Chaoyue Wang
Hi! My name is Chaoyue Wang, a researcher specializing in generative models and artificial intelligence-generated content (AIGC). Prior to that, I worked as a postdoctoral researcher in machine learning and generative modeling at the School of Computer Science, University of Sydney. I obtained my bachelor's degree from Tianjin University in 2014 and my PhD degree from the University of Technology Sydney in 2018.
My research primarily focuses on addressing three fundamental challenges in generative modeling: "Generative Model Training," "Controllable and Interpretable Generative Models," and "Generalized Generative Applications." I have authored over 30 academic works published in prestigious journals and conferences such as IEEE T-PAMI, T-EVC, IEEE T-IP, NeurIPS, CVPR, ECCV, IJCAI, among others. My research has garnered recognition from the academic community, including receiving the Distinguished Student Paper Award at IJCAI-17 for my first-author work on TDGAN and having my first-author work on E-GAN selected as Best of the Physics arXiv by MIT TR and Best of GAN Papers in the Year 2018 by DTRANSPOSED. Additionally, I played a leading role in writing the first official white paper on AIGC.
My current research interests center on developing foundation generative models that are generalizable, interpretable, and controllable. I currently focus on conducting cutting-edge research in the directions of "Pretraining of Foundation Generative Models," "Disentangled Feature Learning within Pretrained Generative Models," and "Multimodal Data Fine-tuning of Pre-trained Generative Models." If you are also interested in these topics or related applications, please feel free to contact me at chaoyue[dot]wang[at]outlook[dot]com.
