杨连平
杨连平
职称：副教授
研究方向：应用数学和人工智能
研究方向简介
东大应数指导团队的学术发展理念是把数学变成技术、将数据变成知识，以凭借数学思维去突破现代人工智能的核心关键问题为目标。东大应数指导团队培养了一批批优秀的研究生被阿里巴巴、腾讯、百度、华为等顶尖企业高薪录用并获得很高的评价和赞赏！从2017年开始，华为为应数团队的学生指定提供两个华为奖学金指标。东大应数期待努力的你成为我们大家庭的一员，在现代人工智能技术的发展中展现自己的力量！和学术界同仁共同努力为更美好的未来贡献数学界一份子的力量！目前本团队主要在数学与机器视觉、人工智能交叉领域开展研究，当前研究的主要问题与项目有：1)面像识别;2)姿态识别3)生成对抗网络4）超分辨重建5)国家重大专项“科技冬奥”项目.
发表文章：
Yang, L., Zhang, H., Wei, P., Sun, Y., & Zhang, X. (2021). DC-EDN: densely connected encoder-decoder network with reinforced depthwise convolution for face alignment. Applied Intelligence.
Niu, B., Wen, W., Ren, W., Zhang, X., Yang, L., Wang, S., … Shen, H. (2020). Single Image Super-Resolution via a Holistic Attention Network. In A. Vedaldi, H. Bischof, T. Brox, & J.-M. Frahm (Eds.), Computer Vision -- ECCV 2020 (pp. 191–207). Cham: Springer International Publishing.
Yang, L., Qin, Y., & Zhang, X. (2020). Lightweight densely connected residual network for human pose estimation. Journal of Real-Time Image Processing.
杨连平 孙玉波张红良 李封 张祥德. (2020).基于编解码残差的人体关键点匹配网络.计算机科学, 47(6), 114–120.
Yang, L., Li, Y., Duan, X., & Zhang, X. (2017). Face Detection with Better Representation Using a Multi-region WR-Inception Network Model. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10568 LNCS).
Yang, L., & Zhang, W. (2017). A Multiresolution Graphical Representation for Similarity Relationship and Multiresolution Clustering for Biological Sequences. Journal of Computational Biology, 24(4).
Zhang, M., Yang, L., Ren, J., Ahlgren, N. A., Fuhrman, J. A., & Sun, F. (2017). Prediction of virus-host infectious association by supervised learning methods. BMC Bioinformatics, 18.
Fu, H., Yang, L., & Zhang, X. (2017). Noncoding variants functional prioritization methods based on predicted regulatory factor binding sites. Current Genomics, 18(4).
Fu, H., Yang, L., & Zhang, X. (2016). A prioritization method for identifying disease-causative gene based on hyper graph network. Current Proteomics, 13(2).
Yang, L., Zhang, X., Fu, H., & Yang, C. (2016). An estimator for local analysis of genome based on the minimal absent word. Journal of Theoretical Biology, 395, 23–30.
Fu, H., Yang, L., & Zhang, X. (2015). An RNA secondary structure prediction method based on minimum and suboptimal free energy structures. Journal of Theoretical Biology, 380.
Zhu, H., Zhao, C., Zhang, X., & Yang, L. (2014). An image encryption scheme using generalized Arnold map and affine cipher. Optik, 125(22).
Yang, L., Zhang, X., & Zhu, H. (2013). Alignment free comparison: K word voting model and its applications. Journal of Theoretical Biology, 335.
Zhu, H., Zhao, C., Zhang, X., & Yang, L. (2013). A novel iris and chaos-based random number generator. Computers and Security, 36.
Yang, L., Zhang, X., Wang, T., & Zhu, H. (2013). Large local analysis of the unaligned genome and its application. Journal of Computational Biology, 20(1).
Yang, L., Zhang, X., & Zhu, H. (2012). Alignment free comparison: Similarity distribution between the DNA primary sequences based on the shortest absent word. Journal of Theoretical Biology, 295.
Huang, Y., Yang, L., & Wang, T. (2011). Phylogenetic analysis of DNA sequences based on the generalized pseudo-amino acid composition. Journal of Theoretical Biology, 269(1).
Zhang, X.-D., Tang, Q.-S., Zhu, H.-G., & Yang, L.-P. (2010). A combinatorial summation method for a class of multiple series. Dongbei Daxue Xuebao/Journal of Northeastern University, 31(7).
Yang, L., Chang, G., Zhang, X., & Wang, T. (2010). Use of the Burrows-Wheeler similarity distribution to the comparison of the proteins. Amino Acids, 39(3).
Yang, L., Zhang, X., & Wang, T. (2010). The Burrows-Wheeler similarity distribution between biological sequences based on Burrows-Wheeler transform. Journal of Theoretical Biology, 262(4). Zhang, S., Yang, L., & Wang, T. (2009). Use of information discrepancy measure to compare protein secondary structures. Journal of Molecular Structure: THEOCHEM, 909(1–3).
Zhu, H., Zhang, X., Yang, L., & Tang, Q. (2009). Fingerprint-based random sequence generator. Jisuanji Yanjiu Yu Fazhan/Computer Research and Development, 46(11).
Zhang, X.-D., Zhu, H.-G., Yang, L.-P., & Tang, Q.-S. (2007). Biometric-based random sequence generator. Dongbei Daxue Xuebao/Journal of Northeastern University, 28(7).