Jeffrey A. Fessler
William L. Root Distinguished University Professor of Electrical Engineering and Computer Science; Professor, EECS – Electrical and Computer Engineering
Studies statistical signal and image processing for tomographic imaging, including machine-learning methods for inverse problems in medical image reconstruction.
Jeffrey A. Fessler's lab at Michigan works on medical imaging & healthcare AI, learning theory & optimization and signal processing & information theory. 41 papers since 2023, 2 at top venues, mostly NeurIPS. 7 PhD students and 1 master's student. 74 alumni, most often to Apple and Google. Applications close .
At a glance
- papers, 2023–2026
- 11/ year
- at T1 venues
- 5%
- PhD students
- 7
- MS students
- 1
- alumni listed
- 74
Papers by yearfrom the lab homepage, 2023–2026
- 6 papers2026 · T1 0 · collecting
- 12 papers2025 · T1 0
- 10 papers2024 · T1 2
- 13 papers2023 · T1 0
T1 venuesOtherVenues: NeurIPS
Research focuspaper titles that use each topic's words
- Learning theory & optimization9 papers
- image9 papers
- Medical imaging & healthcare AI5 papers
- Signal processing & information theory5 papers
- statistical signal0 papers
Paper list
From the lab homepage.
20266 papers
- IEEE Trans. Computational ImagingA convergent generalized Krylov subspace method for compressed sensing MRI reconstruction with gradient-driven denoisers (opens in a new tab)
- IEEE Trans. Med. ImagBilevel optimized implicit neural representation for scan-specific accelerated MRI reconstruction (opens in a new tab)
- Trans. Mach. Learning ResDynamic subspace estimation from undersampled data using Grassmannian geodesics (opens in a new tab)
- IEEE Trans. Computational ImagingScan-adaptive MRI undersampling using neighbor-based optimization (SUNO) (opens in a new tab)
- —SIAM J. Imaging Sci., 19(2):839-78, 2026
- IEEE Trans. Computational ImagingSpatiotemporal maps for dynamic MRI reconstruction (opens in a new tab)
202512 papers
- IEEE Trans. Sig. ProcALPCAH: Subspace learning for sample-wise heteroscedastic data (opens in a new tab)
- IEEE Trans. Computational ImagingConvergent complex quasi-Newton proximal method for gradient-driven denoisers in compressed sensing MRI reconstruction (opens in a new tab)
- Proc. Conf. on Parsimony and LearningFlowDAS: A flow-based framework for data assimilation (opens in a new tab)
- Allerton Conf. on Comm., Control, and ComputingFlowDAS: A stochastic interpolant-based framework for data assimilation (opens in a new tab)
- IEEE Trans. Microwave Theory \& TechFMCW inverse circular synthetic aperture radar using a fast time-domain reconstruction (opens in a new tab)
- Mag. Res. MedImpact of tissue sample preparation methods on myelin-sensitive quantitative MR imaging (opens in a new tab)
- Mag. Res. MedMultidimensional RF pulse design using auto-differentiable spin-domain optimization and its application to reduced field-of-view imaging (opens in a new tab)
- Eur. J. Nuc. MedShorter SPECT scans using self-supervised coordinate learning to synthesize skipped projection views (opens in a new tab)
- Trans. Mach. Learning ResStreaming heteroscedastic probabilistic PCA with missing data (opens in a new tab)
- IEEE Trans. Computational ImagingSwap-Net: A memory-efficient 2.5D network for sparse-view 3D cone beam CT reconstruction to ICF applications (opens in a new tab)
- IEEE Trans. Computational ImagingTest-time adaptation improves inverse problem solving with patch-based diffusion models (opens in a new tab)
- IEEE Trans. Computational ImagingUsing randomized \Nystrom preconditioners to accelerate variational image reconstruction (opens in a new tab)
202410 papers
- IEEE Trans. Computational ImagingA complex quasi-Newton proximal method for image reconstruction in compressed sensing MRI (opens in a new tab)
- IEEE Trans. Computational ImagingAccelerated Wirtinger flow with score-based image priors for holographic phase retrieval in Poisson-Gaussian noise conditions (opens in a new tab)
- NeurIPS (T1)DiffusionBlend: learning 3D image prior through position-aware diffusion score blending for 3D computed tomography reconstruction (opens in a new tab)
- Nature CommImaging 3D chemistry at 1 nm resolution with fused multi-modal electron tomography (opens in a new tab)
- NeurIPS (T1)Learning image priors through patch-based diffusion models for solving inverse problems (opens in a new tab)
- IEEE Trans. Med. ImagManifold regularizer for high-resolution fMRI joint reconstruction and dynamic quantification (opens in a new tab)
- Mag. Res. MedModel-based reconstruction for looping-star MRI (opens in a new tab)
- IEEE Trans. Computational ImagingProvable preconditioned plug-and-play approach for compressed sensing MRI reconstruction (opens in a new tab)
- Med. PhysStatistical image reconstruction with beam hardening compensation for X-ray CT by a calibration step (2DIterBH) (opens in a new tab)
- Phys. Med. BiolX-ray source motion blur modeling and deblurring with generative diffusion for digital breast tomosynthesis (opens in a new tab)
202313 papers
- EJNMMI Phys90Y SPECT scatter estimation and voxel dosimetry in radioembolization using a unified deep learning framework (opens in a new tab)
- SampTAALPCAH: Sample-wise heteroscedastic PCA with tail singular value regularization (opens in a new tab)
- Proc. SPIE 12463 Medical Imaging: Phys. Med. ImDeep CNN task-based image quality assessment: application to digital breast tomosynthesis reconstruction and denoising (opens in a new tab)
- IEEE Trans. Computational ImagingEfficient approximation of Jacobian matrices involving a non-uniform fast Fourier transform (NUFFT) (opens in a new tab)
- Proc. IEEE Conf. Acoust. Speech Sig. ProcHeMPPCAT: mixtures of probabilistic principal component analysers for data with heteroscedastic noise (opens in a new tab)
- Phys. Med. BiolModel-based deep CNN-regularized reconstruction for digital breast tomosynthesis with a task-based CNN image assessment approach (opens in a new tab)
- IEEE Trans. Patt. Anal. Mach. IntMomentum-Net: Fast and convergent iterative neural network for inverse problems (opens in a new tab)
- SIAM J. Math. of Data SciOptimally weighted PCA for high-dimensional heteroscedastic data (opens in a new tab)
- IEEE Trans. Med. ImagPhysics-guided deep scatter estimation by weak supervision for quantitative SPECT (opens in a new tab)
- IEEE Trans. Computational ImagingSparse-view cone beam CT reconstruction using data-consistent supervised and adversarial learning from scarce training data (opens in a new tab)
- IEEE Trans. Computational ImagingSPECT reconstruction with a trained regularizer using CT-side information: Application to 177Lu SPECT imaging (opens in a new tab)
- Mag. Res. MedStochastic optimization of 3D non-Cartesian sampling trajectory (SNOPY) (opens in a new tab)
Where alumni went74 listed, 59% to industry
Industry 19Academia 13
Most often to
- Apple
- McKinsey & Co
Also to
- Harvard University
- Johns Hopkins University
- KAIST
Apply to MichiganFall 2027
Michigan CSE Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
Similar labsnearest by research summary
- USUSC
Paul Bogdan
- Computational neuroscience & cognition
- USStanford
Emily Fox
- Probabilistic & causal inference
- GBImperial
Dan Goodman
- Computational neuroscience & cognition
- USMIT
Michael Carbin
- Probabilistic & causal inference
- CAToronto
Rahul G. Krishnan
- Probabilistic & causal inference
- USUIUC
Rainer Engelken
- Learning theory & optimization
Your notes
Save this lab to keep notes.
Data
Checked against the lab homepage.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Lab homepage
Tiers are venue classifications, not lab rankings.
Michigancloses