Thesis

1. S. Ravishankar, "Adaptive sparse representations and their applications," Ph.D. thesis, University of Illinois at Urbana-Champaign, 2014.

2. S. Ravishankar, "Magnetic resonance image reconstruction from highly undersampled K-Space data using dictionary learning," Master's thesis, University of Illinois at Urbana-Champaign, 2010.

3. S. Ravishankar, "Object Recognition Using Contour Segments," B. Tech. Thesis, Indian Institute of Technology (IIT), Madras, 2008.

Journals

1. S. Ravishankar and Y. Bresler, “MR image reconstruction from highly undersampled k-space data by dictionary learning,” IEEE Transactions on Medical Imaging, vol. 30, no. 5, pp. 1028–1041, 2011.

2. S. Ravishankar and Y. Bresler, “Learning sparsifying transforms,” IEEE Transactions on Signal Processing, vol. 61, no. 5, pp. 1072–1086, 2013. (IEEE Signal Processing Society Young Author Best Paper Award for 2016) (ECE ILLINOIS News)

3. S. Ravishankar and Y. Bresler, “Learning doubly sparse transforms for images,” IEEE Transactions on Image Processing, vol. 22, no. 12, pp. 4598-4612, Dec. 2013.

4. B. Wen, S. Ravishankar, and Y. Bresler, "Structured overcomplete sparsifying transform learning with convergence guarantees and applications," International Journal of Computer Vision, vol. 114, no. 2-3, pp. 137–167, 2015.

5. S. Ravishankar, B. Wen, and Y. Bresler, “Online Sparsifying Transform Learning – Part I: Algorithms,” IEEE Journal of Selected Topics in Signal Processing, vol. 9, no. 4, pp. 625-636, June 2015.

6. S. Ravishankar and Y. Bresler, “Online Sparsifying Transform Learning – Part II: Convergence Analysis,” IEEE Journal of Selected Topics in Signal Processing, vol. 9, no. 4, pp. 637-646, June 2015.

7. S. Ravishankar and Y. Bresler, “\ell_0 Sparsifying transform learning with efficient optimal updates and convergence guarantees,” IEEE Transactions on Signal Processing, vol. 63, no. 9, pp. 2389-2404, May 2015. (arXiv)

8. S. Ravishankar and Y. Bresler, "Efficient blind compressed sensing using sparsifying transforms with convergence guarantees and application to magnetic resonance imaging,” SIAM Journal on Imaging Sciences, vol. 8, no. 4, pp. 2519–2557, 2015. (arXiv)

9. S. Ravishankar and Y. Bresler, “Data-Driven Learning of a Union of Sparsifying Transforms Model for Blind Compressed Sensing,” in IEEE Transactions on Computational Imaging, vol. 2, no. 3, pp. 294-309, Sept. 2016. (arXiv)

10. S. Ravishankar, B. E. Moore, R. R. Nadakuditi, and J. A. Fessler, “Low-rank and Adaptive Sparse Signal (LASSI) Models for Highly Accelerated Dynamic Imaging,” in IEEE Transactions on Medical Imaging, vol. 36, no. 5, pp. 1116-1128, May 2017. (arXiv)

11. B. Wen, S. Ravishankar, and Y. Bresler, "FRIST - Flipping and Rotation Invariant Sparsifying Transform Learning and Applications," in Inverse Problems, vol. 33, no. 7, pp. 074007-1- 074007-27, June 2017. (arXiv)

12. S. Ravishankar, R. R. Nadakuditi, and J. A. Fessler, “Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its Application to Inverse Problems,” in IEEE Transactions on Computational Imaging, vol. 3, no. 4, pp. 694-709, Dec. 2017. (arXiv)

13. X. Zheng, S. Ravishankar, Y. Long, and J. A. Fessler, “PWLS-ULTRA: An Efficient Clustering and Learning-Based Approach for Low-Dose 3D CT Image Reconstruction,” in IEEE Transactions on Medical Imaging, vol. 37, no. 6, pp. 1498-1510, June 2018. (arXiv)

14. 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. (arXiv)

15. B. 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. (arXiv)

16. 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. (arXiv)

17. 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. (arXiv)

18. 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.

19. Z. Li, S. Ravishankar, Y. Long and J. A. Fessler, "DECT-MULTRA: 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. (arXiv)

20. B. Wen, S. Ravishankar, L. Pfister and Y. Bresler, "Transform 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. (arXiv)

21. S. Ravishankar, J. C. Ye and J. A. Fessler, "Image Reconstruction: From Sparsity to Data-Adaptive Methods and Machine Learning," in Proceedings of the IEEE, vol. 108, no. 1, pp. 86-109, Jan. 2020. (arXiv)

22. 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.

23. A. Lahiri, G. Wang, S. Ravishankar and J. A. Fessler, "Blind Primed Supervised (BLIPS) Learning for MR Image Reconstruction," in IEEE Transactions on Medical Imaging, vol. 40, no. 11, pp. 3113-3124, Nov. 2021.

24. 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.

25. 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.

26. 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.

27. 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).

28. 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.

29. A. Lahiri, M. Klasky, G. Maliakkal, J. A. Fessler and S. Ravishankar, “Sparse-view Cone Beam CT Reconstruction using Data-consistent Supervised and Adversarial Learning from Scarce Training Data,” accepted to IEEE Transactions on Computational Imaging, 2022. (arXiv)

30. Z. Zha, B. Wen, X. Yuan, S. Ravishankar, J. Zhou, and C. Zhou, “Learning Nonlocal Sparse and Low-Rank Models for Image Compressive Sensing,” accepted to IEEE Signal Processing Magazine Special Issue on Physics-Driven Machine Learning for Computational Imaging, 2022. (arXiv)

31. A. Ghosh, M. T. McCann, M. Mitchell, and S. Ravishankar, “Learning Sparsity-Promoting Regularizers using Bilevel Optimization,” submitted to SIAM Journal on Imaging Sciences, 2022. (arXiv)

32. S. Liang, A. Lahiri, and S. Ravishankar, “Adaptive Local Neighborhood-based Neural Networks for MR Image Reconstruction from Undersampled Data,” submitted to IEEE Transactions on Medical Imaging, 2022. (arXiv)

33. X. Yang, Z. Huang, Y. Long, and S. Ravishankar, “Multi-layer Clustering-based Residual Sparsifying Transform for Low-dose CT Image Reconstruction,” submitted to Medical Physics, 2022. (arXiv)

Conference and Workshop Papers

1. S. Ravishankar, A. Jain, and A. Mittal, “Multi-stage contour based detection of deformable objects,” in European Conference on Computer Vision, 2008, pp. 483-496.

2. S. Ravishankar, A. Jain, and A. Mittal, “Automated feature extraction for early detection of diabetic retinopathy in fundus images,” in IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 210 -217.

3. S. Ravishankar and Y. Bresler, “Highly undersampled MRI using adaptive sparse representations,” in IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1585–1588, 2011.

4. S. Ravishankar and Y. Bresler, “Adaptive sampling design for compressed sensing MRI,” in Conf. Proc. IEEE Eng. Med. Biol. Soc., pp. 3751–3755, 2011.

5. S. Ravishankar and Y. Bresler, “Learning sparsifying transforms for image processing,” in IEEE International Conference on Image Processing (ICIP), 2012, pp. 681–684.

6. S. Ravishankar and Y. Bresler, “Learning doubly sparse transforms for image representation,” in IEEE International Conference on Image Processing (ICIP), 2012, pp. 685-688.

7. S. Ravishankar and Y. Bresler, “Sparsifying transform learning for compressed sensing MRI,” in Proc. IEEE Int. Symp. Biomed. Imag., 2013, pp. 17-20.

8. S. Ravishankar and Y. Bresler, "Closed-form solutions within sparsifying transform learning," in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2013, pp. 5378-5382.

9. S. Ravishankar and Y. Bresler, "Learning overcomplete sparsifying transforms for signal processing," in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2013, pp. 3088-3092.

10. S. Ravishankar and Y. Bresler, "Doubly sparse transform learning with convergence guarantees," in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014, pp. 5262-5266.

11. B. Wen, S. Ravishankar, and Y. Bresler, “Learning overcomplete sparsifying transforms with block cosparsity,” in IEEE International Conference on Image Processing (ICIP), October, 2014, pp. 803-807. (Received the Top 10% Paper Award Certificate from the conference)

12. S. Ravishankar, B. Wen, and Y. Bresler, “Online Sparsifying Transform Learning for Signal Processing,” in IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2014, pp. 364-368.

13. S. Ravishankar and Y. Bresler, “Blind compressed sensing using sparsifying transforms,” in International Conference on Sampling Theory and Applications (SampTA), May 2015, pp. 513–517.

14. S. Ravishankar and Y. Bresler, “Data-driven adaptation of a union of sparsifying transforms for blind compressed sensing MRI reconstruction,” in Proc. SPIE, vol. 9597, 2015, pp. 959 713–959 713– 10. (Invited)

15. B. Wen, S. Ravishankar, and Y. Bresler, “Video Denoising By Online 3D Sparsifying Transform Learning,” in IEEE International Conference on Image Processing (ICIP), 2015, pp. 118-122.

16. B. Wen, S. Ravishankar, and Y. Bresler, "Learning Flipping and Rotation Invariant Sparsifying Transforms," in IEEE International Conference on Image Processing (ICIP), 2016, pp. 3857-3861.

17. S. Ravishankar, B. E. Moore, R. R. Nadakuditi, and J. A. Fessler, “LASSI: A Low-rank and Adaptive Sparse Signal Model for Highly Accelerated Dynamic Imaging,” in IEEE Image, Video, and Multidimensional Signal Processing (IVMSP) Workshop, 2016, pp. 1-5.

18. X. Zheng, Z. Lu, S. Ravishankar, Y. Long, and J. A. Fessler, “Low Dose CT Image Reconstruction with Learned Sparsifying Transform,” in IEEE Image, Video, and Multidimensional Signal Processing (IVMSP) Workshop, 2016, pp. 1-5. (Updated arXiv version)

19. S. Ravishankar, R. R. Nadakuditi, and J. A. Fessler, “Sum of Outer Products Dictionary Learning for Inverse Problems,” in IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2016, pp. 1142-1146. (Invited Paper in Symposium on Big Data Analysis and Challenges in Medical Imaging)

20. S. Ravishankar, B. E. Moore, R. R. Nadakuditi, and J. A. Fessler, “Efficient Learning of Dictionaries with Low-rank Atoms,” in IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2016, pp. 222-226.

21. X. Zheng, S. Ravishankar, Y. Long, and J. A. Fessler, "Union of Learned Sparsifying Transforms Based Low-Dose 3D CT Image Reconstruction," in International Conference on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine, 2017, pp. 69-72.

22. S. Ravishankar and J. A. Fessler, "Data-driven Models and Approaches for Imaging," in OSA Imaging and Applied Optics, 2017, paper MW2C.4. (Invited)

23. B. E. Moore and S. Ravishankar, “Online Data-driven Dynamic Image Restoration using DINO-KAT Models,” in IEEE International Conference on Image Processing (ICIP), 2017, pp. 3590-3594.

24. S. Ye, S. Ravishankar, Y. Long, and J. A. Fessler, “Adaptive Sparse Modeling and Shifted-Poisson Likelihood Based Approach for Low-Dose CT Image Reconstruction,” in IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2017, pp. 1-6. (Finalist for Best Student Paper Award)

25. S. Ravishankar, I. Y. Chun, and J. A. Fessler, “Physics-Driven Deep Training of Dictionary-Based Algorithms for MR Image Reconstruction,” in Asilomar Conference on Signals, Systems, and Computers, 2017, pp. 1859-1863. (Invited Paper in session on Computational Imaging)

26. S. Ravishankar, B. E. Moore, R. R. Nadakuditi, and J. A. Fessler, “Efficient Online Dictionary Adaptation and Image Reconstruction for Dynamic MRI," in Asilomar Conference on Signals, Systems, and Computers, 2017, pp. 835-839.

27. Z. Li, S. Ravishankar, Y. Long, and J. A. Fessler, “Image-domain Material Decomposition using Data-driven Sparsity Models for Dual-energy CT,” in IEEE International Symposium on Biomedical Imaging (ISBI), 2018, pp. 52-56. (Won a Best Student Paper Award)

28. S. Ravishankar, A. Lahiri, C. Blocker, and J. A. Fessler, “Deep Dictionary-Transform Learning for Image Reconstruction,” iin IEEE International Symposium on Biomedical Imaging (ISBI), 2018, pp. 1208-1212. (Invited)

29. S. Ravishankar, A. Ma, and D. Needell, “Analysis of Fast Alternating Minimization for Structured Dictionary Learning,” in Information Theory and Applications Workshop (ITA), San Diego, CA, 2018, pp. 1-9. (Invited)

30. Z. Li, S. Ravishankar, Y. Long, and J. A. Fessler, “Learned Mixed Material Models for Efficient Clustering Based Dual-Energy CT Image Decomposition,” in IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2018, pp. 529-533.

31. S. Ravishankar and B. Wohlberg, “Learning Multi-Layer Transform Models," in 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, USA, 2018, pp. 160-165. (Invited)

32. Z. Li, S. Ravishankar, and Y. Long, "Image-domain Multi-Material Decomposition Using a Union of Cross-Material Models," Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2019, pp. 1107210-1-1107210-5.

33. 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. (arXiv version)

34. 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. (arXiv version) (Received Best Paper Award Finalist Certificate)

35. 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. (arXiv version)

36. 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.

37. 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.

38. 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.

39. 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.

40. 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.

41. 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.

42. 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.

43. 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.

44. 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.

45. 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.

46. 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.

47. 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 Reaction in Type I X-ray Bursts using GADGET II TPC: Software,” in 16^th International Symposium on Nuclei in the Cosmos (NIC), article no. 11034, 2022.

48. 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 Reaction in Type I X-ray Bursts using GADGET II TPC: Hardware,” in 16^th International Symposium on Nuclei in the Cosmos (NIC), article no. 11046, 2022.

49. C. Wang, R. Zhang, S. Ravishankar, and B. Wen, “REPNP: Plug-and-Play with Deep Reinforcement Learning Prior for Robust Image Restoration,” in IEEE International Conference on Image Processing, 2022, to appear.

Conferences/Workshops with Short Abstracts or Posters

1. S. Ravishankar and Y. Bresler, “Multiscale dictionary learning for MRI,” in Proc. ISMRM, page 2830, 2011.

2. S. Ravishankar and Y. Bresler, “Learning Sparsifying Transforms for Signal and Image Processing,” in SIAM Conference on Imaging Science, May 2012, p. 51.

3. S. Ravishankar and Y. Bresler, “Closed-Form Optimal Updates In Transform Learning,” in Signal Processing with Adaptive Sparse Structured Representations (SPARS) workshop, July 2013.

4. S. Ravishankar and Y. Bresler, “Learning Overcomplete Signal Sparsifying Transforms,” in Signal Processing with Adaptive Sparse Structured Representations (SPARS) workshop, July 2013.

5. S. Ravishankar and Y. Bresler, “Efficient Sparsifying Transform Learning and its Applications,” in Gordon Research Conference on Image Science, June 2014. (Poster)

6. B. Wen, S. Ravishankar, and Y. Bresler, “Online Sparsifying Transform Learning and Applications,” in Gordon Research Conference on Image Science, June 2014. (Poster)

7. S. Ravishankar, R. R. Nadakuditi, and J. A. Fessler, "Efficient Dictionary Learning Methods Using Sums of Outer Products (SOUP-DIL)," in IMA Workshop on Optimization and Parsimonious Modeling, January 2016, presented as poster. Abstract Online.

8. S. Ravishankar, R. R. Nadakuditi, J. A. Fessler, and B. E. Moore, “Efficient Dictionary Learning Models and Their Application to Inverse Problems,” in Gordon Research Conference on Image Science, June 2016. (Poster)

9. X. Zheng, S. Ravishankar, Z. Lu, Y. Long, and J. A. Fessler, “Image reconstruction for Low Dose X-ray CT Using Learned Overcomplete Sparsifying Transforms,” in Gordon Research Conference on Image Science, June 2016. (Poster)

10. 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. Abstract Online.

11. 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. Abstract Online.

12. 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. Abstract Online.

13. 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. Abstract Online.

14. 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. Abstract Online.

15. 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. Abstract Online.

16. 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.

17. M. T. McCann and S. Ravishankar, “Learning Regularizers for Image Reconstruction,” in SIAM Conference on Imaging Science, 2020. Abstract Online.

18. 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.

19. 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. (Accepted extended abstract)

20. Z. Huang, M. L. Klasky, and S. Ravishankar, “Learning from Hydrodynamics Simulations with Mass Constraints for Density Reconstruction in Dynamic Tomography,” in Machine Learning for Scientific Imaging (MLSI), 2022. Abstract Online.

21. M. Klasky, A. Lahiri, J. Fessler, S. Ravishankar, M. Espy, M. T. McCann, T. Wilcox, and A. Khatiwada, “Limited-view cone beam CT reconstruction using 3D patch-based supervised and adversarial learning: Validation using hydrodynamic simulations and experimental tomographic data,” in Machine Learning for Scientific Imaging (MLSI), 2022. Abstract Online.

22. 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.

23. S. Liang, X. Li, A. Ghosh, Q. Qu, S. Ravishankar, “Robust Deep Image Prior with Partial Guidance,” in Allerton Conference on Communication, Control, and Computing, 2022. Abstract Online.

24. S. Liang, J. Jia, S. Liu, and S. Ravishankar, “Improving the Robustness of Deep Unrolling-based MRI Reconstruction by Learned Randomized Smoothing,” in SIAM Conference on Computational Science and Engineering, 2023.

Patents

1. Indian Patent 278693, “Automated System for Early Detection of Diabetic Retinopathy,” filed January 13, 2009. Application number: 94/CHE/2009. Status: Granted.

2. United States Patent 9734601, “Highly Accelerated Imaging and Image Reconstruction using Adaptive Sparsifying Transforms,” filed April 3, 2015. Granted August 15, 2017.

3. International PCT Patent Application PCT/US2017/058654, “Method of Dynamic Radiographic Imaging using Singular Value Decomposition,” filed October 27, 2017.

4. US Provisional Patent Application “Data-Driven Adaptation of a Union of Sparse Models and Its Applications,” filed October 15, 2015 and assigned serial number 62/241,951.

5. US Provisional Patent Application “Efficient Online Data-Driven Learning of Sparsifying Transforms for Large-Scale Signal Processing Application,” filed December 1, 2015 and assigned serial number 62/261,362.

Book Chapters

1. S. Ravishankar and R. R. Nadakuditi, “Dictionary Methods for Compressed Sensing: Framework for Microscopy,” in Statistical Methods for Materials Science: The Data Science of Microstructure Characterization, published February 2019.

2. Z. Huang, S. Ye, M. T. McCann, and S. Ravishankar, “Model-based Reconstruction with Learning: From Unsupervised to Supervised and Beyond,” in Deep Learning for Biomedical Image Reconstruction, edited by Jong Chul Ye, Yonina C Eldar, and Michael Unser, Cambridge University Press, expected to be published in 2022.