研究方向

主要为因果可解释AI技术，包括：

1）高维、异质、小样本、多模态场景的独立性及条件独立性分析、因果发现

2）医学影像、组学、电子病历的因果表征学习及融合

3）因果推理与大模型结合的理论与应用

招生情况

计划2026年招收硕士生、博士生、博士后、科研助理

欢迎对上述方向感兴趣的同学联系

教育经历

2018 - 2019 卡内基梅隆大学 机器学习 CSC国家公派联培博士 导师：Kun Zhang

2015 - 2020 复旦大学 计算机 理学博士，导师：周水庚

2012 - 2015 广东工业大学 数学 理学硕士，导师：郝志峰、蔡瑞初

2007 - 2011 广东工业大学 数学 理学学士

学术服务

会议领域主席：SIGKDD 2026

会议程序委员 / 审稿人：ICML 2021, 2025, 2026; NeurIPS 2025; ICLR 2026; CVPR 2026; AAAI 2023, 2024, 2025, 2026; IJCAI 2026; BIBM 2025, 2026; ACM-MM 2025; ECML-PKDD 2023, 2024

期刊青年编委：Biomedical Informatics 2025, 2026; Interdisciplinary Sciences: Computational Life Sciences 2025

期刊特刊编辑：Mathematics 2025

国内学术组织委员：中国计算机学会-生物信息学专业委员会；中国人工智能学会-生物信息学与人工生命专业委员会

论文发表（*通讯，#同等贡献）

2026

Yixin Ren, Hao Zhang*, Yewei Xia, Feng Xie, Jihong Guan, Shuigeng Zhou*. Causal Discovery by Multi-Level Wavelet Mapping Correlation Based Statistical Dependence Measurement. ACM Transactions on Knowledge Discovery from Data (TKDD), 2026.

Xueliang Cui, Juncai Zhang, Jiacheng Hou, Dan Lu, Hao Zhang*, Ruxin Wang*. BiomedCCPL: Causal Conditional Prompt Learning for Biomedical Vision-Language Models. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026.

Xianliang Huang, Chen Xiao, Yuanxiang Ni, Guanming Liu, Mingkai Liu, Dikai Fan, Xiao Liu, Hao Zhang*. Semantic-Guided Progressive Object Removal with Gaussian Splatting. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2026.

Chenghou Jin, Yixin Ren, Hongxu Ma, Yewei Xia, Yi Guan, Hao Zhang, Jiandong Ding, Jihong Guan, Shuigeng Zhou. Invariant Feature Learning for Counterfactual Watch-time Prediction in Video Recommendation. In: Proceedings of the Fortieth AAAI Conference on Artificial Intelligence (AAAI), 2026.

Shaofan Chen#, Guoyuan He, Wentao Ma, Hao Zhang#, Tongqing Zhou, Siwei Wang, Lichuan Gu. Causal Direction Discovery via Related Conditional Residual. Pattern Recognition (PR), 2026, 176: 113196.

Zeyu Lin, Zongbao Yang, Shaoqi Wu, Junhao Li, Hao Zhang*, Ruxin Wang*. Ot-Amf: An Optimal Transport-Based Adaptive Multi-Scale Fusion Framework For Cancer Survival Prediction. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2026.

Zongbao Yang, Yuchen Lin, Yichen He, Jinlong Hu, Ruxin Wang, Hao Zhang, Shoubin Dong. IKDP: Implicit Knowledge Enhanced Disease Prediction via heterogeneous admission sequence graphs, Artificial Intelligence in Medicine (ARTMED), 2026, 24: 103365.

Yinghan Hong, Sirui Liang, Jiahao Lian, Guizhen Mai, Liang Zhao, Yueting Xue, Yi Xiang, Hao Zhang, Fangqing Liu, Zhifeng Hao. Evolutionary Constrained Optimization Based on Causal Random Forest. Expert Systems with Application (ESWA), 2026.

2025

Yixin Ren, Juncai Zhang, Yewei Xia, Ruxin Wang, Feng Xie, Hao Zhang*, Shuigeng Zhou*. Regression-based Conditional Independence Test with Adaptive Kernels. Artificial Intelligence (AIJ), 2025, 347: 104391. 

Yan Liu, Mingjie Chen, Chaojie Ji, Hao Zhang*, Ruxin Wang*. SERENA: A Unified Stochastic Recursive Variance Reduced Gradient Framework for Riemannian Non-Convex Optimization. In: Proceedings of The Forty-second International Conference on Machine Learning (ICML), 2025, 267: 38144-38168. 

Xichen Guo, Feng Xie, Yan Zeng, Hao Zhang, Zhi Geng. Data-Driven Selection of Instrumental Variables for Additive Nonlinear, Constant Effects Models. In: Proceedings of The Forty-second International Conference on Machine Learning (ICML), 2025, 267: 21163-21183. 

Zheng Li, Zeyu Liu, Feng Xie, Hao Zhang, Chunchen Liu, Zhi Geng. Local Identifying Causal Relations in the Presence of Latent Variables. In: Proceedings of The Forty-second International Conference on Machine Learning (ICML), 2025, 267: 35390-35418.

Zheng Li, Xichen Guo, Feng Xie*, Zeng Yan, Hao Zhang*, Zhi Geng. Local Learning for Covariate Selection in Nonparametric Causal Effect Estimation with Latent Variables. In: Proceedings of the Thirty-nine Annual Conference on Neural Information Processing Systems (NeurIPS), 2025.

Yixin Ren, Haocheng Zhang, Yewei Xia, Hao Zhang*, Jihong Guan, Shuigeng Zhou*. Fast Causal Discovery by Approximate Kernel-based Generalized Score Functions with Linear Computational Complexity. In: Proceedings of 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2025, V1: 1197-1208. 

Yixin Ren, Chenghou Jin, Yewei Xia, Like, Longtao Huang, Hui Xue, Hao Zhang, Jihong Guan, Shuigeng Zhou. Score-based Generative Modeling for Conditional Independence Testing. In: Proceedings of 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2025, V2: 2410-2419. 

Yewei Xia, Xueliang Cui, Hao Zhang*, Yixin Ren, Feng Xie, Jihong Guan, Ruxin Wang*, Shuigeng Zhou*. Identifying Causal Mechanism Shifts under Additive Models with Arbitrary Noise. In: Proceedings of The 34th International Joint Conference on Artificial Intelligence (IJCAI), 2025: 4706-4714. 

Yewei Xia, Yixin Ren, Hong Cheng, Hao Zhang*, Jihong Guan, Minchuan Xu, Shuigeng Zhou*. Efficient Constraint-based Window Causal Graph Discovery in Time Series with Multiple Time Lags. In: Proceedings of The 34th International Joint Conference on Artificial Intelligence (IJCAI), 2025: 9095-9103. 

Yuanpeng Zeng, Yuzhong Peng*, Ru Zhang, Hao Zhang*, Shaojie Qiao*, Faliang Huang, Qing Tian. GCCNet: A Novel Network Leveraging Gated Cross-Correlation for Multi-View Classification. IEEE Transactions on Multimedia (TMM), 2025, 27: 1086-1099. 

Shihuan He, Zongbao Yang, Jianbo Zhao, Hao Zhang*, Ruxin Wang*. Cyclic Contrastive Representation Learning for Incomplete Multi-modal Medical Image Segmentation. IEEE Journal of Biomedical and Health Informatics (JBHI), 2025.

Jianbo Zhao, Yuzhong Peng*, Zongbao Yang, Zhichen Chen, Changan Yuan, Xiao Qin, Ruxin Wang*, Hao Zhang*. Dynamic Debiasing of Multi-Hop Fact Verification via Counterfactual Reasoning. Knowledge-Based Systems (KBS), 2025, 332: 114875.

Zongbao Yang, Shihuan He, Zhichen Chen, Hao Zhang*, Ruxin Wang*. Knowledge-Enhanced Complementary Information Fusion with Heterogeneous Temporal Graph Learning for Disease Prediction. In: Proceedings of The 28th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2025: 426-436. 

Juncai Zhang, Huazhen Huang, Jianbo Zhao, Yixin Ren, Yuzhong Peng, Ruxin Wang*, Hao Zhang*. AdaMM: An Adaptive Multimodal Model with Learnable Weights for Protein-ligand Affinity Prediction. In: Proceedings of The 2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2025. 

MingJie Xu, Li Cai, Zongbao Yang, Ruxin Wang*, Hao Zhang*. Gene-Guided Multimodal Data Fusion for Cancer Patient Survival Analysis. Neurocomputing, 2025.

Wenyi Wu, Hao Zhang, Zhisen Wei, Xiao-Yuan Jing, Qinghua Zhang, Songsong Wu. Both Reliable and Unreliable Predictions Matter: Domain Adaptation for Bearing Fault Diagnosis without Source Data. Neurocomputing, 2025, 657(7): 131661. 

Wen Zheng, Zhongji Li, Yuanyuan Chen, Lei Deng, Hao Zhang, Yuzhong Peng. GEP-DNN4Mol: Automatic Chemical Molecular Design Based on Deep Neural Networks and Gene Expression Programming. Health Information Science and Systems (HISS), 2025, 13(31), https://doi.org/10.1007/s13755-025-00344-8. 

2024

Yixin Ren, Yewei Xia, Hao Zhang*, Jihong Guan, Shuigeng Zhou*. Efficiently Learning Significant Fourier Feature Pairs for Statistical Independence Testing. In: Proceedings of the Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS), 2024, 37: 99800-99835.

Hao Zhang, Yixin Ren, Yewei Xia, Shuigeng Zhou, Jihong Guan. Towards Effective Causal Partitioning by Edge Cutting of Adjoint Graph. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024, 46(12): 10259-10271.

Mingjie Chen, Hongcheng Wang*, Ruxin Wang, Yuzhong Peng, Hao Zhang*. CDRM: Causal Disentangled Representation Learning for Missing Data. Knowledge-Based Systems (KBS), 2024, Volume 299, 5, 112079.

Ru Zhang, Yanmei Lin, Yijia Wu, Lei Deng, Hao Zhang*, Mingzhi Liao*, Yuzhong Peng*. MvMRL: A Multi-view Molecular Representation Learning Method for Molecular Property Prediction. Briefings in Bioinformatics (BIB), 2024, Volume 25, Issue 4, July, bbae298, doi.org/10.1093/bib/bbae298.

Wenwei Xu, Hao Zhang*, Yewei Xia, Yixin Ren, Shiugeng Zhou*, Jihong Guan. Hybrid Causal Feature Selection for Cancer Biomarker Identification from RNA-seq Data. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 2024, May, doi: 10.1109/TCBB.2024.3406922.

Yixin Ren, Yewei Xia, Hao Zhang*, Jihong Guan, Shuigeng Zhou*. Learning Adaptive Kernels for Statistical Independence Test. In: Proceedings of  the International Conference on Artificial Intelligence and Statistics (AISTATS), 2024: 2494-2502.

Shaofan Chen, Yuzhong Peng*, Guoyuan He, Hao Zhang*, Li Cai, Chengdong Wei. CDSC: Causal Decomposition Based on Spectral Clustering. Information Sciences (INS), 2024, 657: 119985.

Wen Zheng, Zhongji Li, Yuanyuan Chen, Lei Deng, Hao Zhang, Yuzhong Peng. CDRM: GEP-DNN4Mol: Automatic Chemical Molecular Design Based on Deep Neural Networks and Gene Expression Programming. International Symposium on Bioinformatics Research and Application (ISBRA), 2024.

2023

Hao Zhang, Chuanxu Yan, Yewei Xia, Shuigeng Zhou, Jihong Guan. Causal Gene Identification Using Non-linear Regression-based Independence Tests. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 2023, 10(1): 185-195.

Yuzhong Peng#, Hao Zhang#, Ziqiao Zhang, Yanmei Lin, Shuigeng Zhou, Shaojie Qiao. GEP-DL4Mol: A Novel Molecular Deep-learning Model Optimization Framework for Boosting Molecular Properties Prediction. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2023: 432-435.

Qirui Li, Zhiping Peng, Delong Cui, Jianpeng Lin, Hao Zhang. UDL: A Cloud Task Scheduling Framework Based on Multiple Deep Neural Networks. Journal of Cloud Computing (JCC) , 2023, 12: 114.

Yewei Xia, Hao Zhang*, Yixin Ren, Jihong Guan, Shuigeng Zhou*. Causal Discovery by Continuous Optimization with Conditional Independence Constraint: Methodology and Performance. IEEE International Conference on Data Mining (ICDM), 2023: 668-677.

Yinghan Hong, Junping Guo*, Guizhen Mai, Yingqing Lin, Hao Zhang*, Zhifeng Hao, Gengzhong Zheng. High-dimensional Causal Discovery Based on Heuristic Causal Partitioning. Applied Intelligence (APIN), 2023, 53(20): 23768-23796.

Hao Zhang, Yewei Xia, Kun Zhang, Shuigeng Zhou, Jihong Guan. Conditional Independence Test Based on Residual Similarity. ACM Transactions on Knowledge Discovery from Data (TKDD), 2023, 17(8): 1-18.

Yating Zhong, Yuzhong Peng, Yanmei Lin, Dingjia Chen, Hao Zhang, Wen Zheng, Yuanyuan Chen, Changliang Wu. MODILM: Towards Better Complex Diseases Classification Using A Novel Multi-omics Data Integration Learning Model. BMC Medical Informatics and Decision Making (MIDM), 2023, 23(1): 82.

Yixin Ren, Hao Zhang, Yewei Xia, Jihong Guan, Shuigeng Zhou. Multi-level Wavelet Mapping Correlation for Statistical Dependence Measurement: Methodology and Performance. In: Proceedings of AAAI, 2023, 37(5): 6499-6506.

Hao Zhang, Yewei Xia, Yixin Ren, Jihong Guan, Shuigeng Zhou. Differentially Private Nonlinear Causal Discovery from Numerical Data. In: Proceedings of AAAI, 2023, 37(10): 12321-12328.

Jieguang He, Zhiping Peng, Delong Cui, Jingbo Qiu, Qirui Li, Hao Zhang. Enhanced Sooty Tern Optimization Algorithm Using Multiple Search Guidance Strategies and Multiple Position Update Modes for Solving Optimization Problems. Applied Intelligence (APIN), 2023, 53(6): 6763-6799.

2022

Hao Zhang, Kun Zhang, Shuigeng Zhou, Jihong Guan. Residual Similarity Based Conditional Independence Test and Its Application in Causal Discovery. In: Proceedings of AAAI, 2022, 36(5): 5942-5949.

Hao Zhang, Shuigeng Zhou, Chuanxu Yan, Jihong Guan, Xin Wang, Ji Zhang, Jun Huan. Learning Causal Structures Based on Divide and Conquer. IEEE Transactions on Cybernetics (TCYB), 2022, 52 (5): 3232-3243.

Yuzhong Peng*, Daoqing Gong, Chuyan Deng, Hongya Li, Hao Zhang*. An Automatic Hyperparameter Optimization DNN Model for Precipitation Prediction. Applied Intelligence (APIN), 2022, 52(3): 2703–2719.

2021

Hao Zhang, Kun Zhang, Shuigeng Zhou, Jihong Guan. Testing Independence Between Linear Combinations for Causal Discovery. In: Proceedings of AAAI, 2021, 6538-6546.

Hao Zhang, Chuanxu Yan, Shuigeng Zhou, Jihong Guan, Ji Zhang. Combined Cause Inference: Definition, Model and Performance. Information Sciences (INS), 2021, 574: 431-443.

2019

Zhihao Li, Haipeng Jia, Yunquan Zhang, Shice Liu, Shigang Li, Xiao Wang, Hao Zhang. Efficient Parallel Optimizations of A High-performance SIFT on GPUs. Journal of Parallel and Distributed Computing (JPDC), 2019, 124: 78-91.

Hao Zhang, Shuigeng Zhou, Jihong Guan. Measuring Conditional Independence by Independent Residuals for Causal Discovery. ACM Transactions on Intelligent Systems and Technology (TIST), 2019, 10(5):50-69.

Yuzhong Peng, Huasheng Zhao, Hao Zhang, Wenwei Li, Xiao Qin, Jianping Liao, Zhiping Liu, Jie Li. An extreme Learning Machine and Gene Expression Programming-based Hybrid Model for Daily Precipitation Prediction. International Journal of Computational Intelligence Systems (IJCIS), 2019, 12(2): 1512-1525. 

Hao Zhang, Shuigeng Zhou, Jihong Guan. Recursively Learning Causal Structures Using Regression-based Conditional Independence Test. In: Proceedings of AAAI, 2019, 3108-3115.

2018

Hongya Li, Yuzhong Peng, Chuyan Deng, Yonghua Pan, Daoqing Gong, Hao Zhang. Multicellular Gene Expression Programming-based Hybrid Model for Precipitation Prediction Coupled with EMD. International Conference on Intelligent Computing (ICIC), 2018: 207-218.

Hao Zhang, Shuigeng Zhou, Jihong Guan. Measuring Conditional Independence by Using Independent Residuals: Theoretical Results and Application in Causal Discovery. In: Proceedings of AAAI, 2018, 2029-2036.

2017

Hao Zhang, Shuigeng Zhou, Kun Zhang, Jihong Guan. Causal Discovery by Using Regression-based Conditional Independence Tests. In: Proceedings of AAAI, 2017, 1250-1256.

2016

Yifu Huang, Kai Huang, Yang Wang, Hao Zhang, Jihong Guan, Shuigeng Zhou. Exploiting Twitter Moods to Boost Financial Trend Prediction Based on Deep Network Models. Intelligent Computing Methodologies: International Conference on Intelligent Computing (ICIC), 2016: 449-460.

2015

Zhifeng Hao, Hao Zhang*, Ruichu Cai, Wen Wen, Zhihao Li. Causal Discovery on High Dimensional Data. Applied Intelligence (APIN), 2015, 42: 594-607.

科研项目

主持：

中国科学院“百 人计划”，在研

广东省杰出青年基金，在研

深圳市“鹏城孔雀计划”特聘岗，在研

广东省高校青年创新人才（教育厅），已结题

深圳市博士后来深科研资助，在研

国家自然科学基金（面上），面向多模态生物标志物的因果学习，在研

国家自然科学基金（青年），面向癌症数据的致病基因分析，已结题

中国博士后基金（一等资助），基于深度神经网络的高维因果发现，已结题

广东省自然科学基金（面上），面向高维数据的递归式因果发现，在研

骨干：

中国科学院战略性先导科技专项，多维大数据驱动的中国人群精准健康研究，已结题

国家重点研发计划，面向跨尺度医学数据的融合表征及因果数学理论与应用，在研

广东省普通高校创新团队（教育厅），生物序列与结构大数据智能算法研究创新团队，在研

广东省自然科学基金（面上），基于因果进化算法的大规模黑盒优化问题研究，已结题

深圳市基础研究（重点），面向重症监护的生命检测及预警技术研究，在研

参与：  

国家自然科学基金（联合），大数据查询处理的隐私保护技术，已结题

国家自然科学基金（面上），基于类不平衡深度特征学习的石化设备故障信号分类研究，已结题 

广东省自然科学基金（面上），面向民航空域监视系统的轻量级安全技术研究，已结题