Nanqing Dong
Nanqing Dong
Wolfson Building, Parks Road, Oxford OX1 3QD
Interests
My research focuses on the interaction among machine learning, computer vision, optimization, and quantum computing.
My full publication list can be found in my Google Scholar page.
Recently, I am interested in the following areas.
- Label-Efficient Learning Paradigms
- unsupervised/self-supervised learning
- partially supervised learning
- transfer learning/domain adaptation
- Learning-Based Computer Vision
- semantic understanding
- image restoration & enhancement
- medical image analysis
I have been actively involved in the research in the following areas.
- Quantum Machine Learning
- Distributed/Federated Learning
Meanwhile, I am collaborating with researchers with different backgrounds to apply AI/ML in the following areas.
- AI for Affordable Healthcare
- AI for Fair Education
- Data-Driven Energy Control
- Financial Machine Learning
Biography
I am a Ph.D. candidate at the Department of Computer Science, University of Oxford. I am generously funded by the Department of Computer Science Scholarship. Prior to Oxford, I did research at the Machine Learning Department, Carnegie Mellon University. I obtained my M.S. degree from the Department of Statistical Science, Cornell University.
Academic Service
Conference Reviewers (Invited):
- AAAI Conference on Artificial Intelligence
- CVPR: IEEE Conference on Computer Vision and Pattern Recognition
- ECCV: European Conference on Computer Vision
- ICCV: IEEE International Conference on Computer Vision
- ICPR: International Conference on Pattern Recognition
- MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention
Journal Reviewers (Invited):
- Artificial Intelligence in Medicine
- IEEE Access
- IEEE Journal of Biomedical and Health Informatics (JBHI)
- IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)
- IEEE Transactions on Medical Imaging (TMI)
- IEEE Transactions on Multimedia (TMM)
- IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
- IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
- International Journal of Computer Vision (IJCV)
- Neural Computing and Applications
- Neural Networks
- Pattern Recognition
Student Ambassador:
- Computer Science Student Ambassador, University of Oxford
Teaching
Tutor:
- Artificial Intelligence, HT 2022
- Computer Networks, TT 2022
Supervisor:
- Group Design Practical, HT 2022
- Group Design Practical, TT 2022
Demonstrator:
- Databases, MT 2019
- Machine Learning, MT 2019
- Imperative Programming Parts 1 and 2, HT 2020
Teaching Assistant:
- Databases, MT 2019
- Discrete Mathematics, MT 2019
Selected Publications
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Towards Robust Partially Supervised Multi−Structure Medical Image Segmentation on Small−Scale Data
Nanqing Dong‚ Michael Kampffmeyer‚ Xiaodan Liang‚ Min Xu‚ Irina Voiculescu and Eric Xing
In Applied Soft Computing. 2022.
Details about Towards Robust Partially Supervised Multi−Structure Medical Image Segmentation on Small−Scale Data | BibTeX data for Towards Robust Partially Supervised Multi−Structure Medical Image Segmentation on Small−Scale Data | DOI (https://doi.org/10.1016/j.asoc.2021.108074)
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Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images
Nanqing Dong‚ Michael Kampffmeyer and Irina Voiculescu
In International Conference on Medical Image Computing and Computer Assisted Intervention. 2022.
Details about Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images | BibTeX data for Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images
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Federated Contrastive Learning for Decentralized Unlabeled Medical Images
Nanqing Dong and Irina Voiculescu
In International Conference on Medical Image Computing and Computer−Assisted Intervention. Pages 379−387. 2021.
Details about Federated Contrastive Learning for Decentralized Unlabeled Medical Images | BibTeX data for Federated Contrastive Learning for Decentralized Unlabeled Medical Images | DOI (https://doi.org/10.1007/978-3-030-87199-4_36)