朱修彬，男，工学博士，硕士毕业后曾长期在IT领域从事数据库开发和研究工作。2018年在西安电子科技大学自动化专业获得工学博士学位，2019年入职西安电子科技大学机电工程学院，加入系统控制与自动化研究团队(Systems Control and Automation Group，SCAG)。研究领域包括粒计算的基础理论研究，联邦计算与差分隐私，模型可解释性研究，时序数据异常值检测和基于粒计算的时空数据挖掘等。先后主持与参与国家自然科学基金面上项目3项、，中央高校基本科研业务费2项、科学技术部高端外国专家引进计划项目1项、国防科技173计划技术领域基金1项、横向课题若干项。

现担任Knowledge-Based Systems期刊副主编；

担任IEEE Transactions on Fuzzy Systems，IEEE Transactions on Cybernetics, IEEE Transactions on Neural Networks, Applied Soft Computing, ，IEEE Conference on Decision and Control等学术期刊、会议审稿人。

近年来，课题组围绕人工智能与多学科交叉的前沿领域，开展了深入的研究工作，特别是在基于大模型的遥感影像目标检测、生物医学信号处理、医学影像分析、融合知识的机器学习、可解释人工智能、大语言模型等方面取得了显著成果，与国内外相关大学和科研院所建立了良好的合作关系。课题组发表了在IEEE Transactions发表了一系列高水平论文，并致力于将人工智能技术与实际应用场景紧密结合，推动理论创新与技术落地，取得了良好的效果。

招生信息简介：

具备良好的数学基础，入学前需掌握如下理论知识：高等数学，线性代数，概率与统计； 

具备良好的英语阅读、写作和沟通能力； 

能够熟练掌握一门程序设计语言(c、matlab、python或其他语言)，具有独立分析和解决问题的能力。 

每年招收硕士生3~4名。

期刊论文：

[1] X. B. Zhu, W. Pedrycz, and Z. W. Li, “A design of granular Takagi-Sugeno fuzzy model through the synergy of fuzzy subspace clustering and optimal allocation of information granularity,” IEEE Transactions on Fuzzy Systems, vol. 26, no. 5, pp. 2499-2509, 2018.

[2] X. B. Zhu, W. Pedrycz, and Z. W. Li, “Granular encoders and decoders: A study in processing information granules,” IEEE Transactions on Fuzzy Systems, vol.25, no. 5, pp. 1115-1126, 2017.

[3] X. B. Zhu, W. Pedrycz, and Z. W. Li, “Granular models and granular outliers”, IEEE Transactions on Fuzzy Systems, vol. 26, no. 6, pp. 3835-3846, 2018.

[4] X. B. Zhu, W. Pedrycz, and Z. W. Li, “Granular representation of data: A design of families of epsilon-information granules,” IEEE Transactions on Fuzzy Systems, vol. 26, no. 4, pp. 2107-2119, 2018.

[5] X. B. Zhu, W. Pedrycz, and Z. W. Li, “A Development of Hierarchically Structured Granular Models Realized through Allocation of Information Granularity,” IEEE Transactions on Fuzzy Systems, vol. 29, no. 12, pp. 3845-3858, 2020.

[6] X. B. Zhu, D. Wang, W. Pedrycz, and Z. W. Li, “Horizontal Federated Learning of Takagi-Sugeno Fuzzy Rule-based Models,” IEEE Transactions on Fuzzy Systems, vol. 30, no. 9, pp. 3537-3547, 2022.

[7] X. B. Zhu, D. Wang, W. Pedrycz, and Z. W. Li, “Fuzzy rule-based local surrogate models for black-box model explanation,” IEEE Transactions on Fuzzy Systems, vol. 31, no. 6, pp. 2056-2064, 2023.

[8] X. B. Zhu, X. C. Hu, L. Yang, W. Pedrycz, and Z. W. Li, “A development of fuzzy rule-based regression models through using decision trees,” IEEE Transactions on Fuzzy Systems

[9] G. Zhang, X. B. Zhu, L. Yin, W. Pedrycz, and Z. W. Li, “Fuzzy Prediction Model in Privacy Protection: Takagi-Sugeno Rules Model via Differential Privacy,” IEEE Transactions on Fuzzy Systems

[10] X. C. Hu, X. B. Zhu, Lan Yang, W. Pedrycz, and Z. W. Li, “A Design of Fuzzy Rule-based Classifier for Multi-class Classification and its Realization in Horizontal Federated Learning,” IEEE Transactions on Fuzzy Systems, 2024.

[11] D. Wang, Monika Richter, X. B. Zhu, W. Pedrycz, Adam Gacek, Aleksander Sobotnicki, and Z. W. Li, “Detecting Characteristic Points for the Analysis of Bioimpedance Signal Through a Synergy of Fuzzy Rule-based models and Granular Neural Networks,” IEEE Transactions on Fuzzy Systems, 2024.

[12] Lan Yang, X. B. Zhu, W. Pedrycz, and Z. W. Li, and X. C. Hu, “A Granular Aggregation of Multifaceted Gaussian Process Models,” IEEE Transactions on Fuzzy Systems, 2024.

[13] Huimin Zhang, X. C. Hu, X. B. Zhu, Xinwang Liu, and W. Pedrycz, “Application of Gradient Boosting in the Design of Fuzzy Rule-based Regression Models,” IEEE Transactions on Knowledge and Data Engineering, 2024.

[14] X. B. Zhu, W. Pedrycz, and Z. W. Li, “Granular data description: Designing ellipsoidal information granules,” IEEE Transactions on Cybernetics, vol. 47, no. 12, pp. 4475-4484, 2017. 

[15] X. B. Zhu, W. Pedrycz, and Z. W. Li, “A development of granular input space in system modeling,” IEEE transactions on Cybernetics, vol. 51, no. 3, pp. 1639-1650, 2021.

[16] X. B. Zhu, W. Pedrycz, and Z. W. Li, “A two-stage approach for constructing type-2 information granules,” IEEE transactions on Cybernetics, vol.52, no.4, pp. 2214-2224, 2022.

[17] X. B. Zhu, W. Pedrycz, and Z. W. Li, “A granular approach to interval output estimation for rule-based fuzzy models,” IEEE transactions on Cybernetics, vol. 52, no. 7, pp. 7029-7038, 2022.

[18] X. B. Zhu, D. Wang, W. Pedrycz, and Z. W. Li, “A design of granular classifier based on granular data descriptors,” IEEE Transactions on Cybernetics, vol. 53, no. 3, pp. 1790-1801, 2023.

[19] P. Nie, X. B. Zhu, W. Pedrycz, and Z. W. Li, “Optimization of granulation-degranulation mechanism through neurocomputing,” IEEE transactions on Cybernetics, vol. 52, no. 6, pp. 2168-2267, 2022.

[20] X. B. Zhu, W. Pedrycz, Ting Qu, and Z. W. Li, “From numeric to granular models: A Quest for error and performance analysis,” IEEE transactions on Cybernetics, vol. 54, no. 1, pp. 150-161, 2024.

[21] K. Y. Wu, X. C. Hu, L. Y. Liu, X. B. Zhu, J. C. Huang, and W. Pedrycz, “Local Surrogate Models with Residual Fuzzy Rules for Model Agnostic Explanations,” IEEE transactions on Cybernetics, 2025.

[22] X. B. Zhu, W. Pedrycz, and Z. W. Li, “Development and analysis of neural networks realized in the presence of granular data,” IEEE Transactions on Neural Networks and Learning Systems, vol.31 no.9, pp. 3606-3619, 2020.

[23] X. B. Zhu, W. Pedrycz, and Z. W. Li, “Construction and evaluation of information granules: From the perspective of clustering,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol.52, no.3, pp.2024-2037, 2022.

[24] X. B. Zhu, D. Wang, W. Pedrycz, and Z. W. Li, “Design and development of granular fuzzy rule-based models for knowledge transfer,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 2, pp. 704-715, 2023.

[25] X. B. Zhu, D. Wang, W. Pedrycz, and Z. W. Li, “Privacy-preserving realization of fuzzy clustering and fuzzy modeling through vertical federated learning,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, doi：10.1109/TSMC.2023.3320680, 2023.

[26] D. Wang, X. B. Zhu, W. Pedrycz, and Z. W. Li, “Transfer learning realized with nonlinearly transformed input space,” IEEE Transactions on Emerging Topics in Computing, vol. 11, no. 2, pp. 448-460, 2023.

[27] X. B. Zhu, W. Pedrycz, and Z. W. Li, “Fuzzy clustering with nonlinearly transformed data,” Applied Soft Computing, vol. 61, pp. 364-376, Dec 2017.

[28] D. Wang, P. Nie, X. B. Zhu, W. Pedrycz, and Z. W. Li, “Designing of higher order information granules through clustering heterogeneous granular data,” Applied Soft Computing, vol. 112, pp. 107820, 2021. 

[24] X. Y. Han, X. B. Zhu, W. Pedrycz, and Z. W. Li, “A three-way classification with fuzzy decision trees,” Applied Soft Computing, vol. 132, pp. 109788, 2022.

[19] G. Zhang, X. B. Zhu, L. Yin, W. Pedrycz, and Z. W. Li, “Granular data representation under privacy protection: Tradeoff between data utility and privacy via information granularity,” Applied Soft Computing, vol. 131, pp. 109808, 2022.

[30] D. Wang, X. B. Zhu, W. Pedrycz, Adam Gacek, Aleksander Sobotnicki and Z. W. Li, “Modeling and Analysis of Cardioimpedance Signals Using Polynomial Models and Fuzzy Rule-based Models,” Applied Soft Computing, vol. 144, no. C, pp. 110482, 2023. 

[31] X.Y. Han, X. B. Zhu, W. Pedrycz, and Z. W. Li, “A Design of Fuzzy Rule-based Classifier Optimized through Softmax Function and Information Entropy,” Applied Soft Computing, 2024.

[32] D. Wang, Kai Yu, X. B. Zhu, Zhenhua Yu, “Optimal solutions to granular fuzzy relation equations with fuzzy logic operations,” Applied Soft Computing, Volume 163, September 2024, 111861.

[33] D. Wang, X. B. Zhu, W. Pedrycz, and Z. W. Li, “A Randomization Mechanism for Realizing Granular Models in Distributed System Modeling,” Knowledge-based Systems, vol. 232, pp. 107376, 2021.

[34] T. Y. Liu, X. B. Zhu, W. Pedrycz, Z. W. Li, “A design of information granule-based under-sampling method in imbalanced data classification,” Soft Computing, vol.24, no. 22, pp. 17333-17347, 2020.

[35] Huimin Zhang, X. B. Zhu, “Development and evaluation of M + 1-way classification mechanism realized through identifying foreign patterns,” Soft computing, vol. 27, no. 4659–4668, 2023.

[36] D. Wang, X. B. Zhu, W. Pedrycz, Zhenhua Yu, Z. W. Li, “A development of coordinate-based fuzzy encoding algorithm in compression of grayscale images,” Soft Computing, doi: 10.1007/s00500-023-09106-8, 2023.

[37] D. Wang, X. B. Zhu, W. Pedrycz, Zhenhua Yu and Z. W. Li, “Development of granular fuzzy relation equations based on a subset of data,” IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 8, pp. 1416-1427, 2021.

[39] T. L. Jing, W. Pedrycz, X. B. Zhu, G. Succi, and Z. W. Li, “Linguistic models: optimization with the use of conditional Fuzzy C-Means,” IEEE Transactions on Emerging Topics in Computational Intelligence, DOI:10.1109/TETCI.2023.3265391, 2023.

 

会议论文

[1] Xiubin Zhu, Witold Pedrycz, and Zhiwu Li, Granular description of data: Building information granules with the aid of the principle of justifiable granularity [C]// IEEE International Conference on Fuzzy Systems. IEEE, 2016:969–976.  

[2] Runqi Nan, Xiubin Zhu, Dan Wang, and Ziyue Ma, “A realization of self-face diagnosis algorithm based on chinese medicine theory,” the 5th International Conference on Artificial Intelligence and Big Data (ICAIBD 2022), May 27-30, 2022, Chengdu, China

[3] Zhidong Zhang, Xiubin Zhu, Ding Liu, “Model of Gradient Boosting Random Forest Prediction,” 2022 IEEE International Conference on Networking, Sensing and Control (IEEE ICNSC 2022), Shanghai, China