BIMSA > 包承龙
副教授 包承龙

单位: 清华丘成桐数学科学中心, 北京雁栖湖应用数学研究院

团队: 计算数学

邮箱: baochenglong@bimsa.cn

研究方向: 数据科学与应用数学

个人主页: https://matbc.github.io

教育经历

  • 2009 - 2014      新加坡国立大学      数学      博士      (Supervisor: Prof. Hui Ji and Prof. Defeng Sun)
  • 2005 - 2009      中山大学      数学      学士

工作经历

  • 2024 -      清华大学丘成桐数学科学中心      副教授
  • 2024 -      BIMSA      副研究员
  • 2021 - 2024      BIMSA      兼职助理教授
  • 2018 - 2024      清华大学丘成桐数学科学中心      助理教授
  • 2015 - 2017      新加坡国立大学      博士后

出版物

  • [1] Tangjun Wang, Chenglong Bao, Zuoqiang Shi, Convection-Diffusion Equation: a Theoretically Certified Framework for Neural Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence (2025)
  • [2] Y Zhou, J Xu, C Bao, C Ding, J Zhu, A Regularized Newton Method for Nonconvex Optimization with Global and Local Complexity Guarantees, arXiv preprint arXiv:2502.04799 (2025)
  • [3] Qi Zhang, Chenglong Bao, Hai Lin, Mingxu Hu, Averaging Orientations with Molecular Symmetry in Cryo-EM, SIAM Journal on Imaging Sciences, 17(4), 2174-2195 (2024)
  • [4] C Bao, D Zheng, Q Wu, N Yan, Z Shi, M Hu, H Peng, Q Hu, Z Yan, Y Peng, CryoPROS: Correcting Misalignment Caused by Preferred Orientation Using AI-generated Auxiliary Particles, (2024)
  • [5] Dihan Zheng, Shiqi Tang, Roland Wagner, Ronny Ramlau, Chenglong Bao, and Raymond H. Chan, PhaseNet: A Deep Learning Based Phase Reconstruction Method for Ground-Based Astronomy, SIAM Journal on Imaging Sciences, 17(3), 1511-1538 (2024)
  • [6] W Wan, J Pan, Y Zhang, C Bao, Z Shi, A Neural Network Framework for High-Dimensional Dynamic Unbalanced Optimal Transport, arXiv preprint arXiv:2409.13188 (2024)
  • [7] Xianglong Du, Weizhi Shao, Chenglong Bao, Linfeng Zhang, Jun Cheng, Fujie Tang, Revealing the molecular structures of $\alpha$-Al2O3(0001)-water interface by machine learning based computational vibrational spectroscopy, accepted by Journal of Chemical Physics (2024)
  • [8] Tangjun Wang, Zehao Dou, Chenglong Bao, Zuoqiang Shi, Diffusion Mechanism in Neural Network: Theory and Applications, IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(2), 667-680 (2024)
  • [9] Xiao Fan, Qi Zhang, Hui Zhang, Jianying Zhu, Lili Ju, Zuoqiang Shi, Mingxu Hu and Chenglong Bao, CryoTRANS: predicting high-resolution maps of rare conformations from self-supervised trajectories in cryo-EM, Communications Biology, 7(1), 1058 (2024)
  • [10] Huaming Ling, Chenglong Bao, Jiebo Song, Zuoqiang Shi, Fast and Scalable Semi-Supervised Learning for Multi-View Subspace Clustering, arXiv preprint arXiv:2408.05707 (2024)
  • [11] T Wang, C Bao, Z Shi, Interface Laplace Learning: Learnable Interface Term Helps Semi-Supervised Learning, arXiv preprint arXiv:2408.05419 (2024)
  • [12] Mengjia Cai, Jianying Zhu, Qi Zhang, Yu Xu, Zuoqiang Shi, Chenglong Bao, Mingxu Hu, Enhancing Density Maps by Removing the Majority of Particles in Single Particle Cryogenic Electron Microscopy Final Stacks, Journal of Visualized Experiments (JoVE), 207(207), e66617 (2024)
  • [13] Xiao Fan, Qi Zhang, Mingxu Hu, Hui Zhang, Jianying Zhu, Zuoqiang Shi, and Chenglong Bao, CryoTRANS: Quality Preserved Trajectory for Boosting Resolutions of Rare Conformations in cryo-EM, Preprint(2024)
  • [14] Dihan Zheng, Yihang Zou, Xiaowen Zhang, and Chenglong Bao, SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational Autoencoder, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle WA, USA, June 17-21, 2024(2024), 25889-25899
  • [15] Yueyao Li, Chenglong Bao and Wenxun Xing, Globalized distributionally robust optimization with multi core sets, arXiv preprint arXiv:2403.08169 (2024)
  • [16] Tangjun Wang, Zehao Dou, Chenglong Bao, and Zuoqiang Shi, Diffusion Mechanism in Residual Neural Network: Theory and Applications, IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(2024), 2, 667-680
  • [17] Chenglong Bao, Liang Chen, Jiahong Li, Zuowei Shen, Accelerated Gradient Methods with Gradient Restart: Global Linear Convergence(2024)
  • [18] Chenglong Bao; Chang Chen; Kai Jiang; Lingyun Qiu, Convergence analysis for Bregman iterations in minimizing a class of Landau free energy functionals, SIAM Journal on Numerical Analysis, 62(2024), 1, 476-499
  • [19] Dihan Zheng, Shiqi Tang, Roland Wagner, Ronny Ramlau, Chenglong Bao, Raymond Chan, PhaseNet: A Deep Learning Based Phase Reconstruction Method for Ground-based Astronomy, SIAM Journal on Imaging Sciences, 17(3), 1511-1538 (2024)
  • [20] Jianying Zhu, Qi Zhang, Hui Zhang, Zuoqiang Shi, Mingxu Hu, Chenglong Bao, A minority of final stacks yields superior amplitude in single-particle cryo-EM, Nature Communications, 7822(2023), 14
  • [21] Zhijun Zeng, Pipi Hu, Chenglong Bao, Yi Zhu, Zuoqiang Shi, Reconstruction of dynamical systems from data without time labels, arXiv preprint arXiv:2312.04038 (2023)
  • [22] Zanyu Li, Chenglong Bao, Riemannian Anderson Mixing Methods for Minimizing C2-Functions on Riemannian Manifolds(2023)
  • [23] Hui Zhang, Dihan Zheng, Qiurong Wu, Nieng Yan, Zuoqiang Shi, Mingxu Hu, and Chenglong Bao, Addressing Preferred Orientation in Single-Particle cryo-EM through AI-generated Auxiliary Particles(2023)
  • [24] Z Li, C Bao, Riemannian Anderson Mixing Methods for Minimizing -Functions on Riemannian Manifolds, arXiv preprint arXiv:2309.04091 (2023)
  • [25] Yuhao Zhou, Chenglong Bao, Chao Ding, Jun Zhu, A semismooth Newton based augmented Lagrangian method for nonsmooth optimization on matrix manifolds, Mathematical Programming, 201(1), 1-61 (2023)
  • [26] Wei Wan, Yuejin Zhang, Chenglong Bao, Bin Dong, and Zuoqiang Shi, A scalable deep learning approach for solving high-dimensional dynamic optimal transport, SIAM Journal on Scientific Computing, 45(4), B544-B563 (2023)
  • [27] C Bao, L Chen, J Li, The Global R-linear Convergence of Nesterov's Accelerated Gradient Method with Unknown Strongly Convex Parameter, arXiv preprint arXiv:2308.14080 (2023)
  • [28] Chenglong Bao, Liang Chen, Jiahong Li., The Global R-linear Convergence of Nesterov’s Accelerated Gradient Method with Unknown Strongly Convex Parameter(2023)
  • [29] Chenglong Bao, Qianxiao Li, Zuowei Shen, Cheng Tai, Lei Wu, and Xueshuang Xiang, Approximation analysis of convolutional neural networks, East Asian Journal on Applied Mathematics, 13(3), 524-549 (2023)
  • [30] T Wang, W Tao, C Bao, Z Shi, An axiomatized PDE model of deep neural networks, arXiv preprint arXiv:2307.12333 (2023)
  • [31] Convergence Analysis for Restarted Anderson Mixing and Beyond, Fuchao Wei, Chenglong Bao, Yang Liu, Guangwen Yang.(2023)
  • [32] F Wei, C Bao, Y Liu, G Yang, Convergence analysis for restarted anderson mixing and beyond, arXiv preprint arXiv:2307.02062 (2023)
  • [33] Jintao Xu, Chenglong Bao, and Wenxun Xing, Convergence Rates of Training Deep Neural Networks via Alternating Minimization Methods, Optimization Letters, 18(2023), 4, 909-923
  • [34] Chenglong Bao, Lingyun Qiu, and Rongqian Wang, Robust Full Waveform Inversion: A Source Wavelet Manipulation Perspective, SIAM Journal on Scientific Computing, 45(2023), 6, B753-B775
  • [35] Dihan Zheng, Xiaowen Zhang, Kaisheng Ma, and Chenglong Bao, Learn from Unpaired Data for Image Restoration: A Variational Bayes Approach, IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(2023), 5, 5889-5903
  • [36] Q Zhang, C Bao, H Lin, M Hu, The Moments of Orientation Estimations Considering Molecular Symmetry in Cryo-EM, arXiv e-prints, arXiv: 2301.05426, arXiv: 2301.05426 (2023)
  • [37] Fuchao Wei, Chenglong Bao, Yang Liu, and Guangwen Yang, A variant of anderson mixing with minimal memory size, Advances in Neural Information Processing Systems, 35, 16650-16663 (2022)
  • [38] Chenglong Bao, Jian-Feng Cai, Jae Kyu Choi, Bin Dong, Ke Wei, Improved harmonic incompatibility removal for susceptibility mapping via reduction of basis mismatch, Journal of Computational Mathematics, 40(6), 913-935 (2022)
  • [39] Yuexin Zhou, Chenglong Bao, Chao Ding, On the robust isolated calmness of a class of nonsmooth optimizations on Riemannian manifolds and its applications, arXiv preprint arXiv:2208.07518 (2022)
  • [40] Rongqian Wang, Ruixuan Zhang, Chenglong Bao, Lingyun Qiu, and Dinghui Yang, Adapting the residual dense network for seismic data denoising and upscaling, Geophysics, 87(4), V321-V340 (2022)
  • [41] Fuchao Wei, Chenglong Bao, and Yang Liu, A class of short-term recurrence Anderson mixing methods and their applications, (2022)
  • [42] An adaptive block Bregman proximal gradient method for computing stationary states of multicomponent phase-field crystal model, Chenglong Bao, Chang Chen, Kai Jiang. CSIAM Transactions on Applied Mathematics, 3(1), 133-171, 2022.
  • [43] Dihan Zheng, Chenglong Bao, Zuoqiang Shi, Haibin Ling, and Kaisheng Ma, Unsupervised Deep Learning Meets Chan-Vese Model, CSIAM Transactions on Applied Mathematics, 3(2022), 4, 662-691
  • [44] Huaming Ling, Chenglong Bao, Xin Liang, Zuoqiang Shi, Semi-Supervised Clustering via Dynamic Graph Structure Learning, arXiv:2209.02513(2022)
  • [45] Fuchao Wei, Chenglong Bao, and Yang Liu, Stochastic Anderson mixing for nonconvex stochastic optimization, Advances in Neural Information Processing Systems, 34, 22995-23008 (2021)
  • [46] Liyuan Wang, Mingtian Zhang, Zhongfan Jia, Qian Li, Chenglong Bao, and Kaisheng Ma, Afec: Active forgetting of negative transfer in continual learning, Advances in Neural Information Processing Systems, 34, 22379-22391 (2021)
  • [47] Rongqian Wang, Chenglong Bao, and Lingyun Qiu, Seismic Waveform Inversion with Source Manipulation, The 82nd EAGE Annual Conference & Exhibition(2021), 1-5
  • [48] Rongqian Wang, Ruixuan Zhang, Chenglong Bao, Lingyun Qiu, and Dinghui Yang, Seismic Data Denoising and Interpolation Using Deep Learning, The 82nd EAGE Annual Conference & Exhibition(2021), 1-5
  • [49] Linfeng Zhang, Xiaoman Zhang, Chenglong Bao, and Kaisheng Ma, Wavelet J-Net: A Frequency Perspective on Convolutional Neural Networks, , 1-8 (2021)
  • [50] Linfeng Zhang, Chenglong Bao, and Kaisheng Ma, Self-distillation: Towards efficient and compact neural networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(8), 4388-4403 (2021)
  • [51] Ruichen Jiang, Ya-Feng Liu, Chenglong Bao, Bo Jiang, Tightness and equivalence of semidefinite relaxations for MIMO detection, arXiv preprint arXiv:2102.04586 (2021)
  • [52] Zonghan Yang, Yang Liu, Chenglong Bao, and Zuoqiang Shi, Interpolation between residual and non-residual networks, , 119, 10736-10745 (2020)
  • [53] Chenglong Bao, Chang Chen, Kai Jiang, An adaptive block Bregman proximal gradient method for computing stationary states of multicomponent phase-field crystal model, arXiv preprint arXiv:2005.12604, 3(1), 133-171 (2020)
  • [54] Linfeng Zhang, Muzhou Yu, Tong Chen, Zuoqiang Shi, Chenglong Bao, and Kaisheng Ma, Auxiliary training: Towards accurate and robust models, , 372-381 (2020)
  • [55] Jiebo Song, Jia Li, Zhengan Yao, Kaisheng Ma, Chenglong Bao, Zero Norm based Analysis Model for Image Smoothing and Reconstruction, Inverse Problems, 36(2020), 11
  • [56] Kai Jiang, Wei Si, Chang Chen, Chenglong Bao, Efficient Numerical Methods for Computing the Stationary States of Phase Field Crystal Models, SIAM Journal on Scientific Computing, 42(2020), 6, B1350-B1357
  • [57] Dihan Zheng, Sia Huat Tan, Xiaowen Zhang, Zuoqiang Shi, Kaisheng Ma, and Chenglong Bao, An unsupervised deep learning approach for real-world image denoising, (2020)
  • [58] Shaokai Ye, Kailu Wu, Mu Zhou, Yunfei Yang, Sia Huat Tan, Kaidi Xu, Jiebo Song, Chenglong Bao, and Kaisheng Ma, Light-weight calibrator: a separable component for unsupervised domain adaptation, , 13736-13745 (2020)
  • [59] Linfeng Zhang, Yukang Shi, Zuoqiang Shi, Kaisheng Ma, and Chenglong Bao, Task-Oriented Feature Distillation, Advances in Neural Information Processing Systems, 33(2020), 14759-14771
  • [60] H Wang, Zihao and Zhou, Datong and Yang, Ming and Zhang, Yong and Bao ..., Robust Document Distance with Wasserstein-Fisher-Rao Metric, Asian Conference on Machine Learning, 721--736 (2020)
  • [61] Jinxiu Liang, Yong Xu, Chenglong Bao, Yuhui Quan, and Hui Ji, Barzilai–Borwein-based adaptive learning rate for deep learning, Pattern Recognition Letters, 128, 197-203 (2019)
  • [62] S Ye, SH Tan, K Xu, Y Wang, C Bao, K Ma, Brain-inspired reverse adversarial examples, arXiv preprint arXiv:1905.12171 (2019)
  • [63] C Bao, JK Choi, B Dong, Whole brain susceptibility mapping using harmonic incompatibility removal, SIAM Journal on Imaging Sciences, 12(1), 492-520 (2019)
  • [64] Linfeng Zhang, Zhanhong Tan, Jiebo Song, Jingwei Chen, Chenglong Bao, and Kaisheng Ma, SCAN: A Scalable Neural Networks Framework Towards Compact and Efficient Models, Advances in Neural Information Processing Systems, 32(2019), 4027-4036
  • [65] Linfeng Zhang, Jiebo Song, Anni Gao, Jingwei Chen, Chenglong Bao, and Kaisheng Ma, Be your own teacher: Improve the performance of convolutional neural networks via self distillation, , 3713-3722 (2019)
  • [66] Z Zhang, C Bao, H Ji, Z Shen, G Barbastathis, Mathematical Tools for Regularized Coherence Retrieval, Mathematics in Imaging, MW3D. 5 (2018)
  • [67] G Zhu, W Liu, C Bao, D Tong, H Ji, Z Shen, D Yang, L Lu, Investigating energy‐based pool structure selection in the structure ensemble modeling with experimental distance constraints: The example from a multidomain protein P ub1, Proteins: Structure, Function, and Bioinformatics, 86(5), 501-514 (2018)
  • [68] JK Choi, C Bao, X Zhang, PET-MRI joint reconstruction by joint sparsity based tight frame regularization, SIAM Journal on Imaging Sciences, 11(2), 1179-1204 (2018)
  • [69] Chenglong Bao, George Barbastathis, Hui Ji, Zuowei Shen, and Zhengyun Zhang, Coherence retrieval using trace regularization, SIAM Journal on Imaging Sciences, 11(1), 679-706 (2018)
  • [70] Z Zhang, C Bao, H Ji, Z Shen, G Barbastathis, Apparent coherence loss in phase space tomography, Journal of the Optical Society of America A, 34(11), 2025-2033 (2017)
  • [71] C Bao, B Dong, L Hou, Z Shen, X Zhang, X Zhang, Image restoration by minimizing zero norm of wavelet frame coefficients, Inverse problems, 32(11), 115004 (2016)
  • [72] C Wang, J Kipping, C Bao, H Ji, A Qiu, Cerebellar functional parcellation using sparse dictionary learning clustering, Frontiers in neuroscience, 10, 188 (2016)
  • [73] Y Quan, C Bao, H Ji, Equiangular kernel dictionary learning with applications to dynamic texture analysis, Proceedings of the IEEE Conference on Computer Vision and Pattern (2016)
  • [74] Chenglong Bao, Hui Ji, Yuhui Quan, and Zuowei Shen, Dictionary learning for sparse coding: Algorithms and convergence analysis, IEEE transactions on pattern analysis and machine intelligence, 38(7), 1356-1369 (2015)
  • [75] Chenglong Bao, Hui Ji, Zuowei Shen, Convergence analysis for iterative data-driven tight frame construction scheme, Applied and Computational Harmonic Analysis, 38(3), 510-523 (2015)
  • [76] C Bao, Y Quan, H Ji, A convergent incoherent dictionary learning algorithm for sparse coding, ECCV 2014 (2014)
  • [77] C Bao, H Ji, Y Quan, Z Shen, L0 norm based dictionary learning by proximal methods with global convergence, IEEE Conference on Computer Vision and Pattern Recognition (2014)
  • [78] C Bao, JF Cai, H Ji, Fast sparsity-based orthogonal dictionary learning for image restoration, Proceedings of the IEEE International Conference on Computer Vision, 3384-3391 (2013)
  • [79] C Bao, Y Wu, H Ling, H Ji, Real time robust l1 tracker using accelerated proximal gradient approach, IEEE conference on computer vision and pattern recognition, 1830-1837 (2012)
  • [80] C Bao, Sparse coding based image restoration and recognition: algorithms and analysis,
  • [81] S Zhu, C Bao, D Sun, Y Yuan, A Tight Convergence Analysis of Inexact Stochastic Proximal Point Algorithm for Stochastic Composite Optimization Problems

 

更新时间: 2025-08-31 17:00:07