基本信息

张 浩   中国科学院深圳先进技术研究院   副研   博导

电子邮件： h.zhang10@siat.ac.cn
通信地址： 深圳市南山区西丽深圳大学城学苑大道1068号

简介

​张 浩，复旦 & CMU 联培博士，副研，博导，PI，主要从事因果学习+可解释AI+医学数据分析研究；

课题组以支持学生科研发展为主，学生以一作在国际会刊发表一系列成果（2025年已发表CCF-A类 10 篇），包括AI理论研究最高级别刊物 Artificial Intelligence；
国科大、中科大-SIAT联培、南科大-深理工联培均有招生名额，欢迎对我们方向感兴趣的同学加入；
另，诚聘博士后，待遇 40w+/y，欢迎加入。

论文发表

2025

  • 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, accepted.

  • 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, DOI: 10.1016/j.artint.2025.104391. 
  • 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.
  • 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. 

  • 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. 

  • 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, Accepted. 

  • 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. 

  • 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. 

  • 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. 

2024 及之前

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

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

  • 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.

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