My research focuses on the interaction among machine learning, computer vision, optimization, and quantum computing.
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.
- Data-Driven Energy Control
- AI for Fair Education
- AI for Affordable Healthcare
I am a Ph.D. candidate at the Department of Computer Science, University of Oxford. I am 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.
- AAAI Conference on Artificial Intelligence 2021 - 2022
- CVPR: IEEE Conference on Computer Vision and Pattern Recognition 2022
- ICCV: IEEE International Conference on Computer Vision 2021
- ICPR: International Conference on Pattern Recognition 2020
- MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention 2019 - 2021
Journal Reviewers (Invited):
- Artificial Intelligence in Medicine 2021-2022
- IEEE Access 2019 - 2021
- IEEE Transactions on Circuits and Systems for Video Technology 2019 - 2020
- IEEE Transactions on Medical Imaging 2019 - 2021
- IEEE Transactions on Neural Networks and Learning Systems 2020 - 2021
- IEEE Transactions on Pattern Analysis and Machine Intelligence 2021
- Pattern Recognition 2020 - 2021
- Computer Science Student Ambassador, University of Oxford
- Databases MT 2019
- Discrete Mathematics MT 2019
- Machine Learning MT 2019
- Imperative Programming Parts 1 and 2, HT 2020
Self−Supervised Multi−Task Representation Learning for Sequential Medical Images
Nanqing Dong‚ Michael Kampffmeyer and Irina Voiculescu
In European Conference on Machine Learning. Pages 779−794. 2021.
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.
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. 2021.