Chen Chen (陈晨)

Hi! I’m a final‑year PhD student in the School of Computer Science at the University of Sydney, and I’m fortunate to be advised by Associate Professor Chang Xu.
My research focuses on enhancing the reliability of generative AI, with an emphasis on image and video diffusion models. I have worked on a range of projects advancing privacy, robustness, and trustworthiness in generative models (see publications for details). Beyond these projects, my interests span a wide range of topics in computer vision and machine learning. I am committed to exploring new research directions, adapting to emerging challenges, and rapidly expanding my expertise to make impactful contributions.
Contact: cchenleicester[at]gmail.com
news
Sep 18, 2025 | [Paper accepted] Our Amazon internship work “SRSR: Enhancing Semantic Accuracy in Real-World Image Super-Resolution with Spatially Re-Focused Text-Conditioning” has been accepted at NeurIPS 2025. |
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Aug 31, 2025 | [Marathon] Proud to have finished the 2025 Sydney Marathon (42.195 km), one of the Abbott World Marathon Majors. |
May 05, 2025 | [Award] Honored to be awarded the PhD Teaching Fellowship (Associate Lecturer Level) at the School of Computer Science, University of Sydney. |
Mar 02, 2025 | [Paper accepted] Our paper “Investigating Memorization in Video Diffusion Models” has been accepted at DATA-FM Workshop @ ICLR 2025. |
Feb 27, 2025 | [Paper accepted] Our paper “Enhancing Privacy-Utility Trade-offs to Mitigate Memorization in Diffusion Models” has been accepted at CVPR 2025. |
Feb 21, 2025 | [Internship] I have completed my Applied Scientist internship at Amazon and received an inclined vote for return. |
Feb 11, 2025 | [Paper accepted] Our paper “Exploring Local Memorization in Diffusion Models via Bright Ending Attention” has been accepted at ICLR 2025 as a Spotlight paper (Top 5.1%). |
selected publications
- NeurIPS 2025SRSR: Enhancing Semantic Accuracy in Real-World Image Super-Resolution with Spatially Re-Focused Text-ConditioningIn Neural Information Processing Systems (NeurIPS), 2025