开云拜仁合作伙伴

对外经济贸易大学信息开云拜仁合作伙伴

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开云拜仁合作伙伴:张琦

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副教授 / 博士生导师

行政职务:系主任

联系电话:(010)-64491664

办公室:求索楼1151

邮箱:zhangqi@uibe.edu.cn


      研究方向

机器学习

社会网络

智慧医疗

目标识别


学术背景

20129-20177月,北京大学,工业工程管理系,理学博士

201410-201510,耶鲁大学,电子工程,联合培养博士

20089-20127月,南京航空航天大学,电子电气工程,工学学


工作经历

2021年1-至今,对外经济贸易大学 信息开云拜仁合作伙伴 副教授

2018年3月-2020年12月,对外经济贸易大学 信息开云拜仁合作伙伴,讲师


学术发表

? 部分期刊论文

[1] R. Li, Q. Zhang*, N. Zhang, and T. Chu, “On Connections between Invariant Subspace and Quotient Approaches for Analysis and Control of Boolean Networks,”,IEEE Transactions on Automatic Control, forthcoming, 开云拜仁合作伙伴.

[2] Q. Zhang, Y. Xiao, Y. Liu, T. Deng, Z. Li, and R. Li, “Visualizing the intellectual structure and evolution of carbon neutrality research: a bibliometric analysis”, Environmental Science and Pollution Research,  vol. 30, pp. 75838–75862, 2023.

[3] Q. Zhang, Y. Liang, Y. Zhang, Z. Tao, R. Li, and H. Bi, “A comparative study of attention mechanism based deep learning methods for bladder tumor segmentation”, International Journal of Medical Informatics, vol, 171, p. 104984, 2023.

[4] Y. Liu, Q. Zhang, and T. Chu,“Stock index prediction based on multi-time scale learning with multi-graph attention networks”, Applied Inteligence,vol. 53, pp. 16263–16274, 2023.

[5] R. Li, Q. Zhang*, and T. Chu, “Bisimulations of probabilistic boolean networks”, SIAM Journal on Control and Optimization, vol. 60, no. 5, pp. 2631-2657, 2022.(UIBE-A)

[6] C. Shi, Q. Zhang, and T. Chu, “Source estimation in continuous-time diffusion networks via incomplete observation”, Physica A: Statistical Mechanics and its Applications, vol. 592, p. 126843, 2022.

[7] R. Li, Q. Zhang*, and T. Chu, “Quotients of probabilistic boolean networks”, IEEE Transactions on Automatic Control, vol. 67, no. 11, 2022.

[8] C. Shi, Q. Zhang, and T. Chu, “Locating the source of diffusion in networks under mixed observation condition”, Physics Letters A, vol. 434, p. 128033, 2022.

[9] C. Shi, Q. Zhang, and T. Chu, “Effect of observation time on source identification of diffusion in complex networks”, Chinese Physics B, vol. 31, p. 070203, 2022.

[10] Q. Zhang, T. Chu, and C. Zhang, “Semi-supervised graph based embedding with non-convex sparse coding techniques”, IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 5, pp. 2193-2207, 2021.(CCF-A)

[11] R. Li, Q. Zhang*, and T. Chu, “Reduction and analysis of boolean control networks by bisimulation”, SIAM Journal on Control and Optimization, vol. 59, no. 2 : pp. 1033-1056, 2021.(UIBE-A)

[12] R. Li, Q. Zhang*, and T. Chu, “Input-output-to-state stability of systems related through simulation relations”, SIAM Journal on Control and Optimization, vol. 59, no.1, pp. 614-634,2021.(UIBE-A)

[13] R. Li, Q. Zhang*, and T. Chu, “On quotients of Boolean control networks”, Automatica, vol. 125, p.109401, 2021.

[14] Q. Zhang, R. Li, and T. Chu, Kernel semi-supervised graph embedding model for multimodal and mixmodal data”, SCIENCE CHINA Information Sciences, vol. 63, no. 1, p. 119204, 2020.(CCF-A)

[15] Y. Zhang, J. Hu, and Q. Zhang*, “Application of locality preserving projection based unsupervised learning in predicting the oil production for low-permeability reservoirs”, SPE Journal, doi: 10.2118/201231-PA, 2020.

[16] Q. Zhang, “Path-wise cascading probabilistic description for information diffusion in networks”, Ad Hoc & Sensor Wireless Networks, vol. 46, no. 3-4, pp. 297-308, 2020.

[17] Q. Zhang, R. Mao, and R. Li, “Spatial-temporal restricted supervised learning for collaboration recommendation”, Scientometrics, vol. 119, no. 3, pp. 1497-1517, 2019.(UIBE-A)

[18] Q. Zhang, and T. Chu, “Learning in multimodal and mixmodal data: Locality preserving discriminant analysis with kernel and sparse representation techniques”, Multimedia Tools and Applications, vol. 76, no. 14, pp. 15465-15489, 2017.

[19] Q. Zhang, and T. Chu, “Structure regularized traffic monitoring for traffic matrix estimation and anomaly detection by link-load measurements”, IEEE Transactions on Instrumentation and Measurement, vol. 65, no. 12, pp. 2797-2807, 2016.

[20] Q. Zhang, K. Deng, and T. Chu, “Sparsity induced locality preserving projection approaches for dimensionality reduction”, Neurocomputing, vol. 200, pp. 35-46, 2016.

? 部分会议论文

[1] X. Jin, and Q. Zhang, “Intelligent recognition of bladder cancer based on convolutional neural network”, Lecture Notes in Electrical Engineering, Springer, vol. 706, pp. 135-142, 2020.

[2] T. Li, and Q. Zhang, “Deep learning based pathologic images recognition upon invasive bladder cancer”, Lecture Notes in Electrical Engineering, Springer, vol. 706, pp. 395-403, 2020.

[3] C. Shi, Q. Zhang, and T. Chu, “Estimating the diffusion source in complex networks with sparse modeling method”, Lecture Notes in Electrical Engineering, Springer, vol. 594, pp. 20-26, 2020.

[4] Q. Zhang, and T. Chu, Lα-regularization-based sparse semi-supervised learning for data with complex distributions, in Proceedings of IEEE 8th Conference on Data Driven Control and Learning Systems, pp. 883-888, 2019.

[5] C. Shi, Q. Zhang, and T. Chu, “Observer selection for source identification on complex networks”, in Proceedings of the 38th Chinese Control Conference, pp. 7996-8000, 2019.

[6] C. Shi, Q. Zhang, and T. Chu, “Estimating the perturbation origin in networked dynamical systems with sparse observation”, Lecture Notes in Electrical Engineering, Springer, vol. 529, pp. 655-661, 2019.

[7] C. Shi, Q. Zhang, and T. Chu, “Provenance identification in diffusion networks with incomplete cascades”, in Proceedings of the 37th Chinese Control Conference, pp. 9704-9708, 2018.

[8] Q. Zhang, K. Deng, and T. Chu, “An asynchronous linear-threshold innovation diffusion model”, Lecture Notes in Electrical Engineering, Springer, vol. 404, pp. 313-319, 2016.

[9] Q. Zhang, and T. Chu, “Probabilistic cascade model from partial observations”, in Proceedings of the 4th TCCT Workshop on Stochastic Systems and Control, p. 47, 2016.

[10] Q. Zhang, and T. Chu, “Semi-supervised discriminant analysis based on sparse-coding theory”, in Proceedings of the 35th Chinese Control Conference, pp.7082-7087, 2016.


科研项目

国家社科基金重大专项,No.22VMG037,2023.1-2025.12,子课题负责人

基于多尺度信息融合的膀胱肿瘤分类与智能分割研究,国家自然科学基金,2022.1-开云拜仁合作伙伴.12,主持

基于机器学习的北京网络舆情特征分析与监控,北京市社会科学基金,2022.1-开云拜仁合作伙伴.12,主持

大数据视角下基于网络信息反演的热点事件舆情分析及预警策略研究,教育部人文社科基金,2020.1-2022.12,主持

多模态与混合模态视角下基于结构稀疏性与分层局部性的膀胱镜下肿瘤识别及智能诊断研究,北京市自然科学基金,2019.12-2022.12,主持

复杂模态视角下的半监督流形学习方法,惠园优秀青年学者,2020.3-2023.3,主持

基于分层局部性的半监督机器学习方法研究,对外经济贸易大学新进教师启动项目,2019.1-2021.12,主持

人工智能时代下的管理决策方法创新研究,对外经济贸易大学青年学术创新团队,2019.1-2021.12,参与


教授课程

《大数据分析技术基础》(BDT211

《大数据分析实践》(BDT401

商务数据处理与分析》(CMP135

《云计算及其应用》(CMP351)

《计算机应用基础》(CMP122

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