BIMSA > Chenglong Bao
Assistant Professor Chenglong Bao

Affiliation: BIMSA , YMSC

Group: Computational Mathematics

Email: baochenglong@bimsa.cn

Research Field: Data Sciences and Applied Matheamtics

Webpage: https://matbc.github.io

Education Experience

  • 2009 - 2014      National University of Singapore      Matheamtics      Ph.D      (Supervisor: Prof. Hui Ji and Prof. Defeng Sun)
  • 2005 - 2009      Sun Yat-Sen University      Mathematics      Bachelor

Work Experience

  • 2021 -      BIMSA      Adjunct Assistant Professor
  • 2018 -      Yau Mathematical Sciences Center      Assistant Professor
  • 2015 - 2017      National University of Singapore      Research Fellow

Publication

  • [1] Yueyao Li, Chenglong Bao and Wenxun Xing, Globalized distributionally robust optimization with multi core sets, arXiv:2403.08169 (2024)
  • [2] 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)
  • [3] 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, 207 (2024)
  • [4] 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)
  • [5] 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
  • [6] 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)
  • [7] 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
  • [8] Chenglong Bao, Liang Chen, Jiahong Li, Zuowei Shen, Accelerated Gradient Methods with Gradient Restart: Global Linear Convergence(2024)
  • [9] 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)
  • [10] 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
  • [11] 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(2024), 3
  • [12] 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
  • [13] Zanyu Li, Chenglong Bao, Riemannian Anderson Mixing Methods for Minimizing C2-Functions on Riemannian Manifolds(2023)
  • [14] 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)
  • [15] Chenglong Bao, Liang Chen, Jiahong Li., The Global R-linear Convergence of Nesterov’s Accelerated Gradient Method with Unknown Strongly Convex Parameter(2023)
  • [16] Convergence Analysis for Restarted Anderson Mixing and Beyond, Fuchao Wei, Chenglong Bao, Yang Liu, Guangwen Yang.(2023)
  • [17] 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
  • [18] Yuhao Zhou, Chenglong Bao, Chao Ding, Jun Zhu, A Semismooth Newton based Augmented Lagrangian Method for Nonsmooth Optimization on Matrix Manifolds, Mathematical Programming, 201(2023), 1-61
  • [19] Qi Zhang, Chenglong Bao, Hai Lin, Mingxu Hu, Averaging Orientations with Molecular Symmetry in Cryo-EM, accepted by SIAM Journal on Imaging Sciences(2023)
  • [20] 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(2023), 4, B544-B563
  • [21] 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)(2023), 524-549
  • [22] 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
  • [23] 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
  • [24] 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.
  • [25] Chenglong Bao, Chang Chen, Kai Jiang, An adaptive block Bregman proximal gradient method for computing stationary states of multicomponent phase-field crystal model, CSIAM Transactions on Applied Mathematics, 3(1)(2022), 133-171
  • [26] 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)(2022), 914-937
  • [27] 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)(2022), V321-V340
  • [28] Huaming Ling, Chenglong Bao, Xin Liang, Zuoqiang Shi, Semi-Supervised Clustering via Dynamic Graph Structure Learning, arXiv:2209.02513(2022)
  • [29] Fuchao Wei, Chenglong Bao, and Yang Liu, A Class of Short-term Recurrence Anderson Mixing Methods and Their Applications, The Tenth International Conference on Learning Representations (ICLR), Virtual Event(2022)
  • [30] 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(2022), 16650-16663
  • [31] 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
  • [32] 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)(2022), 4388-4403
  • [33] Yuexin Zhou, Chenglong Bao, Chao Ding, On the robust isolated calmness of a class of nonsmooth optimizations on Riemannian manifolds and its applications(2022)
  • [34] 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
  • [35] Rongqian Wang, Chenglong Bao, and Lingyun Qiu, Seismic Waveform Inversion with Source Manipulation, The 82nd EAGE Annual Conference & Exhibition(2021), 1-5
  • [36] Fuchao Wei, Chenglong Bao, and Yang Liu, Stochastic Anderson Mixing for Nonconvex Stochastic Optimization, Advances in Neural Information Processing Systems, 34(2021), 22995-23008
  • [37] 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(2021), 22379-22391
  • [38] Dihan Zheng, Sia Huat Tan, Xiaowen Zhang, Zuoqiang Shi, Kaisheng Ma, and Chenglong Bao, An unsupervised deep learning approach for real-world image denoising, The Nineth International Conference on Learning Representations (ICLR), Virtual Event, May 3-7, 2021(2021)
  • [39] Linfeng Zhang, Xiaoman Zhang, Chenglong Bao, and Kaisheng Ma, Wavelet J-Net: A Frequency Perspective on Convolutional Neural Networks, 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China(2021), 1-8
  • [40] 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
  • [41] 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
  • [42] 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, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020(2020), 13736-13745
  • [43] Zonghan Yang, Yang Liu, Chenglong Bao, and Zuoqiang Shi, Interpolation between Residual and Non-Residual Networks, Proceedings of Machine Learning Research, 119(2020), 10736-10745
  • [44] 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
  • [45] 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
  • [46] 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, 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), 2019(2019), 3713-3722
  • [47] Jinxiu Liang, Yong Xu, Chenglong Bao, Yuhui Quan, and Hui Ji, Barzilai–Borwein-based adaptive learning rate for deep learning, Pattern Recognition Letters, 128(2019), 197-203
  • [48] Chenglong Bao, George Barbastathis, Hui Ji, Zuowei Shen, and Zhengyun Zhang, Coherence Retrieval Using Trace Regularization, SIAM J. IMAGING SCIENCES, 11(2018), 1, 679-706
  • [49] 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(2016), 7
  • [50] Chenglong Bao, Hui Ji, Zuowei Shen, Convergence analysis for iterative data-driven tight frame construction scheme, Appl. Comput. Harmon. Anal., 38(2015), 510-523

 

Update Time: 2024-10-22 08:08:17