, and Fengli Zhang. "DeepTrip: Adversarially Understanding Human Mobility for Trip Recommendation", ACM SIGSPATIAL 2019. [Paper]
• Qiang Gao, Fan Zhou, Goce Trajcevski, Kunpeng Zhang, Ting Zhong and Fengli Zhang. "Predicting Human mobility via Variational Attention", WWW 2019. [Paper] [Code]
• Qiang Gao, Goce Trajcevski, Fan Zhou, Kunpeng Zhang, Ting Zhong and Fengli Zhang. "Trajectory-based Social Circle Inference", 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]

Journal Papers

• Li Huang, Letian Ning, Chaoran Liu, Rong Zhang, Qiang Gao*, and Goce Trajcevski. "Dual-Offset Trajectory Recovery on Roads with Dynamic Transition Probability", IEEE T-ITS, 2026. Accepted
• Qiang Gao, Letian Ning, Li Huang, Chaoran Liu, Xueqin Chen, and Fan Zhou. "Aligning Authentic Location Shares with Mobility Information Bottleneck", IEEE T-Big Data, 2025. Accepted
• Haolun Ding, Zhengwen Fu, Rong Zhang, Li Huang, Xuan Luo, and Qiang Gao*. "Probabilistic Bayesian Learning with Long-Tail Awareness for Trajectory-User Linking", Neural Networks, 2025. [Paper]
• Hongzhu Fu, Yutao Wei, Gege Chen, Xing He, Qiang Gao, and Fan Zhou. "Augmented Graph Information Bottleneck with Type-Aware Periodicity Heterogeneity for Explainable Crime Prediction", IP&M, 2025. [Paper]
• Qiang Gao, Chaoran Liu, Li Huang, Goce Trajcevski, Qing Guo, and Fan Zhou. "Learning to Discover Anomalous Spatiotemporal Trajectory via Open-World State Space Model", Knowledge-Based Systems, 2024. [Paper]
• 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. [Paper]
• Qiang Gao, Jinyu Hong, Xovee Xu, Ping Kuang, Fan Zhou, and Goce Trajcevski. "Predicting Human Mobility via Self-supervised Disentanglement Learning", IEEE T-KDE, 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 T-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 TIST, 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]
• 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 T-NNLS, 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]
• Qiang Gao, Fengli Zhang, Ruijin Wang, and Fan Zhou. "Trajectory big data: a review of key technologies in data processing", Journal of Software, 2016, 28(4): 959-992. [Paper]

Conference Papers

• Li Huang, Haowen Liu, Qiang Gao*, Jiajing Yu, Guisong Liu, and Xueqin Chen. "Adversity-aware Few-shot Named Entity Recognition via Augmentation Learning", AAAI 2025. [Paper] Oral
• Qiang Gao, Xiaojun Shan, Yuchen Zhang, and Fan Zhou. "Enhancing Knowledge Transfer for Task Incremental Learning with Data-free Subnetwork", NeurIPS 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", WWW 2023. [Paper]
• Yujie Li, Yuxuan Yang, Qiang Gao*, Xin Yang. "Cross-regional Fraud Detection via Continual Learning (Student Abstract)", AAAI 2023. [Paper]

Journal Papers

• Yujie Li, Xin Yang, Qiang Gao, Hao Wang, Junbo Zhang, and Tianrui Li. "Cross-regional Fraud Detection via Continual Learning with Knowledge Transfer", IEEE T-KDE, 2024. [Paper]
• Zhipeng Luo, Qiang Gao, Yazhou He, Hongjun Wang, Milos Hauskrecht, and Tianrui Li. "Hierarchical Active Learning with Label Proportions on Data Regions", IEEE T-KDE, 2024. [Paper]
• Qiang Gao, Zhipeng Luo, Diego Klabjan and Fengli Zhang. "Efficient Architecture Search for Continual Learning", IEEE T-NNLS, 2022. [Paper]

Conference Papers

• Yanzhe Xie, Li Huang, Qiang Gao*, Xueqin Chen, Fan Zhou, Kunpeng Zhang. "Beyond Graph Priors: A Co-evolving Framework under Uncertainty for Enterprise Resilience Assessment", AAAI 2026. Accepted
• Jiao Li, Jian Lang, Xikai Tang, Wenzheng Shu, Ting Zhong, Qiang Gao, Yong Wang, Leiting Chen, and Fan Zhou. "Shedding the Facades, Connecting the Domains: Detecting Shifting Multimodal Malicious Content with Test-Time Adaptation", AAAI 2026. Accepted
• Jian Lang, Rongpei Hong, Ting Zhong, Leiting Chen, Qiang Gao, Fan Zhou. "From Shallow Humor to Metaphor: Towards Label-Free Harmful Meme Detection via LMM Agent Self-Improvement", KDD 2026. Accepted
• Jian Lang, Rongpei Hong, Meihui Zhong, Kaiju Li, Ting Zhong, Qiang Gao, and Fan Zhou. "LEAF: Towards Lightweight Explainable Hateful Video Detection via Self-Grounding CoT Guided Stage-Wise Distillation", ACL 2026 Findings. Accepted
• Li Huang, Yanzhe Xie, Qiang Gao*, Kunpeng Zhang, Guisong Liu, and Xueqin Chen. "Progressive Dependency Representation Learning for Stock Ranking in Uncertain Risk Contrasting", KDD 2025. [Paper]
• Xueqin Chen, Xiaoyu Huang, Qiang Gao*, Li Huang, Jiaojing Yu, and Guisong Liu. "Birds of a Feather: Enhancing Multimodal Fake News Detection via Multi-Element Retrieval", ICDE 2025. [Paper]
• Jianfei Sun#, Qiang Gao#, Cong Wu, Yuxian Li, Jiacheng Wang, and Dusit Niyato. "Secure Resource Allocation via Constrained Deep Reinforcement Learning", ML4CS 2024. [Paper] Best Paper
• Haoran Li, Qiang Gao, Hongmei Wu, and Li Huang. "Advancing Event Causality Identification via Heuristic Semantic Dependency Inquiry Network", EMNLP 2024. [Paper]
• Li Huang, Hongmei Wu, Qiang Gao*, Guisong Liu. "Attention Localness in Shared Encoder-Decoder Model for Text Summarization", ICASSP 2023. [Paper]

Journal Papers

• Li Huang, Yanzhe Xie, Zizheng Wang, Qiang Gao*, Kunpeng Zhang, and Philip S. Yu. "Understanding Interactive Stock Dynamics via Sensitivity-aware Dependency Learning", IEEE T-KDE, 2026. Accepted
• Yanlong Huang, Wenxin Tai, Fan Zhou, Qiang Gao, Ting Zhong, and Kunpeng Zhang. "Extracting Key Insights from Earnings Call Transcript via Information-Theoretic Contrastive Learning", IP&M, 2025. [Paper]
• Xueqin Chen, Xiaoyu Huang, Qiang Gao*, Li Huang, and Guisong Liu. "Enhancing Text-centric Fake News Detection via External Knowledge Distillation from LLMs", Neural Networks, 2025. [Paper]
• Qiang Gao, Zhengxiang Liu, Li Huang, Kunpeng Zhang, Jun Wang, and Guisong Liu. "Relational Stock Selection via Probabilistic State Space Learning", IEEE T-KDE, 2024. [Paper]
• 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]
• Nan Liu, Fengli Zhang, Qiang Gao and Xueqin Chen. "Contrastive Learning with Edge-wise Augmentation for Rumor Detection", Int. J. of Intelligent Systems, 2024. [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]