Dongdong Chen 陈东东

  Assistant Professor

  Heriot-Watt University, Edinburgh, UK

  d.chen@hw.ac.uk

 

I am an Assistant Professor in the School of Mathematical and Computer Sciences at Heriot-Watt University, Edinburgh.

My research lies at the intersection of machine learning, signal processing and data science. I am interested in exploiting low-dimensional models and high-dimensional geometry for challenges in representation learning, imaging science, and scientific discovery. Recently, my main interests have been in inverse problems in imaging and vision, small data learning, and foundation models for imaging and vision.

Hiring I am looking for motivated PhD students (full scholarship available) with aligned research interests to work with. Kindly email me with your resume/transcripts and a short research statement.

Prior to joining HWU, I held a postdoctoral position at the University of Edinburgh from 2018 to 2022. I then held an associate researcher (professor) position at Sichuan University until 2023, where I previously received my PhD in 2017.
 

News

  • [07/03/2024] Invited talk (Equivariant Imaging) at Mathematics for Deep Learning, Bath University, UK.
  • [27/02/2024] 1 paper (EMMA) is accepted to CVPR'24.
  • [09/01/2024] Invited talk (Equivariant Imaging) at IMS Young Mathematical Scientist Forum – Applied Math, National University of Singapore (NUS).
  • [19/10/2023] I will be presenting DeepInverse library at the SIAM Conference on Imaging Science in May 2024 in Atlanta, USA.
  • [24/07/2023] I will be joining Heriot-Watt University as an Assistant Professor.
  • [30/06/2023] Exciting to release DeepInverse, a PyTorch library for solving inverse problems with deep learning.
  • [21/04/2023] Invited talk (Equivariant Imaging) at MLMI, ISBI'23, Colombia.
  • [09/01/2023] 1 paper (Sensing Theorems for Unsupervised Imaging) is accepted to Journal of Machine Learning Research (JMLR).
  • [21/10/2022] I will be joining Sichuan University as an Associate Researcher (Professor).
  • [14/09/2022] 1 paper (Multi-Operator Imaging) is accepted to NeurIPS'22.
  • [01/09/2022] 1 paper (Imaging with Equivariant Deep Learning) is accepted to IEEE Signal Processing Magazine (SPM).
  • [28/03/2022] 1 paper (Robust Equivariant Imaging) is accepted to CVPR'22 (oral).
  • [07/01/2022] 1 paper is accepted to ISBI'22.
  • [08/12/2021] Invited talk (Equivariant Imaging) at the DAMTP/CMIH, University of Cambridge.
  • [14/10/2021] Our ICCV paper (Equivariant Imaging) is featured in ICCV Daily.
  • [29/07/2021] 1 paper is accepted to IEEE TMI.
  • [22/07/2021] 1 paper (Equivariant Imaging) is accepted to ICCV'21 (oral).
  • [03/07/2020] 1 paper is accepted to ECCV'20.
  • [23/06/2020] 1 paper is accepted to MICCAI'20.
  • [20/05/2020] 1 paper is accepted to IEEE TSYS.
  • [03/11/2019] 1 paper is accepted to IEEE TNNLS.
  • [29/06/2019] 1 paper is accepted to MICCAI'19.
  • [15/05/2019] 2 papers are accepted to SPARS'19.
  • [06/05/2019] 2 papers are accepted to MIDL'19.
  • [01/02/2019] 2 papers are accepted to ICASSP'19.
  • [22/08/2018] 2 papers are accepted to iTWIST'18.
  • [08/01/2018] I joined the University of Edinburgh and IDCOM as a Postdoctoral Research Associate.

  • Selected Publications

    Equal Contribution, * Corresponding Author(s)

       

    Image Fusion via Vision-Language Model
    Zixiang Zhao, Lilun Deng, Haowen Bai, Yukun Cui, Zhipeng Zhang, Yulun Zhang, Haotong Qin, Dongdong Chen*, Jiangshe Zhang, Peng Wang, Luc Van Gool
    tech report, 2024
    arXiv

       

    Equivariant Multi-Modality Image Fusion
    Zixiang Zhao, Haowen Bai, Jiangshe Zhang, Yulun Zhang, Kai Zhang, Shuang Xu, Dongdong Chen*, Radu Timofte, Luc Van Gool
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024
    arXiv

       

    Sensing Theorems for Unsupervised Learning in Linear Inverse Problems
    Julián Tachella, Dongdong Chen and Mike E. Davies
    Journal of Machine Learning Research (JMLR), 2023
    arXiv pdf (JMLR)

       

    Imaging With Equivariant Deep Learning: From unrolled network design to fully unsupervised learning
    Dongdong Chen, Mike E. Davies, Matthias J. Ehrhardt, Carola-Bibiane Schönlieb, Ferdia Sherry and Julián Tachella
    IEEE Signal Processing Magazine (IEEE SPM), 2023
    arXiv pdf (IEEE Xplore)

       

    Unsupervised Learning From Incomplete Measurements for Inverse Problems
    Julián Tachella, Dongdong Chen and Mike E. Davies
    Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS), 2022
    arXiv pdf (NeurIPS) code

       

    Robust Equivariant Imaging: a fully unsupervised framework for learning to image from noisy and partial measurements
    Dongdong Chen, Julián Tachella and Mike E. Davies
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Oral, 2022
    arXiv IEEE/CVF code talk

       

    Equivariant Imaging: Learning Beyond the Range Space
    Dongdong Chen, Julián Tachella and Mike E. Davies
    International Conference on Computer Vision (ICCV), Oral, 2021
    arXiv IEEE/CVF post code talk

       

    Dual Convolutional Neural Networks for Breast Mass Segmentation and Diagnosis in Mammography
    Heyi Li, Dongdong Chen, Bill Nailon, Mike Davies and Dave Laurenson
    IEEE Transactions on Medical Imaging (IEEE TMI), 2021
    pdf (IEEE Xplore)

       

    Deep Decomposition Learning for Inverse Imaging Problems
    Dongdong Chen and Mike E. Davies
    European Conference on Computer Vision (ECCV), 2020
    arXiv   code

       

    Compressive MRF reconstruction with neural proximal gradient iterations
    Dongdong Chen, Mike E Davies, and Mohammad Golbabaee
    International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2020
    arXiv   ISBI'22 extension   code

       

    COIN: Contrastive Identifier Network for Breast Mass Diagnosis in Mammography
    Heyi Li, Dongdong Chen, Bill Nailon, Mike Davies and Dave Laurenson
    tech report, 2020
    arXiv

       

    An Improved Dual-Channel Network to Eliminate Catastrophic Forgetting
    Dongbo Liu, Zhenan He, Dongdong Chen and Jian Cheng Lv
    IEEE Transactions on Systems, Man, and Cybernetics: Systems (IEEE TSMCS), accepted in May. 2020
    IEEE Xplore

       

    A Network Framework For Small-Sample Learning
    Dongbo Liu, Zhenan He, Dongdong Chen and Jian Cheng Lv
    IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), accepted in Nov. 2019
    IEEE Xplore

       

    Signed Laplacian Deep Learning with Adversarial Augmentation for Improved Mammography Diagnosis
    Heyi Li, Dongdong Chen, Bill Nailon, Mike Davies and Dave Laurenson
    International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019
    pdf

       

    Deep Fully Convolutional Network for MR Fingerprinting
    Dongdong Chen, Mohammad Golbabaee, Pedro A Gómez, Marion I Menzel and Mike E Davies
    International Conference on Medical Imaging with Deep Learning (MIDL), 2019
    pdf

       

    Spatio-temporal regularization for deep MR Fingerprinting
    Mohammad Golbabaee, Dongdong Chen, Mike E. Davies, Marion I. Menzel and Pedro A. Gomez
    International Conference on Medical Imaging with Deep Learning (MIDL), 2019
    pdf

       

    A fully convolutional network for MR Fingerprinting
    Dongdong Chen, Mohammad Golbabaee, Pedro A. Gomez, Marion I. Menzel and Mike E. Daveis
    The Signal Processing with Adaptive Sparse Structured Representations Workshop (SPARS), 2019
    arXiv

       

    Deep learning for Magnetic Resonance Fingerprinting
    Mohammad Golbabaee, Dongdong Chen, Pedro A. Gomez, Marion I. Menzel and Mike E. Davies
    The Signal Processing with Adaptive Sparse Structured Representations Workshop (SPARS), 2019
    arXiv

       

    A Deep Dual-Path Network For Improved Mammogram Image Processing
    Heyi Li, Dongdong Chen, Bill Nailon, Mike Davies and Dave Laurenson
    International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019
    IEEE Xplore   code

       

    Geometriy of Deep Learning for Magnetic Resonance Fingerprinting
    Mohammad Golbabaee, Dongdong Chen, Pedro A. Gomez, Marion I. Menzel and Mike E. Davies
    International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Oral, 2019
    arXiv

       

    Learning Discriminative Representation with Signed Laplacian Restricted Boltzmann Machine
    Dongdong Chen, Jiancheng Lv and Mike E. Daveis
    The international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques (iTWIST), 2018
    arXiv

       

    A deep learning approach for Magnetic Resonance Fingerprinting
    Mohammad Golbabaee, Dongdong Chen, Pedro A. Gomez, Marion I. Menzel and Mike E. Davies
    The international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques (iTWIST), 2018
    arXiv

       

    Improved Breast Mass Segmentation in Mammograms with Conditional Residual U-net
    Heyi Li, Dongdong Chen, Bill Nailon, Mike Davies and Dave Laurenson
    MICCAI Workshop (BIA), 2018. Best Paper Nomination
    arXiv

       

    Graph regularized Restricted Boltzmann Machines
    Dongdong Chen, Jian Cheng Lv and Zhang Yi
    IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), accepted in 2017. SCF Best Student Paper Award
    IEEE Xplore code

       

    Angle-based Embedding Quality Assessment Method for Manifold Learning
    Dongdong Chen, Jiancheng Lv, Jing Yin, Haixian Zhang and Xiaojie Li
    Neural Computing & Applications (NCAA), accepted in 2017
    pdf

       

    Unsupervised Multi-Manifold Clustering by Learning Deep Representation
    Dongdong Chen, Jiancheng Lv and Zhang Yi
    AAAI Workshop, 2017
    pdf

       

    Angle-based Outlier Detection Algorithm with More Stable Relationships
    Xiaojie Li, Jian Cheng Lv and Dongdong Chen
    The Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES), 2014. Best Student Paper Award
    pdf

       

    A Local Non-negative Pursuit Method for Intrinsic Manifold Structure Preservation
    Dongdong Chen, Jian Cheng Lv and Zhang Yi
    AAAI Conference on Artificial Intelligence (AAAI), 2014
    pdf code

      

    Awards and Honors

      

    Academic Activities

      

    Teaching


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