Perception & Modeling
How can agents represent changing spatial relationships and recognize previously unseen events in evolving environments?
Spatio-temporal representations · Dynamic graphs · Relational modeling
Qiang Gao · 高强
Spatio-Temporal Agents
in an Open World.
时空智能体 · 开放世界学习
I study open-world learning to build spatio-temporal agents that adapt to changing environments, acquire new knowledge, and make reliable decisions.
Two parallel research scenarios: Geospatial Intelligence and Financial Spatial Intelligence.
Professor · Ph.D. Supervisor
Southwestern University of Finance and Economics
SWUFE · Chengdu, China
01 / Research
Learning in a world that keeps changing.
How can spatio-temporal agents recognize the unknown, accumulate knowledge, and adapt as relationships, environments, and tasks evolve?
How a spatio-temporal agent works
Observe. Learn. Adapt.
Follow one agent through a changing environment. Introduce an unknown event to see how memory, reasoning, and action adapt.
01 / Environment
An evolving geographic space
Known locations and relationships.
02 / Agent
Memory, reasoning & action
01Perception Observe signals
02Spatio-temporal memory Connect past and present
03Reasoning & planning Evaluate alternatives
04Action Execute a task
Memory: established spatial relationships.
03 / Action & feedback
A plan grounded in memory
Inspect the environment before choosing a route.
- Observe
- Update memory
- Reason & plan
- Act
- Get feedback
- Learn
01 / 06
Observe the environment
Read locations, motion, and relationships across space and time.
Conceptual framework for future research; this visualization does not run a trained agent. Each cycle takes about 20 seconds. Playback starts only when requested.
Open-World Learning
How can agents adapt to distribution shifts and new tasks while retaining useful knowledge over time?
Continual learning · Adaptation · Knowledge transfer · Generalization
Reasoning & Action
How can agents use evolving knowledge and environmental feedback to reason and make decisions under uncertainty?
Spatial reasoning · Decisions under uncertainty · Trustworthy intelligence
From research foundations to agents
Existing foundations. My work spans trajectory understanding, dynamic graph learning, open-world anomaly detection, and continual learning.
Future direction. I aim to connect these foundations in spatio-temporal agents with memory, interaction, and decision-making capabilities, using open-world learning to support long-term adaptation.
Two parallel research scenarios
One research agenda. Two evolving spaces.
Spatio-temporal agents and open-world learning connect intelligence in geographic space with intelligence in financial relational space.
Geographic space
Geospatial Intelligence
地理空间智能
Understanding trajectories, locations, and interactions in evolving geographic environments, and learning as spatial patterns and tasks change.
Trajectory understanding · Spatial relationships · Dynamic environments
Selected geospatial research ↓Financial relational space
Financial Spatial Intelligence
金融空间智能
Understanding financial entities and their evolving relationships, and learning to support decisions as market patterns and dependencies change.
Enterprise networks · Market dependencies · Decisions under uncertainty
Selected financial research ↓Shared questions: recognizing the unknown, retaining knowledge, and adapting to change.
02 / Selected publications
Selected research.
Shared learning foundations, with selected work across two parallel scenarios: geospatial intelligence and financial spatial intelligence.
* Corresponding author · # Equal contribution
Shared learning foundations
Recognizing the unknown
Learning to Discover Anomalous Spatiotemporal Trajectory via Open-World State Space Model
OSSL combines a spatio-temporal state-space block with interacting adapters and open-world objectives to classify known trajectory behaviors and discover multiple unknown anomalous patterns.
Transferring knowledge across tasks
Enhancing Knowledge Transfer for Task Incremental Learning with Data-free Subnetwork
DSN combines neuron-level masks with replay generated without past training data, enabling knowledge transfer to new tasks and back to earlier tasks while mitigating forgetting.
Geospatial Intelligence 地理空间智能
Selected work on learning and modeling in changing geographic environments.
Continual spatio-temporal learning
Enhancing Fine-Grained Urban Flow Inference via Incremental Neural Operator
UNOI treats coarse- and fine-grained urban flows as continuous functions and combines neural operators with flow- and task-incremental learning to address scale sensitivity and catastrophic forgetting.
Modeling changing dependencies
Responsive Dynamic Graph Disentanglement for Metro Flow Forecasting
ReDyNet responds to changes in metro flow and external factors when constructing dynamic graphs, then uses information-bottleneck-based disentanglement to reduce redundant spatial signals.
Financial Spatial Intelligence 金融空间智能
Selected work on learning evolving relationships in financial systems under uncertainty.
Relational modeling & uncertainty
Relational Stock Selection via Probabilistic State Space Learning
RSSL models higher-order stock relationships with a tree structure and attention, then uses a probabilistic Kalman network to track changing stock dynamics with uncertainty estimates.
Risk-aware dependency learning
Progressive Dependency Representation Learning for Stock Ranking in Uncertain Risk Contrasting
PDU uses guidance from two environments to progressively model dependencies in historical trading signals while accounting for uncertain risk in stock ranking.
Explore all publications Chronological archive · * Corresponding author
2027
- Haolun Ding, Rong Zhang, Hang Zhang, Tuohang Li, Hongping Shu, Guisong Liu, and Qiang Gao. "A Semantic-Aware Dual-Stream Framework for Spatiotemporal Trajectory-User Linking", Pattern Recognition, 2027, 183: 115046. [Paper] Journal
2026
- Li Huang, Yanzhe Xie, Zizheng Wang, Qiang Gao*, Kunpeng Zhang, and Philip S. Yu. "Understanding Interactive Stock Dynamics via Sensitivity-aware Dependency Learning", IEEE Transactions on Knowledge and Data Engineering, 2026, 38(6): 4032–4047. [Paper] Journal
- Qiang Gao, Xiaolong Song, Li Huang, Goce Trajcevski, Fan Zhou and Xueqin Chen. "Alleviating Forgetting in Fine-Grained Urban Flow Inference via Incremental Neural Operator", ACM Transactions on Intelligent Systems and Technology, 2026. [Paper] Journal
- Ce Li, Qiang Gao, Kunpeng Zhang and Goce Trajcevski. "Impact of Incorporating Multiple Spatial Sources on Different Paradigms for Short-term Power Load Forecast", ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL). Accepted Conference
- 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", IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Conference
- 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 Conference on Artificial Intelligence (AAAI). Conference
- 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 Hate Video with Test-Time Adaptation", AAAI Conference on Artificial Intelligence (AAAI). Conference
- 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", ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). Conference
- 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", Annual Meeting of the Association for Computational Linguistics (ACL), Findings. Conference
- Li Huang, Letian Ning, Chaoran Liu, Rong Zhang, Qiang Gao*, and Goce Trajcevski. "Dual-Offset Trajectory Recovery on Roads with Dynamic Transition Probability", IEEE Transactions on Intelligent Transportation Systems, 2026, 27(8): 9606–9620. [Paper] Journal
- Tuohang Li, Jiajing Yu, Jianyong Zhao, Yanzhe Xie, Li Huang, Qiang Gao*. "Maximizing Mutual Information in Multi-View Representation Learning for Next POI Prediction", Neural Networks, 2026, 109733. Journal
- Qiang Gao, Letian Ning, Li Huang, Chaoran Liu, Xueqin Chen, and Fan Zhou. "Aligning Authentic Location Shares with Mobility Information Bottleneck", IEEE Transactions on Big Data, 2026, 12(3): 977–993. Journal
- 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, 2026, 195: 108258. [Paper] Journal
2025
- 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 International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL). Conference
- Li Huang, Yanzhe Xie, Qiang Gao*, Kunpeng Zhang, Guisong Liu, and Xueqin Chen. "Progressive Dependency Representation Learning for Stock Ranking in Uncertain Risk Contrasting", ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). [Paper] Conference
- 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", IEEE International Conference on Data Engineering (ICDE). [Paper] Conference
- Qiang Gao, Zizheng Wang, Li Huang, Goce Trajcevski, Guisong Liu, and Xueqin Chen. "Responsive Dynamic Graph Disentanglement for Metro Flow Forecasting", AAAI Conference on Artificial Intelligence (AAAI). [Paper] Oral Conference
- Li Huang, Haowen Liu, Qiang Gao*, Jiajing Yu, Guisong Liu, and Xueqin Chen. "Adversity-aware Few-shot Named Entity Recognition via Augmentation Learning", AAAI Conference on Artificial Intelligence (AAAI). [Paper] Oral Conference
- 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", Information Processing & Management, 2025. [Paper] Journal
- 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", Information Processing & Management, 2025. [Paper] Journal
- 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] Journal
- 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, 2025, 310: 112918. [Paper] Journal
- Qiang Gao, Zhengxiang Liu, Li Huang, Kunpeng Zhang, Jun Wang, and Guisong Liu. "Relational Stock Selection via Probabilistic State Space Learning", IEEE Transactions on Knowledge and Data Engineering, 2025, 37(2): 865–880. [Paper] Journal
- Qiang Gao. "Deep Learning and Semantic Dynamics of Human Mobility", Encyclopedia of GIS, Third Edition, Springer, 2025. Book Chapter
2024
- 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 International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL). [Paper] Conference
- Qiang Gao, Xiaolong Song, Li Huang, Goce Trajcevski, Fan Zhou, and Xueqin Chen. "Enhancing Fine-Grained Urban Flow Inference via Incremental Neural Operator", International Joint Conference on Artificial Intelligence (IJCAI). [Paper] Conference
- Kai Yang, Yi Yang, Qiang Gao, Ting Zhong, Yong Wang, and Fan Zhou. "Self-Explainable Next POI Recommendation", ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). [Paper] Conference
- Jinyu Hong, Ping Kuang, Qiang Gao*, Fan Zhou. "Disentanglement-Guided Spatial-Temporal Graph Neural Network for Metro Flow Forecasting (Student Abstract)", AAAI Conference on Artificial Intelligence (AAAI). [Paper] Conference
- Hongzhu Fu, Fan Zhou, Qing Guo, Qiang Gao*. "Spatial-Temporal Augmentation for Crime Prediction (Student Abstract)", AAAI Conference on Artificial Intelligence (AAAI). [Paper] Conference
- Jianfei Sun#, Qiang Gao#, Cong Wu, Yuxian Li, Jiacheng Wang, and Dusit Niyato. "Secure Resource Allocation via Constrained Deep Reinforcement Learning", International Conference on Machine Learning for Cyber Security (ML4CS). [Paper] Best Paper Conference
- Haoran Li, Qiang Gao, Hongmei Wu, and Li Huang. "Advancing Event Causality Identification via Heuristic Semantic Dependency Inquiry Network", Conference on Empirical Methods in Natural Language Processing (EMNLP). [Paper] Conference
- 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] Journal
- Yujie Li, Xin Yang, Qiang Gao, Hao Wang, Junbo Zhang, and Tianrui Li. "Cross-regional Fraud Detection via Continual Learning with Knowledge Transfer", IEEE Transactions on Knowledge and Data Engineering, 2024. [Paper] Journal
- 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. [Paper] Journal
- 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] Journal
- Nan Liu, Fengli Zhang, Qiang Gao and Xueqin Chen. "Contrastive Learning with Edge-wise Augmentation for Rumor Detection", International Journal of Intelligent Systems, 2024. [Paper] Journal
- 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, 2024, 36(5): 2126–2141. [Paper] Journal
- 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, 2024, 15(1): 1–25. [Paper] Journal
2023
- Qiang Gao, Xiaohan Wang, Chaoran Liu, Goce Trajcevski, Li Huang, Fan Zhou. "Open Anomalous Trajectory Recognition via Probabilistic Metric Learning", International Joint Conference on Artificial Intelligence (IJCAI). [Paper] Conference
- Qiang Gao, Hongzhu Fu, Yutao Wei, Li Huang, Xingmin Liu, and Guisong Liu. "Spatial-Temporal Diffusion Probabilistic Learning for Crime Prediction", International Conference on Knowledge Science, Engineering and Management (KSEM). [Paper] Conference
- Li Huang, Kai Liu, Chaoran Liu, Qiang Gao*, Xiao Zhou, and Guisong Liu. "HBay: Predicting Human Mobility via Hyperspherical Bayesian Learning", International Conference on Knowledge Science, Engineering and Management (KSEM). [Paper] Best Paper Conference
- Jinyu Hong, Fan Zhou, Qiang Gao*, Kuang Ping, Kunpeng Zhang. "Mobility Prediction via Sequential Trajectory Disentanglement (Student Abstract)", AAAI Conference on Artificial Intelligence (AAAI). [Paper] Conference
- Yujie Li, Yuxuan Yang, Qiang Gao*, Xin Yang. "Cross-regional Fraud Detection via Continual Learning (Student Abstract)", AAAI Conference on Artificial Intelligence (AAAI). [Paper] Conference
- Qiang Gao, Xiaojun Shan, Yuchen Zhang, and Fan Zhou. "Enhancing Knowledge Transfer for Task Incremental Learning with Data-free Subnetwork", Conference on Neural Information Processing Systems (NeurIPS). [Paper] Conference
- Miaomiao Li, Jiaqi Zhu, Xin Yang, Yi Yang, Qiang Gao, Hongan Wang. "CL-WSTC: Continual Learning for Weakly Supervised Text Classification on the Internet", ACM Web Conference (WWW). [Paper] Conference
- Li Huang, Hongmei Wu, Qiang Gao*, Guisong Liu. "Attention Localness in Shared Encoder-Decoder Model for Text Summarization", IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). [Paper] Conference
- 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] Journal
- 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] Journal
- 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] Journal
- Xin Yang, Metoh Adler LOUA, Meijun Wu, Li Huang, Qiang Gao. "Multi-granularity Stock Prediction with Sequential Three-way Decisions", Information Sciences, 2023. [Paper] Journal
- 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, 2023, 34(4): 1764–1776. [Paper] Journal
- Qiang Gao, Zhipeng Luo, Diego Klabjan and Fengli Zhang. "Efficient Architecture Search for Continual Learning", IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(11): 8555–8565. [Paper] Journal
2022
- Joojo Walker, Ting Zhong, Fengli Zhang, Qiang Gao and Fan Zhou. "Recommendation via Collaborative Diffusion Generative Model", International Conference on Knowledge Science, Engineering and Management (KSEM). [Paper] Conference
- Fan Zhou, Rongfan Li, Qiang Gao, Goce Trajcevski, Kunpeng Zhang, Ting Zhong. "Dynamic Manifold Learning for Land Deformation Forecasting", AAAI Conference on Artificial Intelligence (AAAI). [Paper] Conference
- 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] Journal
- 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] Journal
2021
- 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] Journal
2020
- Qiang Gao, Fan Zhou, Goce Trajcevski, Fengli Zhang, Xucheng Luo. "Adversity-based Social Circles Inference via Context-Aware Mobility", IEEE Global Communications Conference (IEEE GLOBECOM). [Paper] Conference
- Qiang Gao, Fengli Zhang, Fuming Yao, Ailing Li, Lin Mei and Fan Zhou. "Adversarial Mobility Learning for Human Trajectory Classification", IEEE Access, 2020. [Paper] Journal
2019
- Qiang Gao, Goce Trajcevski, Fan Zhou, Kunpeng Zhang, Ting Zhong, and Fengli Zhang. "DeepTrip: Adversarially Understanding Human Mobility for Trip Recommendation", ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL). [Paper] Conference
- Qiang Gao, Fan Zhou, Goce Trajcevski, Kunpeng Zhang, Ting Zhong and Fengli Zhang. "Predicting Human mobility via Variational Attention", International World Wide Web Conference (WWW). [Paper] Conference
2018
- Qiang Gao, Goce Trajcevski, Fan Zhou, Kunpeng Zhang, Ting Zhong and Fengli Zhang. "Trajectory-based Social Circle Inference", ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL). [Paper] Conference
- Fan Zhou, Qiang Gao, Goce Trajcevski, Kunpeng Zhang, Ting Zhong, Fengli Zhang. "Trajectory-User Linking via Variational AutoEncoder", International Joint Conference on Artificial Intelligence (IJCAI). [Paper] Conference
2017
- Qiang Gao, Fengli Zhang, Ruijin Wang, and Fan Zhou. "轨迹大数据：数据处理关键技术研究综述 (Trajectory Big Data: A Review of Key Technologies in Data Processing)", Journal of Software (软件学报), 2017, 28(4): 959–992. [Paper] Journal
- Qiang Gao, Fan Zhou, Kunpeng Zhang, Goce Trajcevski, Xuecheng Luo, Fengli Zhang. "Identifying Human Mobility via Trajectory Embeddings", International Joint Conference on Artificial Intelligence (IJCAI). [Paper] Conference
Projects & Demos
Chengdu Spatio-Temporal Visualization
Interactive map of Chengdu with 6 types of spatio-temporal data: taxi trajectories, POIs, metro stations, check-in data, weather dynamics, and flow lines — all animated over time on an AMap base layer.
Illustrative simulation · Generated data inspired by T-Drive, Geolife, and Brightkite patterns; not a replay of original datasets.
Open Demo
Beijing Taxi & POI Visualization
Spatio-temporal visualization of Beijing with 130 taxi trajectories (T-Drive pattern), 38 POIs, dynamic heatmaps, and trail effects. Time controller sweeps from 6:00 to 22:00 with playback speed control.
Illustrative simulation · Generated data inspired by T-Drive and Geolife patterns; not a replay of original datasets.
Open Demo04 / About & opportunities
Research, teaching, and collaboration.
About me
I am a Professor and Ph.D. Supervisor at the School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics (SWUFE), and a member of the Complex Laboratory of New Finance and Economics (NiceLab).
My affiliations also include the Engineering Research Center of Intelligent Finance, Ministry of Education, and the Kash Institute of Electronics and Information Industry.
Research funding
Research Funding
- National Natural Science Foundation of China (Youth Program), Grant No. 62102326 (PI, 2022.01–2024.12)
- Natural Science Foundation of Sichuan Province (Youth Program), Grant No. 2023NSFSC1411 (PI, 2023.01–2024.12)
- Sichuan Provincial Science and Technology Program, Grant No. 2023ZYD0145 (PI, 2023.07–2025.07)
- Chengdu “Unveiling the List and Taking the Lead” Program, Grant No. 2023-JB00-00016-GX (Lead of Participating Unit, 2023.11–2025.11)
- Fundamental Research Funds for the Central Universities, Grant No. JBK2406078 (PI, 2024.01–2024.12)
- Guanghua Talent Project of SWUFE – Young Faculty Growth Program (PI, 2022–2024)
- SWUFE Introduced Talent Research Startup Fund, Key Project (PI, 2021.01–2022.12)
Academic service
Activities & Services
Conference & Journal Organization
- Senior Program Committee Member, AAAI, 2027
- Posters and Short Papers Chair, ACM SIGSPATIAL, 2026
- Senior Program Committee Member, IJCAI, 2026
- Program Committee Member, AAAI, 2026
- Program Committee Member, ECAI, 2025
- Program Committee Member, IJCAI, 2025
- Program Committee Member, PAKDD, 2026
- Program Committee Member, ICDE (Demo track), 2025
- Session Chair, IJCAI, 2024
- Program Committee Member, MDM, 2023-2025
- Program Committee Member, KSEM, 2022-2025
- Program Committee Member, SISAP, 2023/2024
- Program Committee Member, ACM SIGSPATIAL, 2021-2026
- Program Committee Member, EAI CollaborateCom, 2020
Reviewing for
- NeurIPS 2025 · CVPR 2026
- IEEE T-KDE · IEEE T-NNLS
- IEEE T-SMC · IEEE T-ITS
- ACM TKDD · IEEE IoT
- Neural Networks · Information Fusion
- Knowledge-Based Systems
- Engineering Applications of AI
- Pattern Recognition
- Applied Soft Computing
- Expert Systems With Applications
- Neurocomputing · Information Sciences
- GeoInformatica
- ACM TALLIP · ACM TSAS
- ISPRS IJGI
- Human-Centric Intelligent Systems
- KDD (2018-2021/2024-2027), Outstanding Reviewer (KDD 2025)
- WWW (2025) · ICME (2024)
- IEEE BigData (2019-2022)
Teaching
Teaching
Current Courses
- Deep Neural Networks (PhD students)
- Deep Learning (Undergraduates)
- Artificial Intelligence Practice (Undergraduates)
Previous Courses
- Deep Neural Networks for PhD students, Spring 2026
- Deep Neural Networks for PhD students, Spring 2025
- Deep Learning for Undergraduates, Fall 2024/2025
- Advanced Machine Learning for Undergraduates, Fall 2022-2024
- Training Program of AI for Undergraduates, Fall 2022-2025
- Machine Learning for Undergraduates, Spring 2022-2024
- Artificial Intelligence for PhD students, Fall 2021
- Data Mining for Undergraduates, Spring 2021, Fall 2021/2022
NiceLab, Jingshi Building · Chengdu, Sichuan, China 611130