丁洁 - 复旦大学 - 信息科学与工程学院

个人简介

学习工作经历 2019.09-至今: 复旦大学信息科学与工程学院，青年副研究员 2018.07-2019.08: 新加坡资讯通信研究院，研究员I级 2013.08-2018.06: 新加坡南洋理工大学，博士 2008.09-2012.06: 哈尔滨工程大学，本科

研究领域

复杂网络的控制和优化机器学习图神经网络水下图像处理、目标识别等

近期论文

查看导师最新文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

J. Ding, C. Wen , G. Li et al. “Target Controllability in Multilayer Networks via Minimum-Cost Maximum-Flow Method", IEEE Trans. on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2020.2995596, 2020. J. Ding, C. Wen, G. Li, X. Yang and T. Hu, “Sparsity-inspired optimal topology control of complex networks", IEEE Trans. on Network Science and Engineering, DOI: 10.1109/TNSE.2019.2954893，2019. J. Ding, C. Wen, G. Li and Z. Chen, “Key node selection in controlling complex networks via convex optimization”, IEEE Trans. on Cybernetics, DOI: 10.1109/TCYB.2018.2888953, 2019. G. Li*, J. Ding*, C. Wen and J. Huang, “Minimum cost control of directed networks with selectable control inputs”, IEEE Trans. on Cybernetics, 49(12)， 4431 - 4440， 2019. (*contributed equally) G. Li*, J. Ding*, C. Wen, L. Wang and F. Guo, “Controlling directed networks with evolving topologies”, IEEE Trans. on Control of Network Systems, 6(1)， 176 - 190, 2018. (*contributed equally) J. Ding, C. Wen and G. Li, “Key node selection in minimum cost control of complex networks”, Physica A: Statistical Mechanics and its Applications, 486, 251-261, 2017. J. Ding, C. Wen and G. Li, “Optimal control of weighted networks based on node connection strength”, The 27th IEEE International Symposium on Industrial Electronics, 2017. J. Ding, C. Wen, G. Li and C. Chua, “Locality sensitive batch feature extraction for high-dimensional Data”, Neurocomputing, 171, 664-672, 2016. G. Li, J. Ding, C. Wen and J. Pei, “Optimal control of complex networks based on matrix differentiation”, Europhysics Letters, 115(6), 68005, 2016. G. Li, C. Wen, W. Wei, Y. Xu, J. Ding, G. Zhao and L. Shi, “Trace ratio criterion for feature extraction in classification”, Mathematical Problems in Engineering, 2014, J. Ding, G. Li, C. Wen and C. Chua, “Min-max discriminant analysis based on gradient method for feature extraction”, 13th International Conference on Control, Automation, Robotics and Vision (ICARCV), Singapore,2014, 725204, 2014.