个人简介

张 浩，中国科学院深圳先进技术研究院副研究员，博士生导师，入选 中国科学院“百 人计划”、广东省杰青、深圳市“孔雀计划”。2020年博士毕业于复旦大学、美国卡内基梅隆大学 联培，主要从事因果学习理论及其在医学数据场景的应用研究，一作/通讯于 Artificial Intelligence、TPAMI、ICML、NeurIPS、KDD、CVPR 等期刊/会议发表成果，入选10+次 三大会 Oral、Spotlight 、ESI 高被引、热点论文；担任 ACM SIGKDD 领域主席，Biomedical Informatics、INSC 青年编委。主持国家级、省部级项目6项，骨干参与中国科学院战略性先导科技专项、国家重点研发计划、国自然重点、深圳市重点项目。

研究兴趣

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

主要项目

中国科学院“百 人计划”（B类)，高维异构数据的因果发现研究（E64403），2026 - 2028，500万，主持

广东省杰出青年基金，多模态肿瘤数据的因果知识提取及融合研究（2026B1515020017），2026 - 2029，100万，主持

国家自然科学基金（面上），面向多模态生物标志物识别的因果学习研究（62472415），2025 - 2028，50万，主持

国家自然科学基金（青年），面向癌症数据的致病基因分析（62006051），2021 - 2023，30万，主持

中国科学院战略性先导科技专项（B类），多维大数据驱动的中国人群精准健康研究（XDB38040200），2020 - 2025，2500万，骨干

论文情况（*通讯，#同等）

2026

Zheng Li, Feng Xie*, Shenglan Nie, Xichen Guo, Ruxin Wang*, Hao Zhang*. A Recursive Decomposition Framework for Causal Structure Learning in the Presence of Latent Variables. In: Proceedings of The Forty-third International Conference on Machine Learning (ICML Oral), 2026.

Zeyu Liu, Zheng Li, Feng Xie, Yan Zeng, Hao Zhang, Kun Zhang. Local Covariate Selection for Average Causal Effect Estimation without Pretreatment and Causal Sufficiency Assumptions. In: Proceedings of The Forty-third International Conference on Machine Learning (ICML Spotlight), 2026.

Yixin Ren, Chenghou Jin, Yewei Xia, Zichuan Lin, Deheng Ye, Hao Zhang, Jihong Guan, Shuigeng Zhou. Streaming Covariate Balancing via Discrepancy-Based Feature Coresets. In: Proceedings of The Forty-third International Conference on Machine Learning (ICML), 2026.

Yixin Ren, Hongquan Liu, Juncai Zhang, Yewei Xia, Zichuan Lin, Deheng Ye, Hao Zhang, Jihong Guan, Shuigeng Zhou. Powerful and Theoretically Guaranteed Independence Testing on Heterogeneous Federated Clients. In: Proceedings of The Forty-third International Conference on Machine Learning (ICML), 2026.

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

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.

Yuanxiang Ni, Xianliang Huang, Chenhang Ma, Chen Xiao, Yuewen Ma, Ruxin Wang*, Hao Zhang*. CoGeo-GS: Concept-Driven and Geometry-Aware Multi-Object Removal in 3D Scenes. In: Proceedings of the IEEE International Conference on Multimedia & Expo (ICME), 2026.

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.

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, 20(4), 1-33.

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.

Mingjie Chen, Yewei Xia, Hao Zhang*, Ruxin Wang*, Yuzhong Peng, Jihong Guan, Shuigeng Zhou*. Causal Discovery by Continuous Optimization with Weighted Superstructure. Neural Networks (NN), 2026, 108974.

Rui Chen, Xinran Wang, Guanyang Li, Hao Zhang, Fangqiu Fu, and Hanlin Zhou. Identification and Validation of an Autophagy-Related Gene Signature for Prognostic Prediction and Immunotherapy Response in Esophageal Squamous Cell Carcinoma. Cancers, 2026, 18(3): 388.

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, 131417.

Lingling Zheng , Junyan Lei , Zhiguo Gong , Jinling Tang , Hao Zhang , Sha Feng. Structure-Consistent Genetic Instrument Screening for Robust Biomedical Causal Inference, Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB), 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.

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.