Zhenghan Fang

I'm a final year PhD student at Johns Hopkins University in Baltimore, affiliated with the Department of Biomedical Engineering and the Mathematical Institute for Data Science. I'm advised by Jeremias Sulam. I have worked closely with Mateo Díaz, Xu Li, Peter van Zijl, and Sam Buchanan. I am a recipient of the Kavli NDI Distinguished Graduate Student Fellowship (2022-2024) and the MINDS Data Science Fellowship (2025 Fall).

Previously, I received my M.S. in Biomedical Engineering from University of North Carolina at Chapel Hill in 2019, where I worked with Dinggang Shen, Yong Chen, and Mingxia Liu, and my B.S. in Electronic Engineering from Fudan University in 2017, where I worked with Jinhua Yu. I worked as an algorithm engineer at Keya Medical from 2019 to 2021 and did summer internships at Amazon in 2024 and 2025.

Email / Scholar / Github

News
2026
Nov 1-4	I will present at the 2026 INFORMS Annual Meeting in San Francisco, California.
Oct 25-28	I will present at the 2026 Asilomar Conference on Signals, Systems, and Computers in Pacific Grove, California.
Oct 21-24	I will give an invited talk at the 2026 BMES Annual Meeting in Orlando, Florida.

Research

I'm interested in machine learning, inverse problems, medical imaging, and computer vision. My research focuses on theory and algorithms for interpretable and trustworthy machine learning for inverse problems, as well as deep learning methods for imaging neural fibers in the brain with magnetic resonance imaging.

Publications
	Learning regularization functionals for inverse problems: A comparative study J. Hertrich, H. S. Wong, A. Denker, S. Ducotterd, Zhenghan Fang, M. Haltmeier, Ž. Kereta, E. Kobler, O. Leong, M. S. Salehi, C.-B. Schönlieb, J. Schwab, Z. Shumaylov, Jeremias Sulam, G. S. Wache, M. Zach, Y. Zhang, M. J. Ehrhardt, S. Neumayer Handbook of Numerical Analysis, 2026 paper / code
	Learning Affine-Equivariant Proximal Operators O. Savir, Zhenghan Fang, Jeremias Sulam IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2026 paper / code
	ProxT2I: Efficient Reward-Guided Text-to-Image Generation via Proximal Diffusion Zhenghan Fang, J. Zheng, Q. Gao, X. Gao, Jeremias Sulam arXiv, 2025 paper
	Beyond Scores: Proximal Diffusion Models Zhenghan Fang, Mateo Díaz, Sam Buchanan, Jeremias Sulam NeurIPS, 2025 paper / code
	What's in a Prior? Learned Proximal Networks for Inverse Problems Zhenghan Fang, Sam Buchanan, Jeremias Sulam ICLR, 2024 project page / talk / slides / poster / paper / code
	ProxiMO: Proximal Multi-operator Networks for Quantitative Susceptibility Mapping S. Orenstein, Zhenghan Fang, H. G. Shin, Peter van Zijl, Xu Li, Jeremias Sulam MICCAI Workshop on Machine Learning in Clinical Neuroimaging (MLCN), 2024 Best Paper, 2nd Place paper / code
	Masked conditional diffusion model for enhancing deepfake detection T. Chen, S. Yang, S. Hu, Zhenghan Fang, Y. Fu, X. Wu, X. Wang International Joint Conference on Neural Networks (IJCNN), 2024 paper
	WaveSep: A Flexible Wavelet-based Approach for Source Separation in Susceptibility Imaging Zhenghan Fang, H. G. Shin, Peter van Zijl, Xu Li, Jeremias Sulam MICCAI Workshop on Machine Learning in Clinical Neuroimaging (MLCN), 2023 paper / code
	DeepSTI: Towards tensor reconstruction using fewer orientations in susceptibility tensor imaging Zhenghan Fang, Kuo-Wei Lai, Peter van Zijl, Xu Li, Jeremias Sulam Medical Image Analysis, 2023 paper / code
	Deep learning-assisted preclinical MR fingerprinting for sub-millimeter T1 and T2 mapping of entire macaque brain Y. Gu, Y. Pan, Zhenghan Fang, L. Ma, Y. Zhu, C. Androjna, K. Zhong, X. Yu, D. Shen Magnetic Resonance in Medicine, 2023 paper
	LMSA-Net: A lightweight multi-scale aware network for retinal vessel segmentation J. Chen, J. Wan, Zhenghan Fang, L. Wei International Journal of Imaging Systems and Technology, 2023 paper
	Annotation-efficient COVID-19 pneumonia lesion segmentation using error-aware unified semi-supervised and active learning Zhenghan Fang, J. Bai, X. Guo, X. Wang, F. Gao, H. Y. Yang, B. Kong, Y. Hou, K. Cao, Q. Song, J. Xia, Y. Yin IEEE Transactions on Artificial Intelligence, 2022 paper
	Automatic Brain Extraction from 3D Fetal MR Image with Deep Learning-based Multi-step Framework J. Chen, Zhenghan Fang, G. Zhang, L. Ling, G. Li, H. Zhang, L. Wang Computerized Medical Imaging and Graphics, 2021 paper
	Harmonized neonatal brain MR image segmentation model for cross-site datasets J. Chen, Y. Sun, Zhenghan Fang, W. Lin, G. Li, L. Wang, UNC UMN Baby Connectome Project Consortium Biomedical Signal Processing and Control, 2021 paper
	Submillimeter MR fingerprinting using deep learning based tissue quantification Zhenghan Fang, Y. Chen, S. C. Hung, X. Zhang, W. Lin, D. Shen Magnetic Resonance in Medicine, 2020 (*co-first authors) paper
	High-resolution 3D MR Fingerprinting using parallel imaging and deep learning Y. Chen, Zhenghan Fang*, S. C. Hung, W. T. Chang, D. Shen, W. Lin NeuroImage, 2020 (co-first authors) paper
	Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy L. Li, L. Qin, Z. Xu, Y. Yin, X. Wang, B. Kong, J. Bai, Y. Lu, Zhenghan Fang, Q. Song, K. Cao Radiology, 2020 paper
	RCA-U-Net: Residual Channel Attention U-Net for Fast Tissue Quantification in Magnetic Resonance Fingerprinting Zhenghan Fang, Y. Chen, D. Nie, W. Lin, D. Shen Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2019 (early accept) paper
	CNS: CycleGAN-Assisted Neonatal Segmentation Model for Cross-Datasets J. Chen, Zhenghan Fang, D. Xiao, D. T. Bui, K. H. Thung, X. Li, J. Yang, W. Lin, G. Li, D. Shen, L. Wang International Workshop on Graph Learning in Medical Imaging (GLMI), MICCAI Workshop, 2019 paper
	Deep Learning for Fast and Spatially-Constrained Tissue Quantification from Highly-Accelerated Data in Magnetic Resonance Fingerprinting Zhenghan Fang, Y. Chen, M. Liu, L. Xiang, Q. Zhang, Q. Wang, W. Lin, D. Shen IEEE Transactions on Medical Imaging, 2019 paper / code
	High-Resolution Image Reconstruction for Portable Ultrasound Imaging Devices R. Wang, Zhenghan Fang*, J. Gu, Y. Guo, S. Zhou, Y. Wang, C. Chang, J. Yu EURASIP Journal on Advances in Signal Processing, 2019 (co-first authors) paper
	Deep Learning for Fast and Spatially-Constrained Tissue Quantification from Highly-Undersampled Data in Magnetic Resonance Fingerprinting (MRF) Zhenghan Fang, Y. Chen, M. Liu, Y. Zhan, W. Lin, D. Shen Machine Learning in Medical Imaging (MLMI), MICCAI Workshop, 2018 paper / code
	Super-Resolution Reconstruction of Plane-Wave Ultrasound Imaging Based on the Improved CNN Method Z. Zhou, Y. Wang, J. Yu, W. Guo, Zhenghan Fang European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS), 2017 paper