Data Mining

Multimodal LLMs

AI4Science

2013-09--2018-07 Institution of Automation, Chinese Academy of Sciences PhD in Artificial Intelligence

2009-09--2013-07 Yanshan University BSc in Electronic Science

2025-02~present    Institute of Automation, Chinese Academy of Sciences    Professor

2022-07~2025-02   Institute of Automation, Chinese Academy of Sciences    Associate Professor

2021-03~2022-06   Institute of Automation, Chinese Academy of Sciences    Assistant Professor

2018-11~2021-03   Tsinghua University                                                            Postdoctoral Researcher

2018-07~2020-09   RealAI                                                                                 R&D Director

• Yunyue Su, Jiahui Chen, Zao Jiang, Zhenyi Zhong, Liang Wang, Qiang Liu#, Zhaoxiang Zhang. SpectraLLM: Uncovering the Ability of LLMs for Molecule Structure Elucidation from Multi-Spectra. International Conference on Learning Representations (ICLR), 2026.
• Mingqing Zhang, Zhuoning Xu, Peijie Wang, Rongji Li, Liang Wang, Qiang Liu#, Jian Xu, Xuyao Zhang, Shu Wu, Liang Wang. AgriDoctor: A Multimodal Intelligent Assistant for Agriculture. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2026.
• Liang Wang, Shaozhen Liu, Yu Rong, Deli Zhao, Qiang Liu#, Shu Wu, Liang Wang. MolSpectra: Pre-training 3D Molecular Representation with Multi-modal Energy Spectra. International Conference on Learning Representations (ICLR), 2025.
• Xiangxin Zhou, Yi Xiao, Haowei Lin, Xinheng He, Jiaqi Guan, Yang Wang, Qiang Liu, Feng Zhou, Liang Wang, Jianzhu Ma. Integrating Protein Dynamics into Structure-Based Drug Design via Full-Atom Stochastic Flows. International Conference on Learning Representations (ICLR), 2025.
• Liang Wang, Qiang Liu#, Shaozhen Liu, Xin Sun, Shu Wu, Liang Wang. Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-shot Molecular Property Prediction. Conference on Neural Information Processing Systems (NeurIPS), 2024.
• Dingshuo Chen, Zhixun Li, Yuyan Ni, Guibin Zhang, Ding Wang, Qiang Liu, Shu Wu, Jeffrey Xu Yu, Liang Wang. Beyond Efficiency: Molecular Data Pruning for Enhanced Generalization. Conference on Neural Information Processing Systems (NeurIPS), 2024.
• Xin Sun, Liang Wang, Qiang Liu#, Shu Wu, Zilei Wang, Liang Wang. DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2024.
• Liang Wang, Hao Fu, Shu Wu, Qiang Liu#, Xuelei Tan, Fangsheng Huang, Mengdi Zhang, Wei Wu. CAMLO: Cross-Attentive Multi-View Network for Long-Term Origin-Destination Flow Prediction. SIAM International Conference on Data Mining (SDM), 2024.
• Haisong Gong, Qiang Liu, Shu Wu, Liang Wang. Text-Guided Molecule Generation with Diffusion Language Model. AAAI Conference on Artificial Intelligence (AAAI), 2024.
• Dingshuo Chen, Yanqiao Zhu, Jieyu Zhang, Yuanqi Du, Zhixun Li, Qiang Liu, Shu Wu, Liang Wang. Uncovering Neural Scaling Law in Molecular Representation Learning. Conference on Neural Information Processing Systems (NeurIPS), 2023.
• Qiang Liu*#, Yingtao Luo*, Yuntian Chen, Wenbo Hu, Tian Tian, Jun Zhu. Physics-Guided Discovery of Highly Nonlinear Parametric Partial Differential Equations. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2023.
• Jiaqi Guan, Xiangxin Zhou, Yuwei Yang, Yu Bao, Jian Peng, Jianzhu Ma, Qiang Liu, Liang Wang, Quanquan Gu. DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design. International Conference on Machine Learning (ICML), 2023.
• Fenyu Hu, Liping Wang, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan. GraphDIVE: Graph Classification by Mixture of Diverse Experts. International Joint Conference on Artificial Intelligence (IJCAI), 2022.

• Xinlong Chen, Yue Ding, Weihong Lin, Jingyun Hua, Linli Yao, Yang Shi, Bozhou Li, Yuanxing Zhang, Qiang Liu#, Pengfei Wan, Liang Wang. AVoCaDO: An Audiovisual Video Captioner Driven by Temporal Orchestration. International Conference on Learning Representations (ICLR), 2026.
• Xinlong Chen, Yuanxing Zhang, Yushuo Guan, Weihong Lin, Zekun Wang, Bohan Zeng, Yang Shi, Sihan Yang, Qiang Liu#, Pengfei Wan, Liang Wang. VidBridge-R1: Bridging QA and Captioning for RL-based Video Understanding Models with Intermediate Proxy Tasks. International Conference on Learning Representations (ICLR), 2026.
• Bowen Fang, Wen Ye, Yunyue Su, Jinghao Zhang, Qiang Liu, Yesheng Liu, Xin Sun, Shu Wu, Jiabing Yang, Baole Wei, Liang Wang. ToolWeaver: Weaving Collaborative Semantics for Scalable Tool Use in Large Language Models. International Conference on Learning Representations (ICLR), 2026.
• Mengqi Zhang, Zisheng Zhou, Xiaotian Ye, Qiang Liu, Zhaochun Ren, Zhumin Chen, Pengjie Ren. Disentangling Knowledge Representations for Large Language Model Editing. International Conference on Learning Representations (ICLR), 2026.
• Yichi Zhang, Yue Ding, Jingwen Yang, Tianwei Luo, Dongbai Li, Ranjie Duan, Qiang Liu, Hang Su, Yinpeng Dong, Jun Zhu. Towards Safe Reasoning in Large Reasoning Models via Corrective Intervention. International Conference on Learning Representations (ICLR), 2026.
• Leqi Zheng, Jiajun Zhang, Canzhi Chen, Chaokun Wang, Hongwei Li, Yuying Li, Yaoxin Mao, Shannan Yan, Zixin Song, Zhiyuan Feng, Zhaolu Kang, Zirong Chen, Hang Zhang, Qiang Liu, Liang Wang, Ziyang Liu. What Should I Cite? A RAG Benchmark for Academic Citation Prediction. The Web Conference (WWW), 2026.
• Liuji Chen, Hao Gao, Jinghao Zhang, Qiang Liu#, Shu Wu, Liang Wang. SEEM: Exploiting Black-Box Text Attacks to Manipulate Tool Selection. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2026.
• Xin Sun, Zhongqi Chen, Qiang Liu, Shu Wu, Bowen Song, Weiqiang Wang, Zilei Wang, Liang Wang. Predict the Retrieval! Test Time Adaptation for Retrieval Augmented Generation. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2026.
• Han Huang, Yuqi Huo, Zijia Zhao, Haoyu Lu, Shu Wu, Bingning Wang, Qiang Liu, Weipeng Chen, Liang Wang. AITQE: An Adaptive Image-Text Quality Enhancer for Scalable MLLM Pretraining. IEEE Transactions on Circuits and Systems for Video Technology (IEEE TCSVT), 2026.
• Junfei Wu, Jian Guan, Kaituo Feng, Qiang Liu, Shu Wu, Liang Wang, Wei Wu, Tieniu Tan. Reinforcing Spatial Reasoning in Vision-Language Models with Interwoven Thinking and Visual Drawing. Conference on Neural Information Processing Systems (NeurIPS), 2025.
• Qiang Liu, Xinlong Chen, Yue Ding, Bowen Song, Weiqiang Wang, Shu Wu, Liang Wang. Attention-guided Self-reflection for Zero-shot Hallucination Detection in Large Language Models. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2025.
• Junfei Wu, Yue Ding, Guofan Liu, Tianze Xia, Ziyue Huang, Dianbo Sui, Qiang Liu#, Shu Wu, Liang Wang, Tieniu Tan. SHARP: Steering Hallucination in LVLMs via Representation Engineering. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2025.
• Haitian Zhong, Yuhuan Liu, Ziyang Xu, Guofan Liu, Qiang Liu, Shu Wu, Zhe Zhao, Liang Wang, Tieniu Tan. REACT: Representation Extraction And Controllable Tuning to Overcome Overfitting in LLM Knowledge Editing. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2025.
• Mengqi Zhang, Bowen Fang, Qiang Liu#, Xiaotian Ye, Shu Wu, Pengjie Ren, Zhumin Chen, Liang Wang. KELE: Residual Knowledge Erasure for Enhanced Multi-hop Reasoning in Knowledge Editing. Conference on Empirical Methods in Natural Language Processing Findings (EMNLP Findings), 2025.
• Yunyue Su, Zhang Jinshuai, Bowen Fang, Wen Ye, Jinghao Zhang, Bowen Song, Weiqiang Wang, Qiang Liu, Liang Wang. Toolscaler: Scalable Generative Tool Calling via Structure-Aware Semantic Tokenization. Conference on Empirical Methods in Natural Language Processing Findings (EMNLP Findings), 2025.
• Wen Ye, Zhaocheng Liu, Gui Yuwei, Tingyu Yuan, Yunyue Su, Bowen Fang, Chaoyang Zhao, Qiang Liu, Liang Wang. GenPilot: A Multi-Agent System for Test-Time Prompt Optimization in Image Generation. Conference on Empirical Methods in Natural Language Processing Findings (EMNLP Findings), 2025.
• Xin Sun, Jianan Xie, Zhongqi Chen, Qiang Liu#, Shu Wu, Yuehe Chen, Bowen Song, Weiqiang Wang, Zilei Wang, Liang Wang. Divide-Then-Align: Honest Alignment based on the Knowledge Boundary of RAG. Annual Meeting of the Association for Computational Linguistics (ACL), 2025.
• Jinghao Zhang, Yuting Liu, Wenjie Wang, Qiang Liu, Shu Wu, Liang Wang, Tat-Seng Chua. Personalized Text Generation with Contrastive Activation Steering. Annual Meeting of the Association for Computational Linguistics (ACL), 2025.
• Xinlong Chen, Yuanxing Zhang, Qiang Liu#, Junfei Wu, Fuzheng Zhang, Tieniu Tan. Mixture of Decoding: An Attention-Inspired Adaptive Decoding Strategy to Mitigate Hallucinations in Large Vision-Language Models. Annual Meeting of the Association for Computational Linguistics Findings (ACL Findings), 2025.
• Xinlong Chen, Yuanxing Zhang, Chongling Rao, Yushuo Guan, Jiaheng Liu, Fuzheng Zhang, Chengru Song, Qiang Liu#, Di Zhang, Tieniu Tan. VidCapBench: A Comprehensive Benchmark of Video Captioning for Controllable Text-to-Video Generation. Annual Meeting of the Association for Computational Linguistics Findings (ACL Findings), 2025.
• Mengqi Zhang, Xiaotian Ye, Qiang Liu, Pengjie Ren, Shu Wu, Zhumin Chen. Uncovering Overfitting in Large Language Model Editing. International Conference on Learning Representations (ICLR), 2025.
• Han Huang, Haitian Zhong, Tao Yu, Qiang Liu#, Shu Wu, Liang Wang, Tieniu Tan. VLKEB: A Large Vision-Language Model Knowledge Editing Benchmark. Conference on Neural Information Processing Systems (NeurIPS), 2024.
• Mengqi Zhang, Xiaotian Ye, Qiang Liu, Pengjie Ren, Shu Wu, Zhumin Chen. Knowledge Graph Enhanced Large Language Model Editing. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024.
• Junfei Wu, Qiang Liu, Ding Wang, Jinghao Zhang, Shu Wu, Liang Wang, Tieniu Tan. Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models. Annual Meeting of the Association for Computational Linguistics Findings (ACL Findings), 2024.

• Yuwei Xia, Mengqi Zhang, Qiang Liu, Liang Wang, Shu Wu, Xiaoyu Zhang. MetaTKG++: Learning Evolving Factor Enhanced Meta-knowledge for Temporal Knowledge Graph Reasoning. Pattern Recognition (PR), 2024.
• Liang Wang, Xiang Tao, Qiang Liu, Shu Wu, Liang Wang. Rethinking Graph Masked Autoencoders through Alignment and Uniformity. AAAI Conference on Artificial Intelligence (AAAI), 2024.
• Yuwei Xia, Ding Wang, Qiang Liu#, Liang Wang, Shu Wu, Xiao-Yu Zhang. Chain-of-History Reasoning for Temporal Knowledge Graph Forecasting. Annual Meeting of the Association for Computational Linguistics Findings (ACL Findings), 2024.
• Zeyu Cui, Zekun Li, Shu Wu, Xiaoyu Zhang, Qiang Liu, Liang Wang, Mengmeng Ai. DyGCN: Efficient Dynamic Graph Embedding with Graph Convolutional Network. IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), 2024.
• Zhixun Li, Liang Wang, Xin Sun, Yifan Luo, Yanqiao Zhu, Dingshuo Chen, Yingtao Luo, Xiangxin Zhou, Qiang Liu#, Shu Wu, Liang Wang, Jeffrey Xu Yu. GSLB: The Graph Structure Learning Benchmark. Conference on Neural Information Processing Systems (NeurIPS), 2023.
• Mengqi Zhang, Yuwei Xia, Qiang Liu, Shu Wu, Liang Wang. Learning Latent Relations for Temporal Knowledge Graph Reasoning. Annual Meeting of the Association for Computational Linguistics (ACL), 2023.
• Mengqi Zhang, Yuwei Xia, Qiang Liu#, Shu Wu, Liang Wang. Learning Long- and Short-term Representations for Temporal Knowledge Graph Reasoning. The Web Conference (WWW), 2023.
• Yuwei Xia, Mengqi Zhang, Qiang Liu#, Shu Wu, Xiao-Yu Zhang. MetaTKG: Learning Evolutionary Meta-Knowledge for Temporal Knowledge Graph Reasoning. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022.
• Yanqiao Zhu, Yichen Xu, Hejie Cui, Carl Yang, Qiang Liu, Shu Wu. Structure-Enhanced Heterogeneous Graph Contrastive Learning. SIAM International Conference on Data Mining (SDM), 2022.
• Yanqiao Zhu, Yichen Xu, Qiang Liu, Shu Wu. An Empirical Study of Graph Contrastive Learning. Conference on Neural Information Processing Systems (NeurIPS), 2021.
• Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang. Graph Contrastive Learning with Adaptive Augmentation. The Web Conference (WWW), 2021.

• Guofan Liu, Yannan Sun, Qiang Liu, Xiaolong Zhang, Zhe Zhao, Junfei Wu, Shu Wu, Liang Wang. Mixture-of-Experts Multi-View Large Vision-Language Models for Multimodal Fake News Detection. IEEE Transactions on Multimedia (IEEE TMM), 2026.
• Qiang Liu, Xiang Tao, Liang Wang, Shu Wu, Liang Wang. Robust and Generalizable Rumor Detection with Semantic Evolving Graph Masked Autoencoder. Pattern Recognition (PR), 2026.
• Qiang Liu*, Mingqing Zhang*, Xiang Tao, Shu Wu, Liang Wang. SINCon: Mitigate LLM-Generated Malicious Message Injection Attack for Rumor Detection. Annual Meeting of the Association for Computational Linguistics (ACL), 2025.
• Zhao Tong, Yimeng Gu, Huidong Liu, Qiang Liu, Shu Wu, Haichao Shi, Xiao-Yu Zhang. Generate First, Then Sample: Enhancing Fake News Detection with LLM-Augmented Reinforced Sampling. Annual Meeting of the Association for Computational Linguistics (ACL), 2025.
• Qiang Liu, Junfei Wu, Shu Wu, Liang Wang. Out-of-distribution Evidence-aware Fake News Detection via Dual Adversarial Debiasing. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2024.
• Junfei Wu, Weizhi Xu, Qiang Liu#, Shu Wu and Liang Wang. Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural Networks. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2024.
• Guofan Liu, Jinghao Zhang, Qiang Liu#, Junfei Wu, Shu Wu, Liang Wang. Uni-Modal Event-Agnostic Knowledge Distillation for Multimodal Fake News Detection. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2024.
• Huanhuan Ma, Weizhi Xu, Yifan Wei, Liuji Chen, Liang Wang, Qiang Liu, Shu Wu, Liang Wang. EX-FEVER: A Dataset for Multi-hop Explainable Fact Verification. Annual Meeting of the Association for Computational Linguistics Findings (ACL Findings), 2024.
• Xiang Tao, Liang Wang, Qiang Liu#, Shu Wu, Liang Wang. Semantic Evolvement Enhanced Graph Autoencoder for Rumor Detection. The Web Conference (WWW), 2024.
• Huanhuan Ma, Jinghao Zhang, Qiang Liu, Shu Wu, Liang Wang. Interpretable Multimodal Out-of-Context Detection with Soft Logic Regularization. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024.
• Haisong Gong, Weizhi Xu, Shu Wu, Qiang Liu, Liang Wang. Heterogeneous Graph Reasoning for Fact Checking over Texts and Tables. AAAI Conference on Artificial Intelligence (AAAI), 2024.
• Qiang Liu*, Weizhi Xu*, Shu Wu, Liang Wang. Counterfactual Debiasing for Fact Verification. Annual Meeting of the Association for Computational Linguistics (ACL), 2023.
• Junfei Wu, Qiang Liu, Weizhi Xu, Shu Wu. Bias Mitigation for Evidence-aware Fake News Detection by Causal Intervention. International ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR), 2022.
• Weizhi Xu, Junfei Wu, Qiang Liu, Shu Wu, Liang Wang. Evidence-aware Fake News Detection with Graph Neural Networks. The Web Conference (WWW), 2022.
• Qiang Liu, Feng Yu, Shu Wu, Liang Wang. Mining Significant Microblogs for Misinformation Identification: An Attention-based Approach. ACM Transactions on Intelligent Systems and Technology (ACM TIST), 2018.
• Feng Yu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan. A Convolutional Approach for Misinformation Identification. International Joint Conference on Artificial Intelligence (IJCAI), 2017.

• Yuting Liu, Jinghao Zhang, Yizhou Dang, Yuliang Liang, Qiang Liu#, Guibing Guo, Jianzhe Zhao, Xingwei Wang. CoRA: Collaborative Information Perception by Large Language Model's Weights for Recommendation. AAAI Conference on Artificial Intelligence (AAAI), 2025.
• Wenbo Hu, Xin Sun, Qiang Liu#, Le Wu, Liang Wang. Uncertainty Calibration for Counterfactual Propensity Estimation in Recommendation. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2025.
• Liang Wang, Shu Wu, Qiang Liu, Yanqiao Zhu, Xiang Tao, Mengdi Zhang, Liang Wang. Bi-Level Graph Structure Learning for Next POI Recommendation. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2024.
• Jinghao Zhang, Guofan Liu, Qiang Liu#, Shu Wu, Liang Wang. Modality-Balanced Learning for Multimedia Recommendation. ACM International Conference on Multimedia (MM), 2024.
• Jinghao Zhang, Yuting Liu, Qiang Liu, Shu Wu, Guibing Guo, Liang Wang. Stealthy Attack on Large Language Model based Recommendation. Annual Meeting of the Association for Computational Linguistics (ACL), 2024.
• Jinghao Zhang, Yanqiao Zhu, Qiang Liu, Mengqi Zhang, Shu Wu, Liang Wang. Latent Structure Mining with Contrastive Modality Fusion for Multimedia Recommendation. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2023.
• Jinghao Zhang, Qiang Liu, Shu Wu, Liang Wang. Mining Stable Preferences: Adaptive Modality Decorrelation for Multimedia Recommendation. International ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR), 2023.
• Mengqi Zhang, Shu Wu, Xueli Yu, Qiang Liu, Liang Wang. Dynamic Graph Neural Networks for Sequential Recommendation. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2023.
• Jinghao Zhang, Yanqiao Zhu, Qiang Liu#, Shu Wu, Shuhui Wang, Liang Wang. Mining Latent Structures for Multimedia Recommendation. ACM International Conference on Multimedia (MM), 2021.
• Yingtao Luo, Qiang Liu#, Zhaocheng Liu. STAN: Spatio-Temporal Attention Network for Next Location Recommendation. The Web Conference (WWW), 2021.
• Qiang Cui, Shu Wu, Qiang Liu, Wen Zhong, Liang Wang. MV-RNN: A Multi-view Recurrent Neural Network for Sequential Recommendation. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2020.
• Qiang Liu, Shu Wu, Liang Wang. DeepStyle: Learning User Preferences for Visual Recommendation. International ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR), 2017.
• Qiang Liu, Shu Wu, Liang Wang. Multi-behavioral Sequential Prediction with Recurrent Log-bilinear Model. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2017.
• Qiang Liu, Shu Wu, Diyi Wang, Zhaokang Li, Liang Wang. Context-aware Sequential Recommendation. IEEE International Conference on Data Mining (ICDM), 2016.
• Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan. Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts. AAAI Conference on Artificial Intelligence (AAAI), 2016.
• Qiang Liu*, Feng Yu*, Shu Wu, Liang Wang, Tieniu Tan. A Dynamic Recurrent Model for Next Basket Recommendation. International ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR), 2016.
• Shu Wu, Qiang Liu, Liang Wang, Tieniu Tan. Contextual Operation for Recommender Systems. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2016.
• Qiang Liu, Shu Wu, Liang Wang. COT: Contextual Operating Tensor for Context-aware Recommender Systems. AAAI Conference on Artificial Intelligence (AAAI), 2015.
• Qiang Liu, Feng Yu, Shu Wu, Liang Wang. A Convolutional Click Prediction Model. ACM International Conference on Information and Knowledge Management (CIKM), 2015.
• Qiang Liu, Shu Wu, Liang Wang. Collaborative Prediction for Multi-entity Interaction with Hierarchical Representation. ACM International Conference on Information and Knowledge Management (CIKM), 2015.

• Yingtao Luo, Zhixun Li, Qiang Liu#, Jun Zhu. Fairness without Demographics through Learning Graph of Gradients. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2025.
• Zhixun Li, Yushun Dong, Qiang Liu, Jeffrey Xu Yu. Rethinking Fair Graph Neural Networks from Re-balancing. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2024.
• Liping Wang, Qiang Liu, Mengqi Zhang, Yaxuan Hu, Shu Wu, Liang Wang. Stage-Aware Hierarchical Attentive Relational Network for Diagnosis Prediction. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2024.
• Qiang Liu, Yingtao Luo, Shu Wu, Zhen Zhang, Xiangnan Yue, Hong Jin, Liang Wang. RMT-Net: Reject-aware Multi-Task Network for Modeling Missing-not-at-random Data in Financial Credit Scoring. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2023.
• Yingtao Luo, Zhaocheng Liu, Qiang Liu#. Deep Stable Representation Learning on Electronic Health Records. IEEE International Conference on Data Mining (ICDM), 2022.
• Zhixun Li, Dingshuo Chen, Qiang Liu, Shu Wu. The Devil is in the Conflict: Disentangled Information Graph Neural Networks for Fraud Detection. IEEE International Conference on Data Mining (ICDM), 2022.
• Qiang Liu, Zhaocheng Liu, Haoli Zhang, Yuntian Chen, Jun Zhu. Mining Cross Features for Financial Credit Risk Assessment. ACM International Conference on Information and Knowledge Management (CIKM), 2021.
• Jingyi Wang, Qiang Liu, Zhaocheng Liu, Shu Wu. Towards Accurate and Interpretable Sequential Prediction: A CNN & Attention-Based Feature Extractor. ACM International Conference on Information and Knowledge Management (CIKM), 2019.