Yonggang Zhang (HKBU)
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Yonggang Zhang
PostDoc @ HKBU,
Address: Level 06, David C Lam Building (DLB)
Shaw Campus, Hong Kong Baptist University
Kowloon Tong, Hong Kong.
E-mail: csygzhang [at] comp.hkbu.edu.hk
[Google Scholar]
[Github]
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Biography
I am a machine learning researcher with research interests in trustworthy machine learning and reasoning,
e.g., adversarial learning, federated learning, and out-of-distribution learning.
My long-term goal is to develop trustworthy machines for human health promotion.
I am currently a postdoc at Hong Kong Baptist University, collaborating with Prof. Yiu-ming Cheung. I have completed my Ph.D. degree at USTC in Jun 2022.
I have served as reviewers for many top-tier conference and journals, such as International Conference on Machine Learning (ICML),
Advances in Neural Information Processing Systems (NeurIPS), International Conference on Learning Representations (ICLR),
Conference on Computer Vision and Pattern Recognition (CVPR), International Conference on Computer Vision (ICCV),
European Conference on Computer Vision (ECCV),
International Joint Conferences on Artificial Intelligence (IJCAI),
Association for the Advancement of Artificial Intelligence (AAAI),
IEEE Transactions on Image Processing (TIP-IEEE),
and IEEE Transactions on Neural Networks and Learning Systems (TNNLS).
Research Interests
My research interests lie in Adversarial Learning, Federated Learning, Out-Of-Distribution Learning.
Specifically, my current research work center around three major topics:
Adversarial Learning: Exploiting and harnessing the bugs of deep models.
Federated Learning: Enabling collaboration for multiple clients without data exposure.
Out-Of-Distribution Learning: Endowing deep models with robustness under various environments.
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