Tao Hong (洪涛)
I am currently a Postdoctoral Fellow in the Department of Radiology at Boston Children’s Hospital, affiliated with Harvard Medical School, working with Prof. Xiaoqing Wang. My research focuses on developing computational methods for large-scale inverse problems and computational imaging, with an emphasis on model-based and learning-based MRI reconstruction.
Previously, I was a postdoctoral fellow at the Oden Institute, The University of Texas at Austin, with Prof. Umberto Villa, and at the University of Michigan, Ann Arbor, with Prof. Jeffrey A. Fessler and Prof. Luis Hernandez-Garcia. I received my Ph.D. in Computer Science from the Technion – Israel Institute of Technology, advised by Prof. Irad Yavneh and Prof. Michael Zibulevsky, where my research focused on numerical optimization and multigrid methods. Ph.D. Thesis.
Research interests
My research lies at the intersection of applied mathematics and computational imaging. I develop computational methods that integrate mathematical structure, imaging physics, and data-driven learning to solve large-scale inverse problems.
Exploiting mathematical structure for efficient and reliable large-scale computation.
Combining physical models with data-driven learning for interpretable reconstruction.
Extracting meaningful information from complex measurements.
News
| Feb 19, 2026 | Our work A Mini-Batch Quasi-Newton Proximal Method for Constrained Total-Variation Nonlinear Image Reconstruction was accepted to SIAM Journal on Imaging Sciences! Find our implementation at Code. |
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| Jan 09, 2026 | Our work A Convergent Generalized Krylov Subspace Method for Compressed Sensing MRI Reconstruction with Gradient-Driven Denoisers was accepted to IEEE Transactions on Computational Imaging! The proposed method reconstructs high-quality images within seconds and is supported by a rigorous convergence analysis. |
| Oct 16, 2025 | Our work Convergent Complex Quasi-Newton Proximal Methods for Gradient-Driven Denoisers in Compressed Sensing MRI Reconstruction was accepted to IEEE Transactions on Computational Imaging! Find our implementation and project website at Code and Project Website. |
| Oct 08, 2025 | Our work Using Randomized Nyström Preconditioners to Accelerate Variational Image Reconstruction was accepted to IEEE Transactions on Computational Imaging! Find our implementation at Code. |
