Nanqing Dong
Interests
During my time at Oxford, my research focused on the interaction among machine learning, computer vision, optimization, and quantum computing.
My full publication list can be found in my Google Scholar page.
I have been working 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
- Distributed / Federated Learning
- distributed optimization
- federated partially supervised learning
- federated learning with blockchain
- Learning-Based Applications
- autonomous driving
- data-driven energy control
- quantum machine learning
- financial machine learning
- AI for Science
- AI for agricultural science
- AI for biological science
- AI for medical science
I am looking for research interns and PhD students on AI for Science.
Biography
Please contact me by nanqing.dong@gmail.com for academic and research purposes.
I am currently a Young Principal Investigator at Shanghai, China (equivalent to the Lecturer in the UK or the Assistant Professor in the USA). I finished my PhD training at the Department of Computer Science, University of Oxford. I was 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
- ECML: European Conference on Machine Learning
- ICASSP: IEEE International Conference on Acoustics, Speech and Signal Processing
- ICCV: IEEE International Conference on Computer Vision
- ICLR: International Conference on Learning Representations
- ICPR: International Conference on Pattern Recognition
- MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention
- NIPS: Annual Conference on Neural Information Processing Systems
Journal Reviewers (Invited):
- Artificial Intelligence in Medicine
- Expert Systems with Applications
- 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)
- Information Fusion
- International Journal of Computer Vision (IJCV)
- Medical Image Analysis (MedIA)
- 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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Federated Partially Supervised Learning with Limited Decentralized Medical Images
Nanqing Dong‚ Michaek Kampffmeyer‚ Irina Voiculescu and Eric Xing
In IEEE Transactions on Medical Imaging. December, 2022.
Details about Federated Partially Supervised Learning with Limited Decentralized Medical Images | BibTeX data for Federated Partially Supervised Learning with Limited Decentralized Medical Images | Link to Federated Partially Supervised Learning with Limited Decentralized Medical Images
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Computationally−Efficient Vision Transformer for Medical Image Semantic Segmentation via Dual Pseudo−Label Supervision
Ziyang Wang‚ Nanqing Dong and Irina Voiculescu
IEEE International Conference on Image Processing. October, 2022.
Details about Computationally−Efficient Vision Transformer for Medical Image Semantic Segmentation via Dual Pseudo−Label Supervision | BibTeX data for Computationally−Efficient Vision Transformer for Medical Image Semantic Segmentation via Dual Pseudo−Label Supervision | Link to Computationally−Efficient Vision Transformer for Medical Image Semantic Segmentation via Dual Pseudo−Label Supervision
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Negational Symmetry of Quantum Neural Networks for Binary Pattern Classification
Nanqing Dong‚ Michaek Kampffmeyer‚ Irina Voiculescu and Eric Xing
In Pattern Recognition. Vol. 129. Pages 108750. 2022.
Details about Negational Symmetry of Quantum Neural Networks for Binary Pattern Classification | BibTeX data for Negational Symmetry of Quantum Neural Networks for Binary Pattern Classification | DOI (https://doi.org/10.1016/j.patcog.2022.108750)