Publications

2026

I. Alkhouri, E. Bell, S. Liang, A. Ghosh, R. Wang, and S. Ravishankar, “Understanding Untrained Deep Models for Inverse Problems: Algorithms and Theory,” in IEEE Signal Processing Magazine Special Issue on The Mathematics of Deep Learning, vol. 43, no. 2, pp. 77-92, March 2026.
I. Alkhouri, S. Liang, R. Wang, Q. Qu, and S. Ravishankar, “Robust Physics-based Deep MRI Reconstruction Via Diffusion Purification,” in IEEE Transactions on Neural Networks and Learning Systems, vol. 37, no. 5, pp. 2347-2361, May 2026.
S. Gautam, A. Li, N. Seiberlich, J. A. Fessler, and S. Ravishankar, “Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO),” in IEEE Transactions on Computational Imaging, vol. 12, pp. 601-613, 2026.
X. Li, S. M. Kwon, S. Liang, I. Alkhouri, S. Ravishankar, and Q. Qu, “DCDP: Decoupled Data Consistency via Diffusion Purification for Solving General Inverse Problems,” accepted to International Conference on Computational Photography (ICCP), 2026.
A. Dhar, S. Gautam, and S. Ravishankar, “Scan-Adaptive Deep Learning-Based Sampling with Pre-Optimized Mask Supervision,” in International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting 2026.(arxiv)
S. Gautam, A. Li, J. A. Fessler, N. Seiberlich, and S. Ravishankar, “Learning Patient-Adaptive Undersampling Patterns for Cardiac MRI Using Nearest Neighbor Search,” in International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting 2026.

2025

S. Liang, E. Bell, Q. Qu, R. Wang, and S. Ravishankar, “Analysis of Deep Image Prior and Exploiting Self-Guidance for Image Reconstruction,” in IEEE Transactions on Computational Imaging, vol. 11, pp. 435-451, 2025.
S. Gautam, M. L. Klasky, B. T. Nadiga, T. Wilcox, G. Salazar, and S. Ravishankar, “Learning Robust Features for Scatter Removal and Reconstruction in Dynamic ICF X-Ray Tomography,” Optics Express, vol. 33, issue 12, pp. 26826-26845, 2025.
T. Wheeler, S. Ravishankar, C. Wrede, A. Andalib, A. Anthony, Y. Ayyad, B. Jain, A. Jarros, R. Mahajan, L. Schaedig, A. Adams, T. Ahn, J. Allmond, D. Bardayan, D. Bazin, K. Bosmpotinis, T. Budner, S. Carmichael, S. Cha, A. Chen, K. A. Chipps, J. Christie, I. Cox, J. Dopfer, M. Friedman, J. Garcia-Duarte, E. Good, T. J. Gray, A. Green, R. Grzywacz, K. Hahn, R. Jain, E. Jensen, T. King, S. Liddick, B. Longfellow, R. Lubna, C. Marshall, Y. Mishnayot, A. Mitchell, F. Montes, T. H. Ogunbeku, J. Owens-Fryar, S. Pain, J. Pereira, E. Pollacco, A. Rogers, Z. Serikow, K. Setoodehnia, L. Sun, J. Surbrook, A. Tsantiri, L. E. Weghorn, “Object Detection with Deep Learning for Rare Event Search in the GADGET II TPC,” in Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 1080, pp. 170659, 2025.
S. Liang, J. Jia, V. H. M. Nguyen, I. Alkhouri, S. Liu, and S. Ravishankar, “Robust MRI Reconstruction by Smoothed Unrolling (SMUG),” in IEEE Journal of Selected Topics in Signal Processing, vol. 19, no. 7, pp. 1558-1573, Oct. 2025.
A. Ghosh, S. M. Kwon, R. Wang, S. Ravishankar, and Q. Qu, “Learning Dynamics of Deep Matrix Factorization Beyond the Edge of Stability,” in International Conference on Learning Representations (ICLR), 2025.
I. Alkhouri, S. Liang, C.-H. Huang, J. Dai, Q. Qu, S. Ravishankar, and R. Wang, “SITCOM: Stepwise Triple-Consistent Diffusion Sampling for Inverse Problems,” in International Conference on Machine Learning (ICML), 2025.
A. Ghosh, B. Cong, R. Yokota, S. Ravishankar, R. Wang, M. Tao, M. Emtiyaz Khan, and T. Mollenhoff, “Variational Learning Finds Flatter Solutions at the Edge of Stability,” in Annual Conference on Neural Information Processing Systems (NeurIPS), 2025. (Spotlight Paper)
S. Liang, I. Alkhouri, S. Gautam, Q. Qu, and S. Ravishankar, “UGoDIT: Unsupervised Group Deep Image Prior Via Transferable Weights,” in Annual Conference on Neural Information Processing Systems (NeurIPS), 2025.
S. Liang, I. Alkhouri, Q. Qu, R. Wang, and S. Ravishankar, “Sequential Diffusion-Guided Deep Image Prior for Medical Image Reconstruction,” ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Hyderabad, India, 2025, pp. 1-5.
E. Bell, S. Liang, I. Alkhouri, and S. Ravishankar, “Tada-DIP: Input-adaptive Deep Image Prior for One-shot 3D Image Reconstruction,” in session on ‘Referenceless training and evaluation for AI-based computational medical imaging’ at the Asilomar Conference on Signals, Systems, and Computers, 2025, pp. 237-241.
S. Liang, I. Alkhouri, Q. Qu, R. Wang, and S. Ravishankar, “Network-Regularized Diffusion Sampling For 3D Computed Tomography,” in 2025 IEEE 10th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Punta Cana, Dominican Republic, 2025, pp. 296-300.
A. Li, M. Zhang, T. Liu, B. H. Cohen, J. Rodriguez-Larios, and S. Ravishankar, "Analysis of Longitudinal Variations in Brain Oscillations for Breath-Focus and Mantra-Based Meditation," Program No. PSTR187.10. 2025 Neuroscience Meeting Planner. San Diego, CA: Society for Neuroscience, 2025.
A. Li, A. B. R. Syed, K. Ika, T. Liu, M. Zhang, B. H. Cohen, and S. Ravishankar, "Quantifying Improvements in Cognitive Skills, Stress, and Mindfulness from Mantra-based and Breath-focus Meditation Techniques," Program No. PSTR187.11. 2025 Neuroscience Meeting Planner. San Diego, CA: Society for Neuroscience, 2025.
A. Li, A. B. R. Syed, V. S. T. Nguyen, M. Zhang, B. H. Cohen, and S. Ravishankar, "Meditation Journey: Dataset and Benchmarks for Longitudinal Study of Different Meditation Techniques," Program No. PSTR146.01. 2025 Neuroscience Meeting Planner. San Diego, CA: Society for Neuroscience, 2025.
T. Wheeler, M. P. Kuchera, R. Ramanujan, R. Krupp, C. Wrede, S. Ravishankar, C. L. Cross, H. Y. I. Heung, A. J. Jones, and B. Votaw, “Sparse Methods for Vector Embeddings of TPC Data,” presented at the Machine Learning and the Physical Sciences (ML4PS) Workshop at NeurIPS 2025.
CPAL 2025 Recent Spotlight Track, Stanford, California, 2025.
1. I. Alkhouri, S. Liang, E. Bell, Q. Qu, R. Wang, and S. Ravishankar, “Image Reconstruction Via Autoencoding Sequential Deep Image Prior,” in CPAL 2025 Recent Spotlight Track (poster), 2025.
2. A. Ghosh, S. M. Kwon, R. Wang, S. Ravishankar, and Q. Qu, “Learning Dynamics of Deep Matrix Factorization Beyond the Edge of Stability,” in CPAL 2025 Recent Spotlight Track (poster), 2025.
3. I. Alkhouri, S. Liang, C.-H. Huang, J. Dai, Q. Qu, S. Ravishankar, and R. Wang, “SITCOM: Step wise Triple-Consistent Diffusion Sampling for Inverse Problems,” in CPAL 2025 Recent Spotlight Track (poster), 2025.

2024

A. Ghosh, M. T. McCann, M. Mitchell, and S. Ravishankar, “Sparsity-Promoting Regularizers using Bilevel Optimization,” in SIAM Journal on Imaging Sciences, vol. 17, no. 1, pp. 31-60, 2024.
S. Liang, A. Lahiri, and S. Ravishankar, "Adaptive Local Neighborhood-Based Neural Networks for MR Image Reconstruction From Undersampled Data," in IEEE Transactions on Computational Imaging, vol. 10, pp. 1235-1249, 2024.
R. Mahajan, T. Wheeler, E. Pollacco, C. Wrede, A. Adams, H. Alvarez-Pol, A. Andalib, A. Anthony, Y. Ayyad, D. Bazin, T. Budner, M. Cortesi, J. Dopfer, M. Friedman, B. Jain, A. Jaros, D. Pérez-Loureiro, B. Mehl, R. De Oliveira, S. Ravishankar, L. J. Sun, and J. Surbrook, “Time Projection Chamber for GADGET II,” Phys. Rev. C 110, 035807 – Published 12 September 2024.
S. Gautam, A. Li and S. Ravishankar, "Patient-Adaptive and Learned MRI Data Undersampling Using Neighborhood Clustering," ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024, pp. 2081-2085.
I. Alkhouri, S. Liang, R. Wang, Q. Qu and S. Ravishankar, "Diffusion-Based Adversarial Purification for Robust Deep MRI Reconstruction," ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024, pp. 12841-12845.
S. Liang, E. Bell, A. Ghosh, and S. Ravishankar, “Pruning Unrolled Networks (PUN) at Initialization for MRI Reconstruction Improves Generalization,” in 2024 58th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 2024, pp. 100-104.
A. Ghosh, X. Zhang, K. K. Sun, Q. Qu, S. Ravishankar, and R. Wang, "Optimal Eye Surgeon: Finding Image Priors Through Sparse Generators at Initialization," in International Conference on Machine Learning (ICML), 2024.
H. Zhang, Y. Lu, I. Alkhouri, S. Ravishankar, D. Song, and Q. Qu, "Improving Training Efficiency of Diffusion Models via Multi-Stage Framework and Tailored Multi-Decoder Architectures," IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), 2024, pp. 7372-7381.
I. Alkhouri, S. Liang, E. Bell, Q. Qu, R. Wang, and S. Ravishankar, "Image Reconstruction Via Autoencoding Sequential Deep Image Prior," in Annual Conference on Neural Information Processing Systems (NeurIPS), 2024.
I. Alkhouri, S. Liang, R. Wang, Q. Qu, and S. Ravishankar, "Robust Physics-based Deep MRI Reconstruction Via Diffusion Purification," in Conference on Parsimony and Learning (CPAL), 2024. Spotlight Track, Poster.
A. Li, P. Pradhan, K. Ika, M. Zhang, B. H. Cohen, and S. Ravishankar, "Understanding Longitudinal Effects of Mantra Meditation and Breath-focused Meditation using EEG," Program No. PSTR421.18. 2024 Neuroscience Meeting Planner. Chicago, IL: Society for Neuroscience, 2024.
IMSI Workshop on Computational Imaging, Chicago, Illinois, 2024.
1. A. Ghosh, X. Zhang, K. K. Sun, Q. Qu, S. Ravishankar, and R. Wang, “Optimal Eye Surgeon: Finding image priors through sparse generators at initialization,” presented as poster at IMSI Workshop on Computational Imaging, Chicago, 2024.
2. X. Li, S. Kwon, I. Alkhouri, S. Ravishankar, and Q. Qu, “Decoupled Data Consistency with Diffusion Purification for Image Restoration,” presented as poster at IMSI Workshop on Computational Imaging, Chicago, 2024.
3. S. Gautam, A. Li, N. Seiberlich, J. Fessler, and S. Ravishankar, “Scan-Adaptive MRI Undersampling Using Neighbor Based Optimization,” presented as poster at IMSI Workshop on Computational Imaging, Chicago, 2024.

2023

A. Lahiri, G. Maliakal, M. L. Klasky, J. A. Fessler and S. Ravishankar, "Sparse-View Cone Beam CT Reconstruction Using Data-Consistent Supervised and Adversarial Learning From Scarce Training Data," in IEEE Transactions on Computational Imaging, vol. 9, pp. 13-28, 2023.
Z. Zha, B. Wen, X. Yuan, S. Ravishankar, J. Zhou and C. Zhu, "Learning Nonlocal Sparse and Low-Rank Models for Image Compressive Sensing: Nonlocal Sparse and Low-Rank Modeling," in IEEE Signal Processing Magazine, vol. 40, no. 1, pp. 32-44, Jan. 2023.
L. Chen, X. Yang, Z. Huang, Y. Long, and S. Ravishankar, “Multi-layer Clustering-based Residual Sparsifying Transform for Low-dose CT Image Reconstruction,” Medical Physics, vol. 50, no. 10, pp. 6096–6117, 2023.
E. Bell, S. Liang, Q. Qu and S. Ravishankar, "Robust Self-Guided Deep Image Prior," ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023, pp. 1-5.
H. Li, J. Jia, S. Liang, Y. Yao, S. Ravishankar and S. Liu, "SMUG: Towards Robust MRI Reconstruction by Smoothed Unrolling," ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023, pp. 1-5.
C. Wang, R. Zhang, G. Maliakal, S. Ravishankar and B. Wen, "Deep Reinforcement Learning Based Unrolling Network for MRI Reconstruction," 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), Cartagena, Colombia, 2023, pp. 1-5.
L. Chen, Z. Huang, Y. Long, and S. Ravishankar, "Unifying Supervised and Unsupervised Methods for Low-dose CT Reconstruction: A General Framework," in 17th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D), 2023, pp. 38-41.
X. Li, S. Ravishankar and Q. Qu, "Robust Deep Image Recovery from Sparsely Corrupted and Sub-Sampled Measurements," 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Herradura, Costa Rica, 2023, pp. 521-525.
S. Gautam, M. L. Klasky, and S. Ravishankar, "Scatter Removal in Dynamic X-Ray Tomography using Learned Robust Features," in Optica Imaging Congress (3D, COSI, DH, FLatOptics, IS, pcAOP), Technical Digest Series (Optica Publishing Group, 2023), paper JTu4A.12. (Best Student Paper Award Finalist)
A. Li, P. Pradhan, A. Wozniak, B. H. Cohen, and S. Ravishankar. “Measuring the effectiveness of mantra-based meditation using EEG data analysis,” Program No: PSTR564.01. 2023 Neuroscience Meeting Planner. Washington, D.C.: Society for Neuroscience, 2023. Online.
Third Workshop on Seeking Low-Dimensionality in Deep Neural Networks
1. L. Chen, Z. Huang, Y. Long, and S. Ravishankar, "Combining Deep Learning and Adaptive Sparse Modeling for Low-dose CT Reconstruction," in Third Workshop on Seeking Low-Dimensionality in Deep Neural Networks, 2023. Presented as Poster.
2. S. Liang, E. Bell, S. Ravishankar, and Q. Qu, "Robust Self-Guided Deep Image Prior," in Third Workshop on Seeking Low-Dimensionality in Deep Neural Networks, 2023. Presented as Poster.
3. G. Maliakal, A. Lahiri, M. L. Klasky, J. A. Fessler, and S. Ravishankar, "Sparse-view Cone Beam CT Reconstruction using Data-consistent Supervised and Adversarial Learning from Scarce Training Data," in Third Workshop on Seeking Low-Dimensionality in Deep Neural Networks, 2023. Presented as Poster.
4. H. Li, J. Jia, S. Liang, Y. Yao, S. Ravishankar, and S. Liu, "SMUG: Towards Robust MRI Reconstruction by Smoothed Unrolling," in Third Workshop on Seeking Low-Dimensionality in Deep Neural Networks, 2023. Presented as Poster.
5. A. Ghosh and S. Ravishankar, "Bilevel learning of l1 regularizers with closed-form gradients (BLORC)," in Third Workshop on Seeking Low-Dimensionality in Deep Neural Networks, 2023. Presented as Poster.

2022

L. Guo, Z. Zha, S. Ravishankar and B. Wen, "Exploiting Non-Local Priors via Self-Convolution for Highly-Efficient Image Restoration," in IEEE Transactions on Image Processing, vol. 31, pp. 1311-1324, 2022.
Z. Huang, M. Klasky, T. Wilcox and S. Ravishankar, “Physics-Driven Learning of Wasserstein GAN for Density Reconstruction in Dynamic Tomography,” Appl. Opt. 61, 2805-2817 (2022).
Z. Huang and S. Ravishankar, “Single-pass Object-adaptive Data Undersampling and Reconstruction for MRI,” in IEEE Transactions on Computational Imaging, vol. 8, pp. 333-345, 2022.
A. Ghosh, M. T. McCann and S. Ravishankar, “Bilevel Learning of l1 Regularizers with Closed form Gradients (BLORC),” in ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 1491-1495.
S. Liang, A. Sreevatsa, A. Lahiri and S. Ravishankar, “LONDN-MRI: Adaptive Local Neighborhood-based Networks for MR Image Reconstruction from Undersampled Data,” in IEEE 19th International Symposium on Biomedical Imaging (ISBI), 2022, pp. 1-4.
A. N. Sietsema, M. T. McCann, M. L. Klasky and S. Ravishankar, “Comparing One-step and Two-step Scatter Correction and Density Reconstruction in X-ray CT,” in Proc. International Conference on Image Formation in X-ray Computed Tomography, 2022, pp. 292-295.
L. Chen, Z. Huang, Y. Long and S. Ravishankar, “Combining Deep Learning and Adaptive Sparse Modeling for Low-dose CT Reconstruction,” in Proc. International Conference on Image Formation in X-ray Computed Tomography, 2022, pp. 153-156.
R. Mahajan, A. Adams, J. Allmond, H. A. Pol, E. Argo, Y. Ayyad, D. Bardayan, D. Bazin, T. Budner, A. Chen, K. Chipps, B. Davids, J. Dopfer, M. Friedman, C. Fry, H. Fynbo, R. Grzywacz, J. Jose, J. Liang, S. Pain, D. Perex-Loureiro, E. Pollacco, A. Psaltis, S. Ravishankar, A. Rogers, L. Schaedig, L. Sun, J. Surbrook, T. Wheeler, L. Weghorn, and C. Wrede, "Measuring the ¹⁵O(α,γ)¹⁹Ne Reaction in Type I X-ray Bursts using GADGET II TPC: Software," in 16th International Symposium on Nuclei in the Cosmos (NIC), article no. 11034, 2022.
T. Wheeler, A. Adams, J. Allmond, H. A. Pol, E. Argo, Y. Ayyad, D. Bardayan, D. Bazin, T. Budner, A. Chen, K. Chipps, B. Davids, J. Dopfer, M. Friedman, H. Fynbo, R. Grzywacz, J. Jose, J. Liang, R. Mahajan, S. Pain, D. Perex-Loureiro, E. Pollacco, A. Psaltis, S. Ravishankar, A. Rogers, L. Schaedig, L. Sun, J. Surbrook, L. Weghorn, and C. Wrede, "Measuring the ¹⁵O(α,γ)¹⁹Ne Reaction in Type I X-ray Bursts using GADGET II TPC: Hardware," in 16th International Symposium on Nuclei in the Cosmos (NIC), article no. 11046, 2022.
C. Wang, R. Zhang, S. Ravishankar and B. Wen, "REPNP: Plug-and-Play with Deep Reinforcement Learning Prior for Robust Image Restoration," 2022 IEEE International Conference on Image Processing (ICIP), Bordeaux, France, 2022, pp. 2886-2890.
A. Ghosh, S. Liang, A. Lahiri, and S. Ravishankar, “Optimal Parallel Combination of Deep Networks and Sparsity Regularization for MR Image Reconstruction,” in International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2022.

2021

M. T. McCann, M. L. Klasky, J. L. Schei and S. Ravishankar, “Local models for scatter estimation and descattering in polyenergetic X-ray tomography,” Optics Express, vol. 29, no. 18, pp. 29423–29438, 2021.
A. Lahiri, G. Wang, S. Ravishankar and J. A. Fessler, "Blind and Primed Supervised (BLIPS) Learning for MR Image Reconstruction," in IEEE Transactions on Medical Imaging, vol. 40, no. 11, pp. 3113-3124, Nov. 2021.
S. Ye, Z. Li, M. T. McCann, Y. Long and S. Ravishankar, "Unified Supervised-Unsupervised (SUPER) Learning for X-Ray CT Image Reconstruction," in IEEE Transactions on Medical Imaging, vol. 40, no. 11, pp. 2986-3001, Nov. 2021.
X. Yang, Y. Long and S. Ravishankar, “Multi-layer Residual Sparsifying Transform (MARS) Model for Low-dose CT Image Reconstruction,” in Medical Physics, vol. 48, no. 10, pp. 6388-6400, 2021.
L. Guo, Z. Zha, S. Ravishankar and B. Wen, "Self-Convolution: A Highly-Efficient Operator for Non-Local Image Restoration," ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, pp. 1860-1864.
K. Yang, N. Borijindargoon, B. P. Ng, S. Ravishankar and B. Wen, "Learning Sparsifying Transforms for Image Reconstruction in Electrical Impedance Tomography," ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, pp. 1405-1409.
X. Yang, Y. Long, and S. Ravishankar, “Two-layer Clustering-based Sparsifying Transform Learning for Low-dose CT Reconstruction,” in the International Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine, 2021, pp. 206-209.
L. Chen, Y. Long, and S. Ravishankar, “Learning Overcomplete or Undercomplete Models in Clustering-based Low-dose CT Reconstruction,” in the International Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine, 2021, pp. 467-471.
S. Liang, B. Iskender, B. Wen and S. Ravishankar, "Labmat: Learned Feature-Domain Block Matching For Image Restoration," 2021 IEEE International Conference on Image Processing (ICIP), 2021, pp. 1689-1693.
A. Lahiri, M. L. Klasky, J. A. Fessler, and S. Ravishankar, “Limited-view Cone Beam CT reconstruction using 3D Patch-based Supervised and Adversarial Learning,” in OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP), OSA Technical Digest (Optical Society of America, 2021), paper DTh1D.4.
M. T. McCann, L. Pfister, A. Khatiwada, M. L. Klasky, J. L. Schei, and S. Ravishankar, "Descattering and Reconstruction in Multimaterial Polyenergetic X-Ray Tomography Using Local Scatter Models," in OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP), OSA Technical Digest (Optical Society of America, 2021), paper DF4F.6.
A. Lahiri, G. Wang, S. Ravishankar, and J. A. Fessler, “Blind Primed Supervised (BLIPS) Learning for MR Image Reconstruction,” in the International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2021, pp. 0279.
S. Liang, A. Sreevatsa, A. Lahiri, and S. Ravishankar, “Adaptive Local Neighborhood-based Networks for MR Image Reconstruction from Undersampled Data,” in the 2nd Learning for Computational Imaging Workshop at the International Conference on Computer Vision (ICCV), 2021.

2020

B. E. Moore, S. Ravishankar, R. R. Nadakuditi and J. A. Fessler, "Online Adaptive Image Reconstruction (OnAIR) Using Dictionary Models," in IEEE Transactions on Computational Imaging, vol. 6, pp. 153-166, 2020.
S. Ravishankar, A. Ma, and D. Needell, “Analysis of fast structured dictionary learning,” in Information and Inference: A Journal of the IMA, vol. 9, no. 4, pp. 785-811, December 2020.
S. Ye, S. Ravishankar, Y. Long and J. A. Fessler, "SPULTRA: Low-Dose CT Image Reconstruction with Joint Statistical and Learned Image Models," in IEEE Transactions on Medical Imaging, vol. 39, no. 3, pp. 729-741, March 2020.
M. B. Lien, C. H. Liu, I. Y. Chun, S. Ravishankar, H. Nien, M. Zhou, J. A. Fessler, Z. Zhong, and T. B. Norris, “Ranging and Light Field Imaging with Transparent Photodetectors,” in Nature Photonics, vol. 14, no. 3, pp. 143-148, 2020.
Z. Li, S. Ravishankar, Y. Long and J. A. Fessler, "Dual-Energy CT Image Decomposition with Learned Mixed Material Models and Efficient Clustering," in IEEE Transactions on Medical Imaging, vol. 39, no. 4, pp. 1223-1234, April 2020.
B. Wen, S. Ravishankar, L. Pfister and Y. Bresler, "Learning for Magnetic Resonance Image Reconstruction: From Model-Based Learning to Building Neural Networks," in IEEE Signal Processing Magazine, vol. 37, no. 1, pp. 41-53, Jan. 2020.
S. Ravishankar, J. C. Ye and J. A. Fessler, "Reconstruction: From Sparsity to Data Adaptive Methods and Machine Learning," in Proceedings of the IEEE, vol. 108, no. 1, pp. 86-109, Jan. 2020.
X. Zheng, S. Ravishankar, Y. Long, M. L. Klasky and B. Wohlberg, "Two-Layer Residual Sparsifying Transform Learning for Image Reconstruction," 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, USA, 2020, pp. 174-177. (Received Best Paper Award Finalist Certificate)
X. Yang, X. Zheng, Y. Long, and S. Ravishankar, “Learned Multi-layer Residual Sparsifying Transform Model for Low-dose CT Reconstruction,” in the International Conference on Image Formation in X-ray Computed Tomography, 2020, pp. 228-231.
A. Lahiri, S. Ravishankar, and J. A. Fessler, “Combining Supervised and Semi-Blind Dictionary (Super-BReD) Learning for MRI Reconstruction,” in the International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2020, pp. 3456.

2019

B. Wen, S. Ravishankar, and Y. Bresler, "VIDOSAT: High-dimensional Sparsifying Transform Learning for Online Video Denoising," in IEEE Transactions on Image Processing, vol. 28, no. 4, pp. 1691-1704, April 2019.
Z. Li, S. Ye, Y. Long and S. Ravishankar, "SUPER Learning: A Supervised-Unsupervised Framework for Low-Dose CT Image Reconstruction," 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Korea (South), 2019, pp. 3959-3968.
IMA Workshop on Computational Imaging, Minneapolis, Minnesota, 2019.
1. M. T. McCann and S. Ravishankar, "Learning Regularization Filters for Image Reconstruction," in IMA Workshop on Computational Imaging, Minneapolis, Minnesota, October 14-18, 2019, presented as poster.
2. A. Sunalkar, R. Wang, V. Boddeti, and S. Ravishankar, "Sparse Representation Learning: A Comparative Study," in IMA Workshop on Computational Imaging, Minneapolis, Minnesota, October 14-18, 2019, presented as poster.
3. Z. Li, S. Ye, Y. Long, and S. Ravishankar, "A Supervised-Unsupervised (SUPER) Learning Framework for Image Reconstruction," in IMA Workshop on Computational Imaging, Minneapolis, Minnesota, October 14-18, 2019, presented as poster.
4. X. Yang, X. Zheng, S. Ravishankar, Y. Long, B. Wohlberg, and M. L. Klasky, "Multi-layer Residual Sparsifying Transform Learning Model for Low-dose CT Image Reconstruction," in IMA Workshop on Computational Imaging, Minneapolis, Minnesota, October 14-18, 2019, presented as poster.
5. M. Klasky, S. Ravishankar, B. Iskender, J. S. Disterhaupt, Y. Lin, and D. Sanzo, "Physics Based Machine Learning for Radiographic Reconstructions," in IMA Workshop on Computational Imaging, Minneapolis, Minnesota, October 14-18, 2019, presented as poster.
6. A. Lahiri, N. Murthy, C. Blocker, S. Ravishankar, and J. A. Fessler, "Combining Supervised and Semi-Blind Residual Dictionary (Super-BReD) Learning," in IMA Workshop on Computational Imaging, Minneapolis, Minnesota, October 14-18, 2019, presented as poster.