Yu Sun

Yu Sun is an assistant professor in the Department of Electrical and Computer Engineering at Johns Hopkins University with a joint appointment at the Data Science and Artificial Intelligence (DSAI) Institute. His research integrates machine learning, computer vision, optimization, and physics to advance computational imaging frameworks for reliable AI-driven imaging systems.

He earned a BEng in electronics and information from Sichuan University (2015) and a PhD in computer science from Washington University in St. Louis (2022), where his dissertation received the Turner Dissertation Award. His academic journey includes a postdoctoral fellowship at Caltech's Department of Computing and Mathematical Sciences.

Dr. Sun's research spans biomedical imaging, computational imaging, inverse problems, and machine learning, focusing on interpretable AI integration for next-generation imaging. His work bridges theoretical foundations with practical applications in medical and scientific imaging domains.

Recent publications reveal a dominant trend in diffusion models for scientific imaging problems, including plug-and-play priors for reconstruction (NeurIPS 2024) and benchmarks for diffusion-based scientific problem-solving (ICLR 2025 Spotlight), demonstrating cross-disciplinary impact from biomedical engineering to cell biology.

Key honors include:

• Turner Dissertation Award for doctoral contributions
• Rising Star Award from the Conference on Parsimony and Learning (CPAL, 2025)

He serves as a consultant associate editor for the IEEE Open Journal of Signal Processing and actively participates in the IEEE Signal Processing Society’s Computational Imaging Technical Committee. His research is supported by institutional funding through the Hopkins Computational Imaging Group.

The Hopkins Computational Imaging Group, which he leads, unites AI, mathematics, and data science to develop principled algorithms for imaging systems, with emphasis on biomedical applications and novel computational frameworks.