2138cn太阳集团

2138cn太阳集团

师资队伍 2138cn太阳集团» 师资队伍» 大数据科学与技术系

2138cn太阳集团:张琦

副教授 / 硕士生导师

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,太阳集团2138手机版 2138cn太阳集团 副教授

2018年3月-2020年12月,太阳集团2138手机版 2138cn太阳集团,讲师


学术发表

? 部分期刊论文

[1] 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.

[2] 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.

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

[4] 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.

[5] 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.

[6] 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.

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

[8] 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.

[9] 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.

[10] 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

基于多尺度信息融合的膀胱肿瘤分类与智能分割研究,国家自然科学基金青年项目,2022.1-2024.12,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,教育部人文社科项目,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,太阳集团2138手机版新进教师启动项目,2019.1-2021.12,主持

人工智能时代下的管理决策方法创新研究,太阳集团2138手机版青年学术创新团队,2019.1-2021.12,参与


教授课程

《大数据分析技术基础》(BDT310

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

2138cn太阳集团-太阳集团2138手机版