Education

• University of Illinois at Urbana-Champaign, Urbana, IL, USA, 2022
  Ph.D. Candidate in Electrical and Computer Engineering, GPA: 4.0/4.0

• University of Illinois at Urbana-Champaign, Urbana, IL, USA, 2017
  M.S. in Electrical and Computer Engineering, GPA: 4.0/4.0

• Tsinghua University, Beijing, China, 2015
  B.S. in Electronic Engineering, GPA: 91/100

Awards

• Distinguished Reviewer, IEEE Transactions on Medical Imaging 2023
• Summa Cum Laude Paper Award, ISMRM 2023
• Distinguished Reviewer, Magnetic Resonance in Medicine 2023
• W.S. Moore Award (co-author), ISMRM 2023
• Young Investigator Award (1st Place), OCSMRM, 2022
• Thomas and Margaret Huang Award for Graduate Research, UIUC, 2022
• Magna Cum Laude Paper Award, ISMRM, 2022
• Best Student Paper Award (co-author), EMBC, 2021
• Summa Cum Laude Paper Award, ISMRM, 2021
• Yunni & Maxine Pao Memorial Fellowship, UIUC, 2021
• Summa Cum Laude Paper Award, ISMRM, 2020
• Mavis Future Faculty Fellowship, UIUC, 2020
• Rambus Computer Engineering Fellowship, UIUC, 2020
• Student Researcher Spotlight at Beckman Institute, UIUC, 2020
• Shun Lien Chuang Memorial Award for Excellence in Graduate Education, UIUC, 2019
• Yee Fellowship Award, UIUC, 2019
• Summa Cum Laude Paper Award, ISMRM, 2019
• Magna Cum Laude Paper Award, ISMRM, 2019
• Magna Cum Laude Paper Award, ISMRM, 2018
• Travel Stipend Award, ISMRM, 2018
• Conference Travel Award, UIUC, 2018
• Summa Cum Laude Paper Award, ISMRM, 2017
• Magna Cum Laude Paper Award, ISMRM, 2017
• Travel Stipend Award, ISMRM, 2017
• Outstanding Graduate Award, Tsinghua University, 2015
• Academic Excellence Scholarship, Tsinghua University, 2014
• Academic Excellence Scholarship, Tsinghua University, 2012

Publications

Journal Publications

• Y. Guan, Y. Li, R. Liu, Z. Meng, Y. Li, L. Ying, Y. Du, and Z.-P. Liang, “Subspace model-assisted deep learning for improved image reconstruction,” IEEE Trans. Med. Imaging, 2023, In Press. (co-first authors)
• Y. Li, Y. Zhao, R. Guo, T. Wang, Y. Zhang, M. Chrostek, W.C. Low, X.-H. Zhu, Z.-P. Liang, and W. Chen, “Machine learning-enabled high-resolution deuterium MR spectroscopic imaging for dynamic metabolic imaging of brain cancer,” IEEE Trans. Med. Imaging, vol. 40, pp. 3879-3890, 2021. (YIA Award of OCSMRM)
• Y. Li, J. Xiong, R. Guo, Y. Zhao, Y. Li, and Z.-P. Liang, “Improved estimation of myelin water fractions with learned parameter distributions,” Magn. Reson. Med., vol. 86, pp. 2795-2809, 2021.
• Y. Chen, Y. Li, and Z. Xu, “Improved low-rank filtering of MR spectroscopic imaging data with pre-learnt subspace and spatial constraints.” IEEE Trans. Biomed. Eng. vol. 67, pp. 2381-2388, 2019. (Corresponding Author)
• Y. Li, F. Lam, B. Clifford, and Z.-P. Liang, “A subspace approach to spectral quantification for MR spectroscopic imaging,” IEEE Trans. Biomed. Eng., vol. 64, pp. 2486-2489, 2017. (Highlighted Article)
• R. Jin, Y. Li, R. K. Shosted, F. Xing, I. Gilbert, J. Perry, J. Woo, Z.-P. Liang, B. P. Sutton, “Optimization of three-dimensional dynamic speech MRI: Poisson-disc under sampling and locally higher-rank reconstruction through partial separability model with regional optimized temporal basis”, Magn. Reson. Med., vol. 91, pp. 61-74, 2024.
• T. Zhang, Y. Zhao, W. Jin, Y. Li, R. Guo, Z. Ke, J. Luo, Y. Li, Z.-P. Liang, “B1 mapping using pre-learned subspaces for quantitative brain imaging”, Magn. Reson. Med., vol. 90, pp. 2089-2101, 2023.
• Z. Meng, R. Guo, T. Wang, B. Bo, Z. Lin, Y. Li, Y. Zhao, X. Yu, D. J. Lin, P. Nachev, Z.-P. Liang, and Y. Li, “Prediction of stroke onset time with joint fast high-resolution magnetic resonance spectroscopic and quantitative T2 mapping”, IEEE Trans. Biomed. Eng., vol. 70, pp. 3147-3155, 2023.
• L. Tang, Y. Zhao, Y. Li, R. Guo, B. Cai, J. Wang, Y. Li, Z.-P. Liang, X. Peng, and J. Luo, “JSENSE-Pro: Joint sensitivity estimation and image reconstruction in parallel imaging using pre-learned subspaces of coil sensitivity functions”, Magn. Reson. Med., vol. 89, pp. 1531-1442, 2023.
• Z. Lin, Z. Meng, T. Wang, R. Guo, Y. Zhao, Y. Li, B. Bo, Y. Guan, J. Liu, H. Zhou, X. Yu, D.J. Lin, Z.-P. Liang, P. Nachev, and Y. Li, “Predicting the onset of ischemic stroke with fast high-resolution 3D MR spectroscopic imaging,” J. Magn. Reson. Imaging, vol. 58, pp. 838-847, 2023. (W.S. Moore Award of ISMRM 2023)
• R. Guo, Y. Li, Y. Zhao, T. Wang, Y. Li, B. Sutton, and Z.-P. Liang, and W. Chen, “Simultaneous mapping of water diffusion coefficients and metabolite distributions of the brain using MR spectroscopic imaging without water suppression,” IEEE Trans. Biomed. Eng., vol. 70, pp. 962-969, 2022.
• T. Zhang, R. Guo, Y. Li, Y. Zhao, Y. Li, and Z.-P. Liang, “T_2^’ mapping of the brain from water-unsuppressed 1H-MRSI and TSE data,” Magn. Reason. Med., vol. 88, pp. 2198-2207, 2022.
• Y. Zhao, R. Guo, Y. Li, K.R. Thulborn, and Z.-P. Liang, “High‐resolution sodium imaging using anatomical and sparsity constraints for denoising and recovery of novel features”, Magn. Reson. Med., vol. 86, pp. 625-636, 2021. (YIA Award of OCSMRM)
• Z. Meng, R. Guo, Y. Li, Y. Guan, Y. Wang, Y. Zhao, B. Sutton, Y. Li, and Z.-P. Liang, “Accelerating T2 mapping of the brain by integrating deep learning priors with low‐rank and sparse modeling”, Magn. Reson. Med., 85.3 (2021): 1455-1467.
• R. Guo, Y. Zhao, Y. Li, T. Wang, Y. Li, B. Sutton, and Z.-P. Liang, “Simultaneous QSM and metabolic imaging of the brain using SPICE: further improvements in data acquisition and processing”, Magn. Reson. Med., 85.2 (2021): 970-977.
• L. Tang, Y. Zhao, Y. Li, R. Guo, B. Clifford, G. E. Fakhri, C. Ma, Z.-P. Liang, and J. Luo, “Accelerated J-resolved 1H-MRSI with limited and sparse sampling of (k,t_1)-space”, Magn. Reson. Med., vol. 85, pp. 30-41, 2021.
• Y. Li, T. Wang, T. Zhang, Y. Li, R. Guo, Y. Zhao, Z. Meng, Z. Lin, J. Liu, X. Yu, Z.-P. Liang, P. Nachev, “Fast high-resolution metabolic imaging of acute stroke with 3D magnetic resonance spectroscopy”, Brain, 143(11), 3225-3233.
• F. Lam, Y. Li, R. Guo, B. Clifford, and Z.-P. Liang, “Ultrafast magnetic resonance spectroscopic imaging using SPICE with learned subspaces”, Magn. Reson. Med., vol. 83, pp. 377-390, 2020. (Editor’s Picks)
• B. Clifford, Y. Gu, Y. Liu, K. Kim, S. Huang, Y. Li, F. Lam, Z.-P. Liang, X. Yu, “High-resolution dynamic 31P-MR spectroscopic imaging for mapping mitochondrial function.” IEEE Trans. Biomed. Eng. (Highlighted Article).
• R. Guo, Y. Zhao, Y. Li, Y. Li, and Z.‐P. Liang, “Simultaneous metabolic and functional imaging of the brain using SPICE.” Magn. Reson. Med., vol. 82, pp. 1993-2002, 2019.
• F. Lam, Y. Li, B. Clifford, and Z.-P. Liang, “Macromolecule mapping of the brain using ultrashort-TE acquisition and reference-based metabolite removal,” Magn. Reson. Med., vol. 79, pp. 2460-2469, 2017.
• X. Peng, F. Lam, Y. Li, B. Clifford, and Z.-P. Liang, “Simultaneous QSM and metabolic imaging of the brain using SPICE,” Magn. Reson. Med., vol. 79, pp. 13-21, 2017.

Conference Publications

• Y. Li, Y. Guan, Y. Zhao, R. Guo, Y. Li, and Z.-P. Liang, “Integrating subspace learning, manifold learning, and sparsity learning to reconstruct image sequences”, Proc. Intl. Soc. Magn. Reson. Med., 2022, p. 4591. (Magna Cum Laude Paper Award)
• Y. Li, R. Guo, Y. Zhao, Y. Li, and Z.-P. Liang, “Improved myelin water fraction estimation integrating learned probabilistic subspaces and low-dimensional manifolds”, Proc. Intl. Soc. Magn. Reson. Med., 2022, p. 5348
• R. Guo, Y. Li, Y. Zhao, T. Wang, Y. Li, B. Sutton, and Z.-P. Liang, “Simultaneous mapping of water diffusion coefficients and metabolite distributions of the brain using MRSI without water suppression”, Proc. Intl. Soc. Magn. Reson. Med., 2022, p. 2691. (Magna Cum Laude Paper Award)
• R. Guo, Y. Li, Y. Zhao, S. Tafti, A. Anderson, B. Sutton, and Z.-P. Liang, “Rapid whole brain high-resolution MR spectroscopic imaging at 7T”, Proc. Intl. Soc. Magn. Reson. Med., 2022, p. 2697
• Y. Zhao, Y. Li, R. Guo, K. R. Thulborn, and Z.-P. Liang, “Reconstructing high-quality sodium MR images from limited noisy k-space data with model-assisted deep learning”, Proc. Intl. Soc. Magn. Reson. Med., 2022, p. 1872
• Y. Guan, Y. Li, R. Liu, Z. Meng,Y. Li, L. Ying, Y. P. Du, and Z.-P. Liang, “Image reconstruction with subspace-assisted deep learning,” Proc. Intl. Soc. Magn. Reson. Med., 2022, p. 2433.
• R. Liu, Y. Li, Z. Meng, Y. Guan, T. Wang, T. Li, Y. P. Du, and Z.-P. Liang, “Reconstructing T2 maps of the brain from highly sparse k-space data with generalized series-assisted deep learning”, Proc. Intl. Soc. Magn. Reson. Med., 2022, p. 6187
• Y. Zhang, J. Hu, M. Zhang, R. Guo, Y. Li, Y. Zhao, Z. Meng, D. Wang, W. Li, B. Li, J. Liu, B. Li, Z.-P. Liang, and Y. Li, “Study of neurometabolic signature in Alzheimer’s disease using high-resolution 3D 1H-MRSI”, Proc. Intl. Soc. Magn. Reson. Med., 2022, p. 6842
• R. Guo, C. Ma, Y. Li, Y. Zhao, Y. Li, T. Wang, Y. Li, G.E. Fakhri, and Z.-P. Liang, “High-Resolution Label-Free Molecular Imaging of Brain Tumor,” in Conf. Proc. IEEE Eng. Med. Biol. Soc., 2021. (Best Student Paper Award)
• Y. Li, Y. Zhao, R. Guo, T. Wang, Y. Zhang, M. Chrostek, W.C. Low, X.-H. Zhu, W. Chen, and Z.-P. Liang, “A marriage of subspace modeling with deep learning to enable high-resolution dynamic deuterium MR spectroscopic imaging,” Proc. Intl. Soc. Magn. Reson. Med., pp. 2524, 2021.
• Y. Li, J. Xiong, R. Guo, Y. Zhao, Y. Li, and Z.-P. Liang, “Improved estimation of myelin water fractions with learned parameter distributions,” Proc. Intl. Soc. Magn. Reson. Med., pp. 2075, 2021.
• Y. Li, Y. Zhao, R. Guo, F. Yu, X.-H. Zhu, W. Chen, and Z.-P. Liang, “Rapid dynamic deuterium MR spectroscopic imaging using deep-SPICE,” Proc. Intl. Soc. Magn. Reson. Med., 2020, p. 3739.
• Y. Li, K. Kim, B. Clifford, R. Guo, Y. Gu, Z.-P. Liang, and X. Yu, “High-resolution dynamic 31P-MRSI of ischemia-reperfusion in rat using low-rank tensor model with deep learning priors,” Proc. Intl. Soc. Magn. Reson. Med., 2020, p. 4538.
• Y. Li, Y. Guan, Z. Meng, F. Yu, R. Guo, Y. Zhao, T. Wang, Y. Li, and Z.-P. Liang, “An information theoretical framework for machine learning based MR image reconstruction,” Proc. Intl. Soc. Magn. Reson. Med., 2020, p. 3858.
• Y. Li, R. Guo, Y. Zhao, T. Wang, Z. Meng, Y. Li and Z.-P. Liang, “Rapid high-resolution simultaneous acquisition of metabolites, myelin water fractions, and tissue susceptibility of the whole brain using ‘SPICY’ 1H-MRSI,” Proc. Intl. Soc. Magn. Reson. Med., pp. 754, 2019.
• Y. Li, R. Guo, Y. Zhao, Y. Chen, B. Clifford, T. Wang, C. Wang, Y. Du, and Z.-P. Liang, “A model-based method for estimation of myelin water fractions,” Proc. Intl. Soc. Magn. Reson. Med., pp. 511, 2019.
• J. Liu, Y. Li, T. Wang, Z. Meng, K. Xue, R. Guo, Y. Zhao, Y. Du, Q. Chen, Z.-P. Liang, and Y. Li, “Multimodal imaging of brain tumors using high-resolution 1H-MRSI without water suppression,” Proc. Intl. Soc. Magn. Reson. Med., pp. 7739, 2019.
• Y. Zhao, Y. Li, R. Guo, B. Clifford, X. Yu, and Z.-P. Liang, “Accelerating high-resolution semi-LASER 1H-MRSI using SPICE,” Proc. Intl. Soc. Magn. Reson. Med., pp. 683, 2019.
• L., Tang, Y. Zhao, Y. Li, R. Guo, B. Clifford, C. Ma, Z.-P. Liang, and J. Luo, “Accelerated J-resolved 1H-MRSI with limited and sparse sampling of (k, tJ)-Space,” Proc. Intl. Soc. Magn. Reson. Med., pp. 756, 2019.
• Y. Li, and Z.-P. Liang, “Constrained image reconstruction using a kernel+sparse model,” Proc. Intl. Soc. Magn. Reson. Med., pp. 657, 2018.
• Y. Li, F. Lam, B. Clifford, R. Guo, X. Peng, and Z.-P. Liang, “Constrained MRSI reconstruction using water side information with a kernel-based method,” Proc. Intl. Soc. Magn. Reson. Med., pp. 540, 2018.
• Y. Li, F. Lam, R. Guo, B. Clifford, X. Peng, and Z.-P. Liang, “Removal of water sidebands from 1H-MRSI data acquired without water suppression,” Proc. Intl. Soc. Magn. Reson. Med., pp. 288, 2018.
• F. Lam, Y. Li, R. Guo, B. Clifford, X. Peng, and Z.-P. Liang, “Further accelerating SPICE for ultrafast MRSI using learned spectral features,” Proc. Intl. Soc. Magn. Reson. Med., pp. 623, 2018.
• F. Lam, Y. Li, and Z.-P. Liang, “Spectral quantification for multiple-TE spectroscopy using spectral priors and measured lineshape distortion function,” Proc. Intl. Soc. Magn. Reson. Med., pp. 657, 2018.
• X. Peng, Y. Li, F. Lam, R. Guo, B. Clifford, and Z.-P. Liang, “Constrained dipole inversion for quantitative susceptibility mapping using a “kernel+sparse” model,” Proc. Intl. Soc. Magn. Reson. Med., pp. 3406, 2018.
• Y. Li, F. Lam, B. Clifford, and Z.-P. Liang, “A subspace approach to spectral quantification,” Proc. Intl. Soc. Magn. Reson. Med., pp. 5467, 2017.
• F. Lam, Y. Li, B. Clifford, X. Peng, and Z.-P. Liang, “Simultaneous mapping of brain metabolites, macromolecules and tissue susceptibility using SPICE,” Proc. Intl. Soc. Magn. Reson. Med., pp. 1249, 2017.
• F. Lam, Y. Li, B. Clifford, and Z.-P. Liang, “Macromolecule mapping with ultrashort-TE acquisition and metabolite spectral prior,” Proc. Intl. Soc. Magn. Reson. Med., pp. 5518, 2017.
• Y. Li, S. Ma, Z. Hu, J. Chen, G. Su, and W. Dou, “Single trial EEG classification applied to a face recognition experiment using different feature extraction methods,” in Conf. Proc. IEEE Eng. Med. Biol. Soc., pp. 7246-7249, 2015.
• Y. Li, Y. Sun, F. Taya, H. Yu, N. Thakor, and A. Bezerianos, “Single trial EEG classification of lower-limb movements using improved regularized common spatial pattern,” in Proc. 7th Int. IEEE/EMBS Conf. Neural Eng., pp. 1056-1059, 2015.

Teaching

• Teaching Assistant - ECE 513: Vector Space Signal Processing (Spring 2021)

Professional Activities

Journal Reviewers

• Magnetic Resonance in Medicine (review more than 51 journal papers)
• IEEE Transactions on Medical Imaging
• IEEE Transactions on Biomedical Imaging
• IEEE Transactions on Computational Imaging

Conference Reviewers

• Annual Meeting of the International Society for Magnetic Resonance in Medicine
• Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Graduate Coursework

• Control System Theory & Design (A+)
• Introduction to Optimization (A+)
• Random Process (A+)
• Vector Space Signal Processing (A+)
• Detection and Estimation (A+)
• Digital Signal Processing (A)
• Pattern Recognition (A)
• Magnetic Resonance Imaging (A)
• Computational Inference (A)
• Statistical Learning Theory (A)
• Machine Learning Theory (A)
• Information Theory (A)