Dongdong Chen

Dongdong Chen 陈东东

Research Associate

The University of Edinburgh, Edinburgh, UK

I am a Postdoctoral Research Associate on Computational Sensing and Machine Learning working with Prof. Mike Davies in the University of Edinburgh. Before that, I received my PhD in computer science from Sichuan University in 2017, under the supervision of Prof. Jiancheng Lv. My research interests are machine learning, deep learning, computational imaging, and inverse problems.

News

[09/01/2023] 1 paper (Sensing Theorems for Unsupervised Imaging) is accepted to Journal of Machine Learning Research (JMLR).

[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] Giving an invited talk in 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

		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
		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 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

MICCAI'18 BIA Best Paper Nomination, 2018
Sichuan University Outstanding Graduate Student, 2017
SCF Best Student Paper Award, 2017
IES Best Student Paper Award, 2014
National Scholarship, 2014

Academic Activities

Journal reviewer: IJCV, IEEE TNNLS, IEEE TIP, IEEE TMI, IEEE TCYB, IEEE TSMCS, IEEE TIM, IEEE TCBB, INFO, PRL, MedIA, EL, NEUCOM, etc.

Conf. PC member: VISAPP'20, VISAPP'21, MICCAI'20, MICAAI'21, MICCAI'22, AAAI'21, AAAI'22, ICML'22, NeurIPS'22, CVPR'23, ICML'23, etc.