The Grainger College of Engineering
Electrical & Computer Engineering

Fan Lam

Fan Lam
Fan Lam
Associate Professor, Bioengineering
(217) 300-3713
4061 Beckman Institute

For More Information

Education

  • Ph. D. Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, 2015
  • M.S., Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, 2011
  • B.S., Biomedical Engineering, Tsinghua University, 2008

Academic Positions

  • Affiliated faculty of Carle-Illinois College of Medicine, 03/2020 - Present, 0%
  • Affiliated faculty of the Carl R. Woese Institute for Genomic Biology, 10/2020 - Present, 0%
  • Affiliated faculty of Beckman Institute for Advanced Science and Technology, 08/2018 - Present, 0%
  • Affiliated faculty of Electrical and Computer Engineering, 10/2018 - Present, 0%
  • Affiliated faculty of Physics, 2025 - Present, 0%
  • Assistant Professor of Bioengineering, 08/2018 - 08/2024, 100%
  • Associate Professor of Bioengineering, 08/2024 - Present, 100%

Other Professional Employment

  • Visiting Professor, Department of Electrical Engineering and Computer Science, University of California Berkeley, 08/2025 - 07/2026

Professional Highlights

  • Scialog Fellow in Advancing Bioimaging
  • List of Teachers Ranked as Excellence, Fall 2019, Fall 2021, Fall 2022
  • IEEE Senior Member
  • NIH NIGMS MIRA Award
  • NIH NIBIB Trailblazer Award
  • NSF CAREER Award

Research Interests

  • Neuroimaging technologies and applications
  • Computational imaging and machine learning
  • Mass spectrometry imaging
  • Magnetic resonance imaging
  • Biomedical imaging

Research Areas

Selected Articles in Journals

  • T. J. Trinklein, S. Rubakhin, S. Okyem, S. W. Croslow, M. Asadian, K. R. Sabitha, O. Lazarov, F. Lam+, J. V. Sweedler+. Multimodal mass spectrometry imaging for plaque- and region-specific neurolipidomics in an Alzheimer’s disease mouse model. ChemRxiv. 2025; doi:10.26434/chemrxiv-2024-2hbb7-v2. Accepted to Nature Communications.
  • M. Anandakumar, T. J. Trinklein, S. Rubakhin, J. V. Sweedler, F. Lam. Integrating model-based reconstruction and deep learning for accelerating mass spectrometry imaging. Analytical Chemistry, 2025; DOI: 10.1021/acs.analchem.5c04075.
  • M. Asadian, S. Croslow, T. Trinklein, S. Rubakhin, F. Lam, J. Sweedler, High-throughput fluorescence-guided sequential single-cell MALDI-ICC mass spectrometry. Analytical Chemistry, 97:15864-15872, 2025.
  • Z. Li, L. T. Sutkus, J. E. Fil, P. Senthil, F. Lam, B. P. Sutton, R. N. Dilger, PigBET: A 2.5D deep learning segmentation framework for multimodal and longitudinal domestic pig MRI utilizing ImageNet pre-trained encoders. Brain Organoid and Systems Neuroscience Journal, 3:180-194, 2025.
  • Z. Wang, B. P. Sutton, F. Lam, High-resolution, volumetric diffusion-weighted MR spectroscopic imaging of the brain, Magn Reson Med, 94:450-469, 2025. (ISMRM Young Investigator Award Finalist)
  • R. Zhao, Z. Wang, A. Anderson, G. Huesmann, F. Lam, Spatiospectral reconstruction from noisy data integrating subspace modeling and self-supervised spatiotemporal denoising. IEEE Trans Med Imaging, 44:3002-3011, 2025.
  • R. A. Khan, Z. Wang, R. Zhao, G. Kim, F. Lam, F. T. Husain, GABA and glutamate/glutamine associations with tinnitus and hearing loss, NeuroReport, 2025. doi: 10.1097/WNR.0000000000002137
  • Y. R. Xie, D. C. Castro, S. S. Rubakhin, T. J. Trinklein, J. V. Sweedler, F. Lam, Multiscale biochemical mapping of the brain via deep-learning-enhanced high-throughput mass spectrometry, Nature Methods, 21:521-530, 2024.
  • Z. Wang, Y. Li, C. Cao, A. Anderson, G. Huesmann, F. Lam, Multi-parametric molecular imaging of the brain using optimized J-resolved subspace MRSI. IEEE Trans. Biomed. Eng., 71:1732-1744, 2024. (Journal Highlight Article)
  • F. Lam, X. Peng, Z.-P. Liang, High-dimensional MR spatiospectral imaging by integrating physics-based modeling and data-driven machine learning, IEEE Sig. Proc. Mag., 40:101-115, 2023.
  • A. Shaffer, S.S. Kwok, A. Naik, A. T. Anderson, F. Lam, T. Wszalek, P. M. Arnold, W. Hassaneen. Ultra-high-field MRI in the diagnosis and management of gliomas: A systematic review. Front. Neurol. 13, 2022.
  • Y. Li, Z. Wang, F. Lam, SNR enhancement for multi-TE MRSI using joint low-dimensional model and spatial constraints, IEEE Trans. Biomed. Eng., 69:3087 - 3097, 2022.
  • Y. Xie, D. C. Castro, S. S. Rubakhin, J. V. Sweedler, F. Lam, Enhancing the throughput of FT mass spectrometry imaging using compressed sensing and subspace modeling. Anal. Chem., 94:5335 – 5343, 2022.
  • F. Lam, J. Chu, J. S. Choi, C. Cao, T. K. Hitchens, S. K. Silverman, Z.-P. Liang, R. N. Dilger, G. E. Robinson, K. C. Li, Epigenetic MRI: Noninvasive imaging of DNA methylation in the brain. PNAS, 119:e2119891119, 2022.
  • X. Peng, B. Sutton, F. Lam, Z.-P. Liang, DeepSENSE: Learning coil sensitivity functions for SENSE reconstruction using deep learning. Magn. Reson. Med., 87:1894 - 1902, 2022.
  • Z. Wang, Y. Li, F. Lam, High-resolution, 3D multi-TE 1H-MRSI using fast spatiospectral encoding and subspace imaging, Magn. Reson. Med., 87:1103 - 1118, 2022.

Articles in Conference Proceedings

  • M. Anandakumar, T. Trinklein, J. V. Sweedler, F. Lam. Integrating model-based reconstruction and deep learning for accelerating mass spectrometry imaging (Oral Presentation, <5% of total abstracts). Proc. of 73rd ASMS Conference on Mass Spectrometry and Allied Topics. Baltimore, Maryland; 2025, ID: 322537.
  • J. Tu, Y. Shi, F. Lam. Score-based self-supervised MRI denoising. The Thirteenth International Conference on Learning Representations (ICLR), 2025. https://openreview.net/forum?id=uNd289HjLi.
  • Y. Wang, U. Saha, E. Roy, A. Smith, F. Lam. Cell-resolved MR spectroscopic signatures for cancer cells mapping: Dual-state subspaces and GFP-labeled glioma mouse validation. Proc. of ISMRM, 2025, p. 0606. (Conference highlighted talk)
  • C. Cao, F. Lam+, D. Llano+, R. Dilger, S. K. Silverman, Z. P. Liang, K. C. Li, G. E. Robinson+. Global DNA methylation as an early biomarker for Alzheimer’s disease. In: Society of Neuroscience. 2024. (+: Co-correspondance)
  • R. Zhao, Z. Wang, F. Lam. Learning disentangled representation for multidimensional MR image reconstruction. 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2024, pp. 1-4. doi: 10.1109/EMBC53108.2024.10782954.
  • Z. Wang, R. Zhao, X. Li, A. Anderson, G. Husemann, F. Lam, Mapping metabolite and neurotransmitter alterations in mesial temporal lobe epilepsy using optimized multi-TE MRSI: A preliminary study. Proc. of ISMRM, 2024, p. 3232. 
  • Z. Wang, B. P. Sutton, F. Lam, Robust volumetric diffusion-weighted MRSI via time-resolved phase reconstruction and correction, Proc. of ISMRM, 2024, p. 0253.
  • X. Peng, F. Lam, A Generative Subspace Model for High-dimensional MR Imaging, In Proc. of ISMRM, 2023, p.0860.
  • Z. Wang, Y. Li, and F. Lam, Whole-Brain Multi-Parametric Molecular Imaging Using Accelerated J-Resolved Subspace 1H-MRSI, In Proc. of ISMRM, 2023, p.3682.
  • R. Zhao, Y. Li, Z. Wang, A. Anderson, P. Arnold, G. Huesmann and F. Lam, MR Spatiospectral Reconstruction using Plug&Play Denoiser with Self-Supervised Training, Proc. of ISMRM, 2023, p.0955.
  • R. Zhao, X. Peng and F. Lam, Integrating Adaptive Generative Network and Subspace Models for Accelerated MR Parameter Mapping, Proc. of ISMRM, 2023, p.1628.
  • Y. Wang, S. S. Rubakhin and F. Lam, High-Resolution 1H-MRSI of the Brain at 9.4T Integrating Relaxation Enhancement and Subspace Imaging, Proc. of ISMRM, 2023, p.3691.
  • Y. Li, Z. Wang, A. Anderson, R. Zhao, P. Arnold, G. Huesmann, and F. Lam, Fast MRSI Reconstruction Combining Linear and Nonlinear Manifold Models, Proc. of ISMRM, 2023, p.0870.
  • R. Zhao, Y. Li, Z. Wang, A. Anderson, P. Arnold, G. Huesmann, F. Lam, SNR-Enhancing Spatiospectral Reconstruction using Plug&Play Denoiser from Self-Supervised Training, In Proc. of ISMRM Data Sampling and Reconstruction Workshop, 2023.
  • X. Ye, Z. Wang, F. Lam, Improved nuisance signal removal for 1H-MRSI using a low-rank plus sparse model with learned subspaces, In Proc. of Annual Meeting of ISMRM, 2022, p. 4315.
  • F. Lam, Y. Li, Y. Zhao, J. Haldar, Improving lipid suppression for 1H-MRSI using region-optimized virtual coils, In Proc. of Annual Meeting of ISMRM, 2022, p. 2621.
  • Z. Wang, Y. Li, F. Lam, High-Resolution brain metabolite T2 mapping using optimized multi-TE MRSI, In Proc. of Annual Meeting of ISMRM, 2022, p. 4998.
  • Z. Wang, F. Lam, Fast volumetric diffusion-weighted MRSI: improved acquisition and data processing, In Proc. of Annual Meeting of ISMRM, 2022, p. 3524.
  • R. Zhao, Z. Wang, F. Lam, Generative image prior constrained subspace reconstruction for high-resolution MRSI, In Proc. of Annual Meeting of ISMRM, 2022, p. 3500.
  • Y. Li, L. Ruhm, A. Henning, F. Lam, LeaRning nonlineAr representatIon and projectIon for faSt constrained MRSI rEconstruction (RAIISE), In Proc. of Annual Meeting of ISMRM, 2022, p. 4808.
  • Y. Li, Z. Wang, F. Lam, High-SNR J-Resolved MRSI by jointly learning nonlinear representation and projection, In Proc. of Annual Meeting of ISMRM, 2022, p. 2206.
  • Z. Wang, F. Lam, High resolution volumetric diffusion-weighted MRSI using a subspace approach, In Proc. of Annual Meeting of International Society for Magnetic Resonance in Medicine, 2021, p. 37.
  • F. Lam, H. Hetherington, J. Pan, Rapid MRSI of the brain on 7T using subspace-based processing, In Proc. of Annual Meeting of International Society for Magnetic Resonance in Medicine, 2021, p. 2206.
  • Z. Wang, Y. Li, F. Lam, Optimized subspace-based J-resolved MRSI for simultaneous metabolite and neurotransmitter mapping, In Proc. of Annual Meeting of International Society for Magnetic Resonance in Medicine, 2021, p. 72.
  • Y. Li, Z. Wang, F. Lam, SNR-enhancing reconstruction for multi-TE MRSI using a learned nonlinear low-dimensional model, In Proc. of Annual Meeting of International Society for Magnetic Resonance in Medicine, 2021, p. 1998.
  • Z. Wang, F. Lam, B0 inhomogeneity corrected reconstruction for low-resolution J-resolved MRSI using low-rank and spatial constraints, In Proc. of Annual Meeting of International Society for Magnetic Resonance in Medicine, 2020, p. 2911.
  • Y. Li, Z. Wang, F. Lam, Separation of metabolites and macromolecules for short-TE 1H-MRSI using learned nonlinear models, In Proc. of Annual Meeting of International Society for Magnetic Resonance in Medicine, 2020, p. 2854.
  • R.-J. Ho, F. Lam, High-resolution 3D spin-echo MRSI using interleaved water navigators, sparse sampling and subspace-based processing, In Proc. of IEEE-EMBC, 2020, pp. 1465-1468.
  • Y. Li, Z. Wang, F. Lam, Separation of metabolite and macromolecule signals for 1H-MRSI using learned nonlinear models, In Proc. of IEEE International Symposium on Biomedical Imaging (ISBI), 2020, pp. 1725-1728.
  • Y. Li, X. Peng, F. Lam, Learning nonlinear low-dimensional models for MR spectroscopic imaging using neural networks, In Proc. of ISMRM, 2019, p. 0947.

Abstracts (in print or accepted)

  • C. Cao, R. N. Dilger, S. K. Silverman, Z.-P. Liang, K. C. Li, G. E. Robinson, D. A. Llano, F. Lam. Distinct spatiotemporal dynamics of DNA methylation in familial and sporadic Alzheimer’s disease mouse models. In: Society of Neuroscience 2025
  • T. Trinklein, S. Rubakhin, M. Asadian, KR Sabitha, O Lazarov, F. Lam+, JV Sweedler+. Mapping lipids associated with Aβ plaques in a mouse model of Alzheimer’s disease via multiscale mass spectrometry imaging. In: Alzheimer’s Association International Conference. 2024.

Journal Editorships

  • Associate Editor, Medical Physics
  • Associate Editor, IEEE Transactions on Medical Imaging
  • Associate Editor, IEEE Transactions on Computational Imaging

Professional Societies

  • Organization for Human Brain Mapping (OHBM)
  • Society of Neuroscience
  • IEEE-EMBS
  • IEEE
  • International Society of Magnetic Resonance in Medicine (ISMRM)

Service on Department Committees

  • Director of the MS in Biomedical Image Computing (MS-BIC) program, Department of Bioengineering, 2022 - Present

Other Outside Service

  • Journal reviewer for Science Advances, Analytical Chemistry
  • Journal reviewer for Magnetic Resonance in Medicine, Magnetic Resonance Imaging, NMR in Biomedicine, NeuroImage, Medical Image Analysis
  • Journal reviewer for IEEE Transactions on Biomedical Engineering, IEEE Transactions on Medical Imaging, IEEE Journal of Biomedical and Health Informatics, Proceedings of the IEEE, IEEE Signal Processing Magazine, Signal Processing Letters

Teaching Honors

  • List of Teachers Ranked as Excellent by Students  (Fall 2019)
  • List of Teachers Ranked as Excellent by Students (Fall 2021)
  • List of Teachers Ranked as Excellent by Students (Fall 2022)
  • List of Teachers Ranked as Excellent by Students (Spring 2024)
  • List of Teachers Ranked as Excellent by Students (Spring 2025)

Research Honors

  • ISMRM Young Investigator Award Finalist (as PI) (2025)
  • Best Technical Development Abstract Award, ISMRM MR Spectroscopy Workshop (as PI) (2024)
  • Scialog Advancing BioImaging Fellow (2023)
  • Cozzarelli Prize Finalist, PNAS (2023)
  • ISMRM Summa Cum Laude Abstract Award (as senior author) (2021)
  • NIH-NIGMS Maximizing Investigators' Research Award (MIRA R35) (2021)
  • NIH-NIBIB Trailblazer Award (2020)
  • National Science Foundation CAREER Award (2020)
  • Junior Fellow of the International Society of Magnetic Resonance in Medicine (2017)
  • Best Student Paper Award, IEEE International Symposium on Biomedical Imaging (2015)
  • Robert T. Chien Memorial Award, ECE, University of Illinois (2015)
  • Beckman Institute Postdoctoral Fellowship (2015)

Public Service Honors

  • Distinguish Reviewer Award, Magnetic Resonance in Medicine (2022)
  • Distinguished Reviewer Award, IEEE Transactions on Medical Imaging (2020)
  • Distinguish Reviewer Awards, Magnetic Resonance in Medicine (2015-2019)

Other Honors

  • IEEE Senior Membership (2021 )

Recent Courses Taught

  • BIOE 205 - Signals & Systems in Bioengrg
  • BIOE 483 - Biomed Computed Imaging System
  • BIOE 486 - Appl Deep Learn Biomed Imaging
  • BIOE 504 - Analytical Methods in Bioeng