General

Qiang Liu

Associate Professor

Institute of Automation, Chinese Academy of Sciences

E-mail: qiang.liu@nlpr.ia.ac.cn

Google Scholar: https://scholar.google.com/citations?user=D-lKLcMAAAAJ


Research Areas

Data Mining

Multimodal LLMs

AI4Science

Education

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

Experience

   
Work Experience

2022-07~present    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

Publications

   
Selected Papers

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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

  • 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, 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.

  • 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.

  • 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.

  • 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.

  • Haisong Gong, Qiang Liu, Shu Wu, Liang Wang. Text-Guided Molecule Generation with Diffusion Language Model. AAAI Conference on Artificial Intelligence (AAAI), 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.

  • 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.

  • 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.

  • 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.

  • Xin Sun, Qiang Liu#, Shu Wu, Zilei Wang, Liang Wang. Noise-Robust Semi-Supervised Learning for Distantly Supervised Relation Extraction. Conference on Empirical Methods in Natural Language Processing Findings (EMNLP Findings), 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.

  • 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.

  • Qiang Liu*, Weizhi Xu*, Shu Wu, Liang Wang. Counterfactual Debiasing for Fact Verification. Annual Meeting of the Association for Computational Linguistics (ACL), 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • Yanqiao Zhu, Yichen Xu, Qiang Liu, Shu Wu. An Empirical Study of Graph Contrastive Learning. Conference on Neural Information Processing Systems (NeurIPS), 2021.

  • 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.

  • Qiang Liu, Yanqiao Zhu, Zhaocheng Liu, Yufeng Zhang, Shu Wu. Deep Active Learning for Text Classification with Diverse Interpretations. ACM International Conference on Information and Knowledge Management (CIKM), 2021.

  • 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.

  • Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang. Graph Contrastive Learning with Adaptive Augmentation. 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.

  • 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.

  • Feng Yu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan. A Convolutional Approach for Misinformation Identification. International Joint Conference on Artificial Intelligence (IJCAI), 2017.

  • 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.

  • Feng Yu, Qiang Liu, 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.