About Me

I am an Associate Professor in the Center of Statistical Research, School of Statistics at the Southwestern University of Finance and Economics. My research interest includes Graph Neural Networks and its applications in Economics and Medical Science.

🔥 News

• 2024.11:  🎉🎉 One paper was accepted by the KDD 2025
• 2024.9:  🎉🎉 One paper was accepted by the ICDM 2024
• 2024.5:  🎉🎉 One paper was accepted by the IJCAI 2024
• 2023.11:   I am going to serve as a TPC member of IEEE the 7th International Conference on Big Data and Artificial Intelligence (BDAI 2024).

📐 Teaching Experience

I will teach 最优化理论 I(Optimization Theory I) and 统计学导论(The Introduction of Statistics) next semester.

Courses taught:

📝 Publications

GNN in Medical Science

arXiv

ASD diagnosis and pathogenic analysis

Lu Wei

Project

• Autism Spectrum Disorder (ASD) is an intricate neurodevelopmental condition that significantly impacts socialization and is distinguished by atypical, limited, or repetitive language behaviors. Our research focuses on the diagnosis of ASD utilizing multi-modal brain image data in conjunction with Graph Neural Networks (GNNs), specifically incorporating Diffusion Tensor Imaging (DTI), structural Magnetic Resonance Imaging (sMRI), and functional Magnetic Resonance Imaging (fMRI). Additionally, our objective is to identify the pathogenic functional regions within the brain networks associated with ASD.

KDD 2025

Machine learning for IMRT

Bin Liu*, Yu Liu, Zhiqian Li, Jianghe Xiao, Huazhen Lin

Project

• We have designed a functional deep reinforcement learning algorithm for automatic radiotherapy treatment planning that fully takes into account the complex scenarios of radiotherapy, and its performance is close to meeting clinical requirements.

GNN in F&E

IJCAI 2023

Analyze Industrial Chain with GNNs

Bin Liu, Jiujun He, Ziyuan Li, Xiaoyang Huang, Xiang Zhang, Guosheng Yin

Project

• The digital development of the industrial chain attracts more and more attention from researchers and policymakers. One of the critical issues is how the efficiency of the industrial chain is affected by external factors, such as macroeconomic policy, government regulation or monetary policy, international political factors, internal factors, such as microeconomic driver factors, and so on. In this project, we conduct a quantitative analysis of the development of the industry chain with GNNs.

Preprints

• Hu, Min, Zhizhong Tan, Bin Liu, and Guosheng Yin. Futures quantitative investment with heterogeneous continual graph neural network. arXiv preprint arXiv:2303.16532 (2023).
• Bin Liu, Yu Liu, Zhiqian Li, Jianghe Xiao, Huazhen Lin. Automatic radiotherapy treatment planning with deep functional reinforcement learning. medRxiv, 2024.06.23.24309060, doi: https://doi.org/10.1101/2024.06.23.24309060.
• Qiang Liu, Zhizhong Tan, Siyang Liu, Min Hu, Bin Liu, Wenyong Wang. Interpret How ESG Affects Industrial Value Extension using Asymmetric GNNs. SSRN Electronic Journal (2024), http://dx.doi.org/10.2139/ssrn.4715337
• Lu Wei, Yi Huang, Guosheng Yin, Fode Zhang, Manxue Zhang, Bin Liu*. Diagnosis and Pathogenic Analysis of Autism Spectrum Disorder Using Fused Brain Connection Graph. arXiv preprint arXiv:2410.07138 (2024).

Conference Papers

• Bin Liu, Yu Liu, Zhiqian Li, Jianghe Xiao, Huazhen Lin. Automatic radiotherapy treatment planning with deep functional reinforcement learning. KDD, 2025, Toronto, ON, Canada.
• Zhizhong Tan, Min Hu, Bin Liu*, Guosheng Yin. Futures Quantitative Investment with Heterogeneous Continual Graph Neural Networ. ICDM, 2024, Abu Dhabi, UAE.
• Jinjin Li, Bin Liu*, Chengyan Liu, Hongli Zhang. Predicting Housing Transaction with Common Covariance GNNs. IJCAI, 2024, Jeju, Korea, pp. 7323-7330.
• Jiujun He, Bin Liu, Guosheng Yin. Enhancing Semi-supervised Domain Adaptation via Effective Target Labeling. AAAI, 2024, Vancouver, Canada, pp. 12385-12393.
• Bin Liu, Jiujun He, Ziyuan Li, Xiaoyang Huang, Xiang Zhang, Guosheng Yin. Interpret ESG Rating’s Impact on the Industrial Chain Using Graph Neural Networks. IJCAI, 2023, Macao, China, pp. 6076-6084.
• Lu Wei, Bin Liu*, Jiujun He, Manxue Zhang, Yi Huang. Autistic Spectrum Disorders Diagnose with Graph Neural Networks. ACM Multimedia, 2023, Ottawa, Canada, pp. 8819–8827.
• Zhuo Tan, Bin Liu*, Guosheng Yin. Asymmetric Self-Supervised Graph Neural Networks. IEEE International Conference on Big Data, Osaka, Japan, 2022, pp. 1369-1376.
• Jiujun He, Bin Liu, Xuan Yang. Non-local Patch Mixup for Unsupervised Domain Adaptation. IEEE International Conference on Data Mining (ICDM), Orlando, FL, USA, 2022, pp. 969-974.
• Bin Liu, Wang Liang, Yin Guosheng. Learning distributed sentence vectors with bi-directional 3D convolutions. The 28th International Conference on Computational Linguistics(COLING), Barcelona, Spain, 2020, pp. 6820–6830.
• Bin Liu, Xiaoxue Gao, Mengshuang He, Lin Liu, Guosheng Yin. A Fast Online COVID-19 Diagnostic System with Chest CT Scans. The 26TH ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Health Day), 2020, Virtual Conference. The online diagnosis system:https://www.covidct.cn/
• Bin Liu, Guosheng Yin. Chinese document classification with bi-directional convolutional language model. The 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Xi’an, China, 2020, pp. 1785–1788.
• Bin Liu, Zenglin Xu, Bo Dai, Haoli Bai, Xianghong Fang, Yazhou Ren, Shandian Zhe. Learning from semantically dependent multi-tasks. International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA, 2017, pp. 3498-3505.
• Haoli Bai, Zenglin Xu, Bin Liu, Yingming Li. Hierarchical probabilistic matrix factorization with network topology for multi-relational social network. Proceedings of The 8th Asian Conference on Machine Learning, PMLR 63:270-285, 2016.
• Bin Liu, Chao Song, Nianbo Liu. Distinguishing uncertain objects with multiple features for crowdsensing. IEEE Global Communications Conference, Austin, TX, USA, 2014, pp. 2751-2756.

Journal Papers

• Min Hu, Bin Liu*, Guosheng Yin. Multi-Site and Multi-Pollutant Air Quality Data Modeling. Sustainability, vol. 16(1), 165. 2024.
• Bin Liu, Guosheng Yin. Graphmax for Text Generation. Journal of Artificial Intelligence Research, vol. 78, pp.823-848, Nov. 2023.
• Zhuo Tan, Yifan Zhu, Bin Liu*. Learning spatial-temporal feature with graph product. Signal Processing, 210 (2023): 109062.
• Shaogao Lv, Linsen Wei, Qian Zhang, Bin Liu, Zenglin Xu. “Improved Inference for Imputation-Based Semisupervised Learning Under Misspecified Setting”, IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 11, pp. 6346-6359, Nov. 2022.
• Zenglin Xu, Bin Liu*, Shandian Zhe, Haoli Bai, Zihan Wang, Jennifer Neville. Variational random function model for network modeling, IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 1, pp. 318-324, Jan. 2019.
• Bin Liu, Lirong He, Yingming Li, Shandian Zhe, Zenglin Xu. NeuralCP: Bayesian Multiway Data Analysis with Neural Tensor Decomposition, Cognitive Computation, 10, pp.1051–1061, 2018.
• He Lirong, Bin Liu, Guangxi Li, Yongpan Sheng, Yafang Wang, and Zenglin Xu. [Knowledge base completion by variational bayesian neural tensor decomposition], Cognitive Computation, 10 (2018): 1075-1084.
• Bin Liu, Yingming Li, Zenglin Xu. Manifold regularized matrix completion for multi-label learning with ADMM. Neural Networks, vol. 101, pp. 57-67, 2018.
• Bin Liu, Zenglin Xu, Shuang Wu, Fei Wang. Manifold regularized matrix completion for multilabel classification. Pattern Recognition Letters, Vol. 80, pp. 58-63, 2016.

🎖 Honors and Awards

• 2016.10 Best Paper Runner Up, ACML 2016.
• 2014.07 Best Paper Candidate, GlobeCom 2014.

🎓 Educations

• 2013.09 - 2017.12, University of Electronic Science and Technology of China, Computer Science PhD.
• 2008.09 - 2011.06, University of Electronic Science and Technology of China, Computer Science Mphil.
• 2004.09 - 2008.06, Liaoning University of Technology, Bachelor of Computational Mathematics

🏫 Visiting

• • 2024.7 - 2024.9, The University of Hong Kong, visiting scholar.
• • 2023.11 - 2023.12, Imperial College of London, visiting scholar.
• • 2016.6 - 2017.6, University of British Columbia, visiting student.

💻 Employment

• 2018.04 - (now), Associate professor at the School of Statistics, Southwestern University of Finance and Economics, Chengdu, China.
• 2018.09 - 2020.09, PostDoc at Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, China.

💬 Invited Talks

• 2024.07, Interpret How External Shocks Affect Industrial Chain using Graph Machine Learning, The 7th International Conference on Econometrics and Statistics (EcoSta 2024), Beijing.
• 2023.08, Customizing personal large-scale language model using co-occurrence statistic information, 首届机器学习与统计会议，华东师范大学，上海.