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Saiprasad Ravishankar's Home Page
  • Home
  • Publications
  • Curriculum Vitae
  • Teaching
  • Recent News
  • Project and Software Links
  • Contact
  • More
    • Home
    • Publications
    • Curriculum Vitae
    • Teaching
    • Recent News
    • Project and Software Links
    • Contact
  • 06/15/25: Article on "Variational Learning Finds Flatter Solutions at the Edge of Stability" posted online.  (arXiv)

  • 06/08/25: Revised article on "DCDP: Decoupled Data Consistency via Diffusion Purification for Solving General Inverse Problems" posted online.  (arXiv)

  • 05/19/25: Article on "A Dataless Reinforcement Learning Approach to Rounding Hyperplane Optimization for Max-Cut" posted online.  (arXiv)

  • 05/16/25: Article on "UGoDIT: Unsupervised Group Deep Image Prior Via Transferable Weights" posted online.  (arXiv)

  • 05/15/25: Our paper on "Object Detection with Deep Learning for Rare Event Search in the GADGET II TPC" has been accepted for publication in Nuclear Inst. and Methods in Physics Research, A. Joint work with postdoc, Dr. Tyler Wheeler, FRIB collaborator Dr. Christopher Wrede and others.

  • 05/08/25: Our work on "Learning Robust Features for Scatter Removal and Reconstruction in Dynamic ICF X-Ray Tomography" has been accepted for publication in Optics Express. Work with my PhD student Siddhant Gautam and Los Alamos National Laboratory collaborators.

  • 05/02/25:  Revised version of paper on "Object Detection with Deep Learning for Rare Event Search in the GADGET II TPC" was submitted to Nuclear Inst. and Methods in Physics Research, A.  (arXiv)

  • 05/01/25: Our paper titled "SITCOM: Step-wise Triple-Consistent Diffusion Sampling For Inverse Problems" has been accepted to the International Conference on Machine Learning (ICML) 2025.  (arXiv)

  • 05/01/25: Paper on "Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)" submitted to IEEE Transactions on Computational Imaging.

  • 05/01/25: Abstract titled "Input-Adaptive Autoencoding Deep Prior For One-shot 3D Image Reconstruction" submitted to session on Referenceless training and evaluation for AI-based computational medical imaging  at the Asilomar Conference on Signals, Systems, and Computers, 2025 (Invited).

  • 04/30/25: Abstract titled "Network-Regularized Diffusion Sampling For 3D Computed Tomography" submitted to session on Advances in machine learning for inverse imaging problems at the Asilomar Conference on Signals, Systems, and Computers, 2025 (Invited).

  • 04/16/25:  Revised version of paper titled "Learning Robust Features for Scatter Removal and Reconstruction in Dynamic ICF X-Ray Tomography" sent to Optics Express.  (arXiv)

  • 04/09/25: Gave talk on our work "Sequential Diffusion-Guided Deep Image Prior For Medical Image Reconstruction" at IEEE ICASSP 2025 in Hyderabad, India.

  • 04/07/25-04/11/25: Chaired sessions on "Computational Imaging Methods and Applications", "Physiological and wearable signal processing I", and "Medical image analysis II" at IEEE ICASSP 2025.

  • 03/26/25: Organized the Wellness Session and the Panel Discussion at the Conference on Parsimony and Learning (CPAL), 2025.

  • 02/25/25: Article on "Understanding Untrained Deep Models for Inverse Problems: Algorithms and Theory" posted online.  (arXiv)

  • 02/16/25: Proposal for an IEEE JSTSP Special Issue on "High-Dimensional Imaging: Emerging Challenges and Advances in Reconstruction and Restoration" was accepted.  Serving as Guest Editor. Call for papers is here.

  • 01/28/25: Paper on "Object Detection with Deep Learning for Rare Event Search in the GADGET II TPC" posted online.  (arXiv)

  • 01/22/25: Work titled "Learning Dynamics of Deep Matrix Factorization Beyond the Edge of Stability" accepted at ICLR 2025. Available online.

  • 01/16/25: Work titled "Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)" posted online.  (arXiv)

  • 01/14/25: Paper on "Analysis of Deep Image Prior and Exploiting Self-Guidance for Image Reconstruction" accepted to IEEE Transactions on Computational Imaging.  (arXiv)  

  • 01/08/25: Work titled "Learnable Scaled Gradient Descent for Guaranteed Robust Tensor PCA" posted online.  (arXiv)

  • 12/30/24: Received the NSF CAREER Award from the CISE Directorate (CCF Division) for work on "Robust Machine Learning Methods for Imaging". 

  • 12/24/24: Paper titled "Pruning Unrolled Networks (PUN) at Initialization for MRI Reconstruction Improves Generalization" posted to arXiv (Link). Accepted at the Asilomar Conference on Signals, Systems, and Computers, 2024. 

  • 12/20/24: Work on "Sequential Diffusion-Guided Deep Image Prior For Medical Image Reconstruction" will appear in IEEE ICASSP 2025.  (arXiv) (Code)

  • 12/10/24: White paper on "Understanding Untrained Deep Models for Inverse Problems: Algorithms and Theory" accepted in the Special Issue on The Mathematics of Deep Learning in the IEEE Signal Processing Magazine.

  • 11/15/24: Organized the 4th CMSE Data Science Student Conference (DISC) 2024 at MSU.

  • 11/14/24: Elected to the IEEE Bio Image and Signal Processing Technical Committee (BISP TC) as member. 

  • 10/21/24: Presented our work on EEG-based analysis of the longitudinal effects of different types of meditation in the Minds, Brains, and Machines conference held in the Center for Data Science at New York University.

  • 10/06/24: Article on "SITCOM: Step-wise Triple-Consistent Diffusion Sampling for Inverse Problems" posted online.  (arXiv)

  • 10/02/24: Work on "Learning Dynamics of Deep Matrix Factorization Beyond the Edge of Stability" posted online.

  • 09/29/24: Serving as Area Chair for the Machine Learning for Signal Processing track of IEEE ICASSP 2025.

  • 09/25/24: Our paper on "Image Reconstruction via Autoencoding Sequential Deep Image Prior" has been accepted at NeurIPS 2024. Work with my PhD student Shijun Liang, postdoc Dr. Ismail Alkhouri, undergraduate student Evan Bell and collaborators Drs. Rongrong Wang and Qing Qu.  (Paper)  (Code)

  • 09/18/24: Gave a talk on “Advancing Machine Learning for Biomedical Imaging” at the BME/IQ Seminar Series in MSU.

  • 09/12/24: Paper titled "Unrolled Diffusion-Guided Deep Image Prior For Medical Image Reconstruction" submitted to IEEE ICASSP 2025.

  • 08/23/24: Paper titled "Learning Robust Features for Scatter Removal and Reconstruction in Dynamic ICF X-Ray Tomography" submitted to Optics Express.  (arXiv)

  • 08/21/24:  Paper on "Enhancing Low-dose CT Image Reconstruction by Parallel Integration of Supervised and Unsupervised Learning" submitted to IEEE Transactions on Radiation and Plasma Medical Sciences.

  • 08/09/24: Gave a talk on "Robustness and Regularization in Deep Learning-based Imaging" at the IMSI Workshop on Computational Imaging in Chicago.

  • 08/05/24-08/09/24: Organized the Workshop on Computational Imaging at the Institute for Mathematical and Statistical Innovation (IMSI), Chicago.  (Workshop Website)

  • 07/29/24: Work titled "Pruning Unrolled Networks (PUN) at Initialization for MRI Reconstruction Improves Generalization" accepted to the Asilomar Conference on Signals, Systems, and Computers, 2024.

  • 07/23/24: Paper titled "Time projection chamber for GADGET II" accepted  for publication as a regular article in Physical Review C.  (Paper Link)

  • 07/16/24: Our abstract titled "Understanding Longitudinal Effects of Mantra Meditation and Breath-focused Meditation using EEG" has been accepted for presentation at Neuroscience 2024.

  • 07/13/24: I will be part of Organizing Committee for the 2nd Conference on Parsimony and Learning (CPAL), 2025.  (Website)

  • 06/17/24: Organizing the Data-driven Understanding of Meditation and Consciousness (DUNES) Webinar Series  (Webinar Website) 

  • 05/15/24: Received NSF Award for Research Experiences for Undergraduates (REU) for supporting 4 undergraduate researchers, along with Dr. Rongrong Wang. This will be part of the CISE Medium Award for research on "Taming Deep Unsupervised Representation Learning in Imaging: Theory and Algorithms".

  • 05/07/24: Abstract on "Understanding Longitudinal Effects of Mantra Meditation and Breath-focused Meditation using EEG" submitted to Neuroscience 2024.

  • 05/01/24: Our paper titled "Optimal Eye Surgeon: Finding image priors through sparse generators at initialization" accepted at the International Conference on Machine Learning (ICML) 2024.  (arXiv) (Code)

  • 05/01/24: Abstract titled "Pruning Unrolled Networks (PUN) at Initialization for MRI Reconstruction Improves Generalization" submitted to session on Machine Learning Methods for Inverse Problems in Biomedical Imaging at the Asilomar Conference on Signals, Systems, and Computers, 2024 (Invited).

  • 03/24/24: Paper on "Adaptive Local Neighborhood-based Neural Networks for MR Image Reconstruction from Undersampled Data" accepted to IEEE Transactions on Computational Imaging.

  • 02/26/24: Paper on "Improving Training Efficiency of Diffusion Models via Multi-Stage Framework and Tailored Multi-Decoder Architectures" accepted to IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024.  (arXiv)

  • 02/20/24: Revised version of "Adaptive Local Neighborhood-based Neural Networks for MR Image Reconstruction from Undersampled Data" accepted with minor revisions to IEEE Transactions on Computational Imaging.  (arXiv version)

  • 02/08/24: Serving as Area Chair for the IEEE International Conference on Image Processing (ICIP), 2024.

  • 02/01/24: Revised version of paper "Robust Physics-based Deep MRI Reconstruction Via Diffusion Purification" submitted to IEEE Transactions on Medical Imaging.

  • 01/31/24: Paper on "Analysis of Deep Image Prior and Exploiting Self-Guidance for Image Reconstruction" submitted to IEEE Transactions on Computational Imaging.  (arXiv)

  • 01/22/24: Revised version of paper "Adaptive Local Neighborhood-based Neural Networks for MR Image Reconstruction from Undersampled Data" submitted to IEEE Transactions on Computational Imaging.  (arXiv version)

  • 01/17/24: Invited to give a talk in session on AI-based approaches for image formation in CT and interventional X-ray imaging at the AAPM Annual Meeting in Los Angeles during July 21-25, 2024.

  • 01/06/24: Presented a half-day tutorial on "Advances in Machine Learning for Image Reconstruction: Sparse Models to Deep Networks" at the Conference on Parsimony and Learning (CPAL), 2024.  (See here) 

  • 12/14/23: Paper on "Improving Training Efficiency of Diffusion Models via Multi-Stage Framework and Tailored Multi-Decoder Architectures" posted online.  (arXiv)

  • 12/13/23: Our papers on "Patient-adaptive and Learned MRI Data Undersampling Using Neighborhood Clustering" (arXiv) and "Diffusion-based Adversarial Purification for Robust Deep MRI Reconstruction" accepted to IEEE ICASSP 2024. 

  • 12/12/23: Paper on "Robust MRI Reconstruction by Smoothed Unrolling (SMUG)" has been posted online.  (arXiv)

  • 11/29/23: Abstract on "Scatter Removal in Dynamic X-Ray Tomography using Robust Features" accepted for oral presentation at Machine Learning for Scientific Imaging, 2024. 

  • 11/20/23: Work on "Robust Physics-based Deep MRI Reconstruction via Diffusion Purification" will be presented (by postdoc Ismail Alkhouri) in the Spotlight track at the Conference on Parsimony and Learning, 2024.

  • 11/15/23: Work on "Measuring the effectiveness of mantra-based meditation using EEG data analysis" presented at Neuroscience 2023. Work with PhD student Angqi Li, undergrads Pratham Pradhan and Annie Wozniak, company Brainwave Science, and NYU collaborator Dr. Barry Cohen.

  • 11/10/23: Co-organizing the 3rd CMSE Data Science Student Conference (DISC) at MSU.

  • 11/08/23: Abstract on "Patient-Adaptive k-space Undersampling for Cardiac MRI Using a Dictionary of Learned Patterns" submitted to ISMRM 2024.

  • 11/06/23: Elected to the IEEE Machine Learning for Signal Processing Technical Committee (MLSP TC) as member.

  • 10/29/23: Invited to give a talk in the Mini-symposium on "Model-based and data-driven hybrid methods in computational imaging" at the SIAM Conference on Imaging Science, 2024.

  • 10/20/23: Proposal for workshop on Computational Imaging accepted by the Institute for Mathematical and Statistical Innovation (IMSI), Chicago. Workshop will be held August 5-9, 2024. (Workshop Website)

  • 10/01/23: Paper on "Robust Physics-based Deep MRI Reconstruction Via Diffusion Purification" submitted to IEEE Transactions on Medical Imaging.  (arXiv)

  • 09/22/23: Paper on "Robust Deep Image Recovery From Sparsely Corrupted and Sub-Sampled Measurements" accepted to IEEE CAMSAP 2023.

  • 09/06/23:  Paper on "Patient-adaptive and Learned MRI Data Undersampling Using Neighborhood Clustering" submitted to IEEE ICASSP 2024.

  • 09/05/23: Paper on "Diffusion-based Adversarial Purification for Robust Deep MRI Reconstruction" submitted to IEEE ICASSP 2024.

  • 08/17/23: Received a Department of Energy (DOE) award for a project (am Co-PI) on "Machine Learning for Time Projection Chambers at FRIB".  Announced awards can be seen here.

  • 08/15/23: Work on “Scatter Removal in Dynamic X-Ray Tomography using Learned Robust Features“ presented at Optica Imaging Congress, 2023. Work with PhD student Siddhant Gautam. The work was a best student paper award finalist.

  • 07/26/23: Special Session proposal on "Robust Reconstruction Methods in Computational Imaging" accepted to IEEE ICASSP 2024.

  • 07/18/23: Presented work on "Unifying Supervised and Unsupervised Methods for Low-dose CT Reconstruction: a General Framework" at the Fully3D Conference, 2023.

  • 07/17/23: Paper on "Multi-layer Clustering-based Residual Sparsifying Transform for Low-dose CT Image Reconstruction" accepted for publication in Medical Physics.

  • 07/16/23: Paper on "Robust Deep Image Recovery From Sparsely Corrupted and Sub-Sampled Measurements" was submitted to IEEE CAMSAP 2023.

  • 07/09/23: Virtually presented in tutorial on "Machine Learning for Image and Multimedia Reconstruction: From Sparse Modeling to Deep Neural Networks" at IEEE International Conference on Multimedia and Expo (ICME) 2023.

  • 06/18/23: Special Session proposal on "Learning and Optimization for Computational Imaging" accepted at IEEE CAMSAP 2023.

  • 06/14/23: Abstract on "Measuring the effectiveness of mantra-based meditation using EEG data analysis" submitted to Neuroscience 2023.

  • 06/12/23: Paper on "Learning Sparsity-Promoting Regularizers using Bilevel Optimization" accepted for publication in the SIAM Journal on Imaging Sciences.  (arXiv version)

  • 04/19/23: Led two-part tutorial at the IEEE International Symposium on Biomedical Imaging (ISBI) on "Recent Advances in Machine Learning for Image Reconstruction: From Sparse Modeling to Deep Networks". My PhD students (Avrajit Ghosh, Gabriel Maliakal, and Shijun Liang) and NTU collaborator Dr. Bihan Wen and his team also contributed/spoke at the tutorial.

  • 04/17/23: Abstracts on "Patient-adaptive and Learned MRI Data Undersampling Using Neighborhood Clustering" and "Robust Self-Guided Deep Image Prior" accepted at MMLS, 2023.

  • 04/12/23: Paper on "Scatter Removal in Dynamic X-Ray Tomography using Learned Robust Features" submitted to Radiographic Imaging and Tomography (RadIT), 2023.

  • 04/12/23: Received NSF Award for Research Experiences for Undergraduates (REU) for supporting 4 undergraduate researchers, along with Dr. Rongrong Wang. This will be part of the CISE Medium Award for research on "Taming Deep Unsupervised Representation Learning in Imaging: Theory and Algorithms".

  • 03/31/23: Work on "Unifying Supervised and Unsupervised Methods for Low-dose CT Reconstruction: a General Framework" accepted at the Fully3D Conference, 2023.

  • 02/17/23: Papers on "Robust Self-Guided Deep Image Prior" and "SMUG: Towards Robust MRI Reconstruction by Smoothed Unrolling" accepted for presentation at IEEE ICASSP, 2023.

  • 02/15/23: Abstract on "Unifying Supervised and Unsupervised Methods for Low-dose CT Reconstruction: a General Framework" submitted to the 17th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D Conference), 2023.

  • 02/15/23: Brainwave Science is supporting my team's research on "Using EEG Imaging and Data Analysis to Understand the Effects of Meditation and Its Benefits for Health".

  • 01/24/23: Invited as Area Chair for the IEEE International Conference on Image Processing (ICIP), 2023.

  • 01/23/23: Paper on "Deep Reinforcement Learning based Unrolling Network for MRI Reconstruction" accepted for presentation at the IEEE International Symposium on Biomedical Imaging (ISBI), 2023.

  • 01/03/23: Speaking at the Third Workshop on Seeking Low-Dimensionality in Deep Neural Networks in Abu Dhabi, UAE. My students Avrajit Ghosh (PhD student), Gabriel Maliakal (PhD student), and Evan Bell (undergraduate researcher) will also present their research. 

  • 12/29/22: Tutorial proposal on "Machine Learning for Image and Multimedia Reconstruction: From Sparse Modeling to Deep Neural Networks" has been accepted at IEEE International Conference on Multimedia and Expo 2023. 

  • 12/07/22: Revised version of paper "Learning Sparsity-Promoting Regularizers using Bilevel Optimization" submitted to SIAM Journal on Imaging Sciences.

  • 11/18/22: Tutorial proposal on "Recent Advances in Machine Learning for Image Reconstruction: From Sparse Modeling to Deep Networks" accepted at IEEE International Symposium on Biomedical Imaging (ISBI), 2023.

  • 11/17/22: Gave an invited talk on "Advancing Machine Learning for Imaging: Regularization and Robustness" in the Communications and Signal Processing (CSP) Seminar Series at the University of Michigan.

  • 11/11/22: We organized the 2nd CMSE Data Science Student Conference (DISC) 2022 in the Michigan State University campus. (Website)

  • 11/10/22: Paper on "Deep Reinforcement Learning based Unrolling Network for MRI Reconstruction" submitted to IEEE International Symposium on Biomedical Imaging, 2023.

  • 10/26/22: Papers on "Robust Self-Guided Deep Image Prior" and "SMUG: Towards robust MRI reconstruction by smoothed unrolling" submitted to IEEE International Conference on Acoustics, Speech and Signal Processing, 2023.

  • 10/07/22: Our paper on "Sparse-view Cone Beam CT Reconstruction using Data-consistent Supervised and Adversarial Learning from Scarce Training Data" has been accepted to the IEEE Transactions on Computational Imaging.  (arXiv)

  • 10/05/22: Paper on "Learning Nonlocal Sparse and Low-Rank Models for Image Compressive Sensing" accepted to the IEEE Signal Processing Magazine Special Issue on Physics-Driven Machine Learning for Computational Imaging.  (arXiv)

  • 09/30/22: Gave a talk at the Annual Allerton Conference on Communication, Control, and Computing on "Robust Deep Image Prior for MRI Reconstruction with Partial Guidance". 

  • 09/20/22: Proposal for Special Session on "Unsupervised Deep Learning of Image Priors for Inverse Problems" accepted at ICASSP 2023. 

  • 09/15/22: Received an NIH R21 Award (from NIBIB) for doing research on "Machine Learning-Based Adaptation of Data Sampling and Reconstruction for Efficient Dynamic MRI".

  • 08/31/22: Teaching a new CMSE 890 course on Advanced Topics in Signal Processing. (Course Website)

  • 08/30/22: Invited to present in Mini Symposium on "Recent Advances in Inverse Problems for Computational Imaging’’ at the 2023 SIAM Conference on Computational Science and Engineering. Talk Title: Improving the Robustness of Deep Unrolling-based MRI Reconstruction by Learned Randomized Smoothing. 

  • 07/28/22: Invited to give a talk in the CSP Seminar series at the University of Michigan. Talk date: November 17, 2022.

  • 07/18/22-07/20/22: Visiting Los Alamos National Laboratory for collaborations.

  • 07/15/22: Joined organizing team for the Third Workshop on Seeking Low‑Dimensionality in Deep Neural Networks (SLowDNN) to be held from January 3-7, 2023.  (SLowDNN Website)

  • 07/13/22: Revised version of paper "Sparse-view Cone Beam CT Reconstruction using Data-consistent Supervised and Adversarial Learning from Scarce Training Data" submitted to IEEE Transactions on Computational Imaging. 

  • 06/30/22: Paper titled "Learning Sparsity-Promoting Regularizers using Bilevel Optimization" submitted to the SIAM Journal on Imaging Sciences.  (arXiv)

  • 06/20/22: Co-authored paper on "RePnP: Plug-and-Play with Deep Reinforcement Learning Prior for Robust Image Restoration" accepted to IEEE ICIP 2022. Work with Bihan Wen and team at NTU, Singapore.

  • 06/15/22: Moderating session on "Deep Learning Assessment" at the CT Meeting 2022.

  • 06/14/22: Invited to give a talk in the Allerton Conference's session on Imaging and Data Science. Talk Title: Robust Deep Image Prior with Partial Guidance.

  • 06/01/22: Paper on "Adaptive Local Neighborhood-based Neural Networks for MR Image Reconstruction from Undersampled Data" posted online.  (arXiv) 

  • 05/26/22: Received an NSF Award from the CISE Directorate for a Medium Project on "Taming Deep Unsupervised Representation Learning in Imaging: Theory and Algorithms". This will be work between MSU and UMich (Prof. Qing Qu).

  • 04/14/22: Giving an invited talk in the ECE Seminar Series at MSU.

  • 03/29/22 - 03/30/22: Chaired sessions on deep learning and image acquisition & reconstruction at ISBI 2022 in Kolkata, India.

  • 03/29/22: Presenting talk on "LONDN-MRI: Adaptive Local Neighborhood-based Networks for MR Image Reconstruction from Undersampled Data" at IEEE ISBI 2022 in Kolkata, India.

  • 03/25/22: Invited talk on "Learning Regularizers for Image Reconstruction" at Bilkent University, Turkey.

  • 03/23/22: Our work on "Single-pass Object-adaptive Data Undersampling and Reconstruction for MRI" has been accepted to the IEEE Transactions on Computational Imaging. Joint work with postdoc, Dr. Zhishen Huang.  (arXiv)

  • 03/22/22: Paper on "Multi-layer Clustering-based Residual Sparsifying Transform for Low-dose CT Image Reconstruction" submitted to Medical Physics.  (arXiv)

  • 03/18/22: Abstract on "Combining Deep Learning and Adaptive Sparse Modeling for Low-dose CT Reconstruction" accepted at the CT Meeting, 2022. Work with Zhishen Huang (MSU CMSE Postdoc) and UM-SJTU Joint Institute collaborators,  Ling Chen and Yong Long.  (arXiv)

  • 03/18/22: Abstract on "Comparing one-step and two-step scatter correction and density reconstruction in X-ray CT" accepted at the CT Meeting, 2022. Work with Alexander Sietsema (MSU Undergraduate Student) and Los Alamos National Laboratory collaborators (Michael McCann and Marc Klasky).  (arXiv)

  • 03/15/22: Paper on "Physics-Driven Learning of Wasserstein GAN for Density Reconstruction in Dynamic Tomography " accepted for publication in Applied Optics. Work with Dr. Zhishen Huang (MSU CMSE Postdoc) and Los Alamos National Laboratory collaborators (Marc Klasky and Trevor Wilcox).

  • 03/15/22: Paper on "Learning Nonlocal Sparse and Low-Rank Models for Image Compressive Sensing" submitted to the IEEE Signal Processing Magazine Special Issue on Physics-Driven Machine Learning for Computational Imaging.  (arXiv)

  • 03/15/22: Serving as Area Chair for the IEEE International Conference on Image Processing (ICIP), 2022.

  • 02/28/22: Recent works (this and this) from ongoing collaborations with Nuclear Astrophysics team at MSU.

  • 02/25/22: Co-authored paper on "RePnP: Plug-and-Play with Deep Reinforcement Learning Prior for Robust Image Restoration" submitted to IEEE ICIP 2022.

  • 02/05/22: Abstract on "Optimized Parallel Combination of Deep Networks and Sparsity Regularization for MR Image Reconstruction (OPCoNS) " accepted at ISMRM 2022. Work with Avrajit Ghosh (MSU CMSE PhD student), Shijun Liang (MSU BME PhD student), and Anish Lahiri (UM postdoc).

  • 01/26/22: Paper on "Sparse-view Cone Beam CT Reconstruction using Data-consistent Supervised and Adversarial Learning from Scarce Training Data" submitted to the IEEE Transactions on Computational Imaging.  (arXiv)

  • 01/24/22: Abstracts on "Combining Deep Learning and Adaptive Sparse Modeling for Low-dose CT Reconstruction" and "Comparing one-step and two-step scatter correction and density reconstruction in X-ray CT" submitted to the CT Meeting, 2022.

  • 01/24/22: Presented talk on "Learning from hydrodynamics simulations with mass constraints for density reconstruction in dynamic tomography" at MLSI 2022.

  • 01/21/22: Our paper on "Bilevel learning of  l1 regularizers with closed-form gradients (BLORC)" has been accepted at ICASSP 2022. Work with Avrajit Ghosh (MSU CMSE PhD student) and Dr. Michael McCann (LANL).  (arXiv)

  • 01/09/22: White paper on "Learning Nonlocal Sparse and Low-Rank Models for Image Compressive Sensing" accepted in the Special Issue on Physics-Driven Machine Learning for Computational Imaging in the IEEE Signal Processing Magazine.

  • 01/07/22: Our paper titled "LONDN-MRI: Adaptive Local Neighborhood-based Networks for MR Image Reconstruction from Undersampled Data" has been accepted to ISBI 2022. Work with Shijun Liang (MSU BME PhD student), Ashwin Sreevatsa (Undergraduate summer intern in 2021), and Anish Lahiri (UM postdoc).

  • 12/08/21: Gave an invited talk in the "From Cells to Galaxies – Exploring the Synergies between Radio Astronomy and Medical Imaging" Virtual Speaker Series on "Machine Learning for Medical Image Reconstruction".  

  • 12/03/21: Paper on "Exploiting Non-Local Priors via Self-Convolution For Highly-Efficient Image Restoration" accepted to the IEEE Transactions on Image Processing.

  • 11/23/21: Gave a talk at the “Seeking Low Dimensionality in Deep Neural Networks” (SLowDNN) Workshop titled "Learning Regularizers for Inverse Problems".

  • 11/17/21: Paper on "Single-pass Object-adaptive Data Undersampling and Reconstruction for MRI" submitted to the IEEE Transactions on Computational Imaging.  (arXiv) 

  • 11/17/21: Abstracts on "Learning from hydrodynamics simulations with mass constraints for density reconstruction in dynamic tomography" and "Limited-view cone beam CT reconstruction using 3D patch-based supervised and adversarial learning: Validation using hydrodynamic simulations and experimental tomographic data" accepted for oral presentations at the Machine Learning for Scientific Imaging 2022 Conference. 

  • 11/10/21: Abstract on "Optimized Parallel Combination of Deep Networks and Sparsity Regularization for MR Image Reconstruction (OPCoNS)" submitted to ISMRM 2022.

  • 11/02/21: Presented a talk on "Descattering and Density Reconstruction in Polyenergetic X-Ray Tomography with Locally Learned Models" at the Asilomar Conference on Signals, Systems, and Computers, 2021.

  • 10/29/21:  Invited to give a talk at the third Deep Reconstruction Workshop during November 14-15, 2021. Talk title: Improving Deep Learning for MR Image Reconstruction by Exploiting Structured Patient-Adaptive Representations.

  • 10/20/21: Paper on "Physics-Driven Learning of Wasserstein GAN for Density Reconstruction in Dynamic Tomography" submitted to Applied Optics.  (arXiv)

  • 10/15/21: Paper on "LONDN-MRI: Adaptive Local Neighborhood-based Networks for MR Image Reconstruction from Undersampled Data" submitted to ISBI 2022.

  • 10/06/21: Paper on "Bilevel learning of  l1 regularizers with closed-form gradients (BLORC)" submitted to ICASSP 2022.

  • 10/04/21: Extended abstract on "Adaptive Local Neighborhood-based Networks for MR Image Reconstruction from Undersampled Data"  accepted to the 2nd Learning for Computational Imaging Workshop at ICCV 2021.

  • 10/02/21:  Revised version of "Exploiting Non-Local Priors via Self-Convolution For Highly-Efficient Image Restoration" submitted to the IEEE Transactions on Image Processing.

  • 09/18/21: Abstracts on "Learning from Hydrodynamics Simulations with Mass Constraints for Density Reconstruction in Dynamic Tomography" and "Limited-view Cone Beam CT reconstruction using 3D Patch-based Supervised and Adversarial Learning" submitted to Machine Learning for Scientific Imaging 2022.

  • 08/16/21: Paper on "Local Models for Scatter Estimation and Descattering in Polyenergetic X-Ray Tomography" accepted for publication in Optics Express.  Available here.

  • 08/02/21: Speaking in the online workshop on “Seeking Low Dimensionality in Deep Neural Networks” (SLowDNN) to be held during November, 2021.  (Website)

  • 07/06/21: Invited to give a talk at the From Cells to Galaxies virtual speaker series.

  • 06/28/21: Paper on "Unified Supervised-Unsupervised (SUPER) Learning for X-ray CT Image Reconstruction" accepted for publication in the IEEE Transactions on Medical Imaging.

  • 06/24/21: Paper on "Blind Primed Supervised (BLIPS) Learning for MR Image Reconstruction" accepted to the IEEE Transactions on Medical Imaging.

  • 06/20/21: Will be a Guest Editor for an IEEE Signal Processing Magazine Special Issue on "Physics-Driven Machine Learning for Computational Imaging". 

  • 05/24/21: Paper on "Exploiting Non-Local Priors via Self-Convolution For Highly-Efficient Image Restoration" submitted to the IEEE Transactions on Image Processing.  (arXiv)

  • 05/23/21: Revised version of  "Unified Supervised-Unsupervised (SUPER) Learning for X-ray CT Image Reconstruction" accepted pending minor revision at the IEEE Transactions on Medical Imaging.  (arXiv)

  • 05/20/21: Paper on "LABMAT: Learned Feature-domain Block Matching for Image Restoration" accepted at ICIP 2021. Work with Shijun Liang (MSU BME PhD student), Berk Iskender (intern during summer 2020), and Bihan Wen (NTU, Singapore).

  • 05/20/21: Paper on "Multi-layer Residual Sparsifying Transform (MARS) Model for Low-dose CT Image Reconstruction" accepted for publication in Medical Physics.  (arXiv)

  • 05/13/21: Revised version of "Blind Primed Supervised (BLIPS) Learning for MR Image Reconstruction" accepted pending minor revision at the IEEE Transactions on Medical Imaging.

  • 04/28/21: Our papers on "Limited-view Cone Beam CT reconstruction using 3D Patch-based Supervised and Adversarial Learning" and "Descattering and Reconstruction in Multimaterial Polyenergetic X-Ray Tomography Using Local Scatter Models" have been accepted for oral presentation at the OSA Imaging and Applied Optics Congress, 2021.

  • 04/08/21: Proposal for a 2nd Workshop on "Learning for Computational Imaging: Sensing, Reconstruction, and Analysis" was accepted at the International Conference on Computer Vision (ICCV), 2021.

  • 04/06/21: Revised version of "Blind Primed Supervised (BLIPS) Learning for MR Image Reconstruction" submitted to the IEEE Transactions on Medical Imaging.  (arXiv)

  • 04/05/21: Gave a virtual invited talk in the LANSCE Futures Spring 2021 Workshop Series, Workshop on Dynamic Radiography, held at Los Alamos National Laboratory. Talk title: Limited-View Cone Beam CT Reconstructions using 3D Patch Based Supervised and Unsupervised Adversarial Learning.

  • 03/31/21: Two abstracts on "Two-layer Clustering-based Sparsifying Transform Learning for Low-dose CT Reconstruction" and "Learning Overcomplete or Undercomplete Models in Clustering-based Low-dose CT Reconstruction" accepted for oral presentations at the International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D), 2021. Joint work with Xikai Yang, Ling Chen, and Yong Long at the UM-SJTU Joint Institute.

  • 03/17/21: Submitted Chapter on "Model-based Reconstruction with Learning: From Unsupervised to Supervised and Beyond" to be published in the book on Deep Learning for Biomedical Image Reconstruction, edited by Jong Chul Ye, Yonina Eldar, and Michael Unser.  (arXiv)

  • 03/14/21: Special session proposal on "Model-based deep learning for inverse problems in imaging" accepted at the Asilomar Conference on Signals, Systems, and Computers, 2021. Organizing with Gregory Ongie (Marquette University) and Zhishen Huang (Postdoc).

  • 03/02/21: Two papers titled "Limited-view Cone Beam CT reconstruction using 3D Patch-based Supervised and Adversarial Learning" and "Descattering and Reconstruction in Multimaterial Polyenergetic X-Ray Tomography Using Local Scatter Models" submitted to the OSA Digital Holography and Three-Dimensional Imaging Topical Meeting, 2021.

  • 02/24/21: Abstract on "Blind Primed Supervised (BLIPS) Learning for MR Image Reconstruction" accepted for presentation in an Oral Scientific session at ISMRM 2021. Work with Anish Lahiri, Guanhua Wang, and Jeff Fessler at UM.

  • 02/22/21: Received certificate of IEEE TMI Distinguished Reviewer in recognition of contributions to IEEE Transactions on Medical Imaging from 2018 through 2020.

  • 01/29/21: Two co-authored papers on "Self-Convolution: A Highly-Efficient Operator for Non-Local Image Restoration" and "Learning Sparsifying Transforms for Image Reconstruction in Electrical Impedence Tomography" accepted for presentation at IEEE ICASSP 2021. Works with Bihan Wen and team at NTU, Singapore.

  • 01/13/21: Paper on "LABMAT: Learned Feature-domain Block Matching for Image Restoration" submitted to ICIP 2021.

  • 01/06/21: Invited to give a talk in the symposium on Recent Advances in CT at AAPM 2021.

  • 12/11/20: Paper on "Descattering and Density Reconstruction in Polyenergetic Tomography using Locally-Learned Models" submitted to the IEEE Transactions on Computational Imaging.  (arXiv)

  • 12/01/20: Paper on "Blind Primed Supervised (BLIPS) Learning for MR Image Reconstruction" submitted to the IEEE Transactions on Medical Imaging.

  • 11/19/20: Gave a talk on my research at Purdue University.

  • 11/09/20: The ARS Foundation will fund our group for developing methods for fast magnetic resonance imaging (MRI).

  • 11/01/20: Elected as member of the IEEE Computational Imaging (CI) Technical Committee for 2021-2023.

  • 10/26/20: Paper on "Two-Layer Clustering-Based Sparsifying Transform Learning for Low-dose CT Reconstruction" submitted to ISBI 2021.  (arXiv)

  • 10/22/20: Co-authored paper on "Learning Sparsifying Transforms for Image Reconstruction in Electrical Impedence Tomography" submitted to ICASSP 2021.

  • 10/10/20: Paper on "Multi-layer Residual Sparsifying Transform (MARS) Model for Low-dose CT Image Reconstruction" submitted to the journal Medical Physics.  (arXiv)

  • 10/06/20: Was elevated to IEEE Senior member.

  • 10/02/20: Paper on "Unified Supervised-Unsupervised (SUPER) Learning for X-ray CT Image Reconstruction" submitted to the IEEE Transactions on Medical Imaging.  (arXiv)

  • 09/02/20: Teaching a new CMSE 890 course on Computational Image Formation and Enhancement. (Course Website)

  • 08/25/20: Gave a Talk in the SPACE webinar series titled "From Transform Learning to Deep Learning and Beyond for Imaging". See YouTube version here.

  • 07/07/20: Postdoc Dr. Michael McCann presented talk on "Learning Regularizers for Image Reconstruction" at the SIAM Conference on Imaging Science (IS20). Abstract here.

  • 06/24/20: Paper on "Self-Convolution: A Highly-Efficient Operator for Non-Local Image Restoration" posted to arXiv.

  • 06/09/20: Paper on "Supervised Learning of Sparsity-Promoting Regularizers for Denoising" posted to arXiv.

  • 05/19/20: Organizing the IEEE SPS Computational Imaging Webinar Series called Signal Processing And Computational imagE formation (SPACE).  (Webinar website)

  • 05/16/20: Received funding from a LANL/DOE award for "Methods for Inverse Problems: Models, Algorithms, Machine Learning, and Theory".

  • 04/06/20: Code for "DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering" posted to Github. See here.

  • 03/20/20: Our paper "Two-layer Residual Sparsifying Transform Learning for Image Reconstruction" received a Best Paper Award Finalist certificate from the IEEE International Symposium on Biomedical Imaging (ISBI), 2020.

  • 03/02/20: Paper on "Learned Multi-layer Residual Sparsifying Transform Model for Low-dose CT Reconstruction" accepted for oral presentation at the CT Meeting, 2020.

  • 01/29/20: Abstract on "Combining Supervised and semi-Blind Dictionary (Super-BReD) Learning for MRI Reconstruction" accepted at the ISMRM Annual Meeting, 2020. Congratulations to Anish Lahiri at UM.

  • 01/21/20: will be organizing a special session on "Algorithms, Learning, and Theory for Computational Imaging" together with postdoc Dr. Michael McCann at the Asilomar Conference on Signals, Systems, and Computers, during November 1-4, 2020.

  • 01/14/20: Invited to give a talk at the From Cells to Galaxies conference to be held in Santa Fe, NM. (Conference postponed)

  • 01/06/20: Paper on "Two-layer Residual Sparsifying Transform Learning for Image Reconstruction" accepted to the IEEE International Symposium on Biomedical Imaging (ISBI), 2020.  (arXiv)

  • 12/10/19-12/24/19: Visiting Los Alamos National Laboratory with Postdoc Michael McCann for collaborations.

  • 11/11/19: Gave a talk titled "From Transform Learning to Deep Learning and Beyond for Imaging" in the Department of Electrical Engineering at the Indian Institute of Technology Madras, India.

  • 11/11/19: Serving as Area Chair for the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020.

  • 11/04/19: Was a co-organizer for the special session on "Modeling, Optimization, and Machine Learning for Computational Imaging" at the  Asilomar Conference on Signals, Systems, and Computers, held in Pacific Grove, California.

  • 10/29/19-11/02/19: Attended the International Conference on Computer Vision (ICCV) in Seoul, South Korea, and co-organized the ICCV Workshop on Learning for Computational Imaging (LCI). The paper "SUPER Learning: A Supervised-Unsupervised Framework for Low-Dose CT Image Reconstruction" was presented in the oral session and "Two-layer Residual Sparsifying Transform Learning for Image Reconstruction" was presented as poster at the LCI Workshop.

  • 10/20/19: Our paper "Transform Learning for Magnetic Resonance Image Reconstruction: From Model-based Learning to Building Neural Networks" has been accepted to IEEE Signal Processing Magazine, Special Issue on Computational MRI: Compressed Sensing and Beyond.

  • 10/14/19-10/18/19: Co-organized the IMA Workshop on Computational Imaging in Minneapolis, Minnesota. The following posters were presented by the group and collaborators during the poster session (link).
    1) Title: Learning Regularization Filters for Image Reconstruction, Presenter: Michael McCann (Michigan State University)
    2) Title: Physics Based Machine Learning for Radiographic Reconstructions, Presenter: Marc Klasky (Los Alamos National Lab)
    3) Title: A Supervised-Unsupervised (SUPER) Learning Framework for Image Reconstruction, Presenter: Saiprasad Ravishankar
    4) Title: Sparse Representation Learning: A Comparative Study, Presenter: Akanksha Sunalkar (University of Michigan)
    5) Title: Multi-layer Residual Sparsifying Transform Learning Model for Low-dose CT Image Reconstruction, Presenter: Xikai Yang (University of Michigan - Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University)
    6) Title: Combining Supervised and Semi-Blind Residual Dictionary (Super-BReD) Learning, Presenter: Anish Lahiri (University of Michigan)

  • 10/01/19: Paper on "Ranging and Light Field Imaging with Transparent Photodetectors" has been accepted to Nature Photonics.

  • 09/28/19: Revised version of paper titled "DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering" has been accepted to the IEEE Transactions on Medical Imaging.

  • 09/20/19: Revised version of "Ranging and Light Field Imaging with Transparent Photodetectors" submitted to Nature Photonics.

  • 09/04/19: Our extended abstract on "Two-layer Residual Sparsifying Transform Learning for Image Reconstruction" has been accepted for presentation at the International Conference on Computer Vision, Workshop on Learning for Computational Imaging, 2019.

  • 09/01/19: Our paper "Analysis of Fast Structured Dictionary Learning" has been accepted to Information and Inference: A Journal of the IMA.

  • 08/22/19: Our paper "SUPER Learning: A Supervised-Unsupervised Framework for Low-Dose CT Image Reconstruction" has been accepted to the International Conference on Computer Vision, Workshop on Learning for Computational Imaging, 2019.

  • 08/07/19: Our paper "Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning" has been accepted to the Proceedings of the IEEE: Special Issue on Biomedical Imaging and Analysis in the Age of Sparsity, Big Data, and Deep Learning.  (arXiv)

  • 08/06/19: Revised version of "Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning" submitted to the Proceedings of the IEEE.

  • 08/01/19: Our paper "SPULTRA: Low-Dose CT Image Reconstruction with Joint Statistical and Learned Image Models" has been accepted to the IEEE Transactions on Medical Imaging.  (arXiv)

  • 07/31/19: Revised version of "SPULTRA: Low-Dose CT Image Reconstruction with Joint Statistical and Learned Image Models" submitted to the IEEE Transactions on Medical Imaging. 

  • 07/15/19-08/08/19: Visiting Los Alamos National Laboratory for collaborations.

  • 07/12/19: Revised version of "Ranging and Light Field Imaging with Transparent Photodetectors" submitted to Nature Photonics.

  • 07/04/19: Revised version of "Transform Learning for Magnetic Resonance Image Reconstruction: From Model-based Learning to Building Neural Networks" submitted to the IEEE Signal Processing Magazine. 

  • 06/25/19: Revised version of "Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning" submitted to the Proceedings of the IEEE.  (arXiv)

  • 06/07/19: Revised version of "Analysis of Fast Structured Dictionary Learning" submitted to Information and Inference: A Journal of the IMA.  (arXiv)

  • 05/31/19: Paper on "Multi-layer Residual Sparsifying Transform Learning for Image Reconstruction" submitted to the IEEE Signal Processing Letters.  (arXiv)

  • 05/15/19: Revised version of "SPULTRA: Low-Dose CT Image Reconstruction with Joint Statistical and Learned Image Models" submitted to the IEEE Transactions on Medical Imaging.  (arXiv)

  • 04/08/19: Our paper "Online Adaptive Image Reconstruction (OnAIR) Using Dictionary Models" has been accepted to the IEEE Transactions on Computational Imaging.

  • 04/05/19: Proposal for a Workshop on "Learning for Computational Imaging: Sensing, Reconstruction, and Analysis" was accepted at the International Conference on Computer Vision 2019. The workshop website is here.

  • 04/04/19: Paper on "Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning" submitted to the Proceedings of the IEEE: Special Issue on Biomedical Imaging & Analysis in the Age of Sparsity, Big Data and Deep Learning. (arXiv)

  • 03/23/19: Paper on "Transform Learning for Magnetic Resonance Image Reconstruction: From Model-based Learning to Building Neural Networks" submitted to the IEEE Signal Processing Magazine, Special Issue on Computational MRI: Compressed Sensing and Beyond.  (arXiv)

  • 02/26/19: Abstract on "Image-domain Multi-Material Decomposition Using a Union of Cross-Material Models" accepted for oral presentation at the Fully 3D 2019 conference.

  • 02/21/19: Invited to organize a special session at the Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, California, during November 3-6, 2019. Session Title: Modeling, Optimization, and Machine Learning for Computational Imaging.

  • 02/18/19: Serving as Area Chair for the IEEE International Conference on Image Processing (ICIP), 2019.

  • 02/10/19: Proposal for a 5-day Workshop on Computational Imaging was accepted by the IMA. The workshop website is here.

  • 02/06/19: Chapter on “Dictionary Methods for Compressed Sensing: Framework for Microscopy,” published as part of the book titled "Statistical Methods for Materials Science: The Data Science of Microstructure Characterization".

  • 01/23/19: Revised version of "Online Adaptive Image Reconstruction (OnAIR) Using Dictionary Models" submitted to the IEEE Transactions on Computational Imaging.  (arXiv)

  • 01/10/19: Four page abstract on "Image-domain Multi-Material Decomposition Using a Union of Cross-Material Models" submitted to the Fully 3D 2019 conference.

  • 12/29/18: Paper on "DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering" submitted to the IEEE Transactions on Medical Imaging.  (arXiv)

  • 11/28/18: Presenting paper (talk) in the Biomedical Image Processing Session at IEEE GlobalSIP 2018.

  • 10/15/18: Serving as Associate Editor for the IEEE International Symposium on Biomedical Imaging (ISBI), 2019.

  • 10/07/18: Paper on "Learning Multi-Layer Transform Models" to appear in the Proceedings of the Annual Allerton Conference on Communication, Control, and Computing, 2018.  (arXiv)

  • 10/03/18: Invited talk on "Learning Multi-Layer Transform Models" in the Computational Imaging and Inverse Problems Session at the 2018 56th Annual Allerton Conference on Communication, Control, and Computing.

  • 09/27/18: Paper on "Ranging and Light Field Imaging with Transparent Photodetectors" submitted to Nature.

  • 09/13/18: Gave a talk on "Data-Driven Models and Approaches for Signal Processing and Imaging" in Los Alamos National Laboratory.

  • 09/10/18: Paper on "Learned Mixed Material Models for Efficient Clustering Based Dual-Energy CT Image Decomposition" accepted to IEEE GlobalSIP, 2018.

  • 09/06/18: Paper on "Online Adaptive Image Reconstruction (OnAIR) Using Dictionary Models" submitted to the IEEE Transactions on Computational Imaging.  (arXiv)

  • 08/27/18: Paper on "SPULTRA: Low-Dose CT Image Reconstruction with Joint Statistical and Learned Image Models" submitted to IEEE Transactions on Medical Imaging.  (arXiv)

  • 08/14/18: Gave a talk in the Department of Electrical Engineering at the Indian Institute of Technology Madras, India.

  • 07/23/18: Our paper "VIDOSAT: High-dimensional Sparsifying Transform Learning for Online Video Denoising" has been accepted to the IEEE Transactions on Image Processing.

  • 07/22/18-07/30/18: Visiting the Korea Advanced Institute of Science and Technology in Daejeon, South Korea.

  • 06/30/18: Paper on "Learned Mixed Material Models for Efficient Clustering Based Dual-Energy CT Image Decomposition" submitted to IEEE GlobalSIP, 2018.

  • 06/26/18-06/28/18: Visiting Tsinghua University in Beijing, China and giving an invited talk.

  • 06/24/18-06/26/18: Visiting Xiamen University in Xiamen, China and giving invited lectures.

  • 06/15/18-06/30/18: Visiting the University of Michigan - Shanghai Jiao Tong University Joint Institute (UM-SJTU JI) in Shanghai, China for invited talk and collaborations.

  • 06/02/18: Revised version of "VIDOSAT: High-dimensional Sparsifying Transform Learning for Online Video Denoising" submitted to the IEEE Transactions on Image Processing. Available here.

  • 05/31/18: Paper titled "Analysis of Fast Structured Dictionary Learning" submitted to Information and Inference: A Journal of the IMA. See arXiv version here.

  • 04/07/18: The paper "Image-domain Material Decomposition using Data-driven Sparsity Models for Dual-energy CT" with Zhipeng Li, Yong Long, and Jeff Fessler has won the student paper competition at the IEEE International Symposium on Biomedical Imaging (ISBI), 2018.

  • 04/05/18:  Our paper "PWLS-ULTRA: An Efficient Clustering and Learning-Based Approach for Low-Dose 3D CT Image Reconstruction" has been accepted to the IEEE Transactions on Medical Imaging Special Issue on Machine Learning for Image Reconstruction.

  • 03/15/18: Invited talk in the Department of Computational Mathematics, Science and Engineering and the Biomedical Engineering Department at Michigan State University.

  • 03/02/18: Revised version of "PWLS-ULTRA: An Efficient Clustering and Learning-Based Approach for Low-Dose 3D CT Image Reconstruction" submitted to the IEEE Transactions on Medical Imaging. (arXiv)

  • 02/28/18: Invited talk in the Department of Electrical Engineering at the University of Notre Dame.

  • 02/20/18: Invited talk in the Department of Electrical and Computer Engineering at The Ohio State University.

  • 01/24/18: Invited talk in the BrainMap seminar series at the Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital.

  • 01/15/18: Invited work submitted to the 2018 Information Theory and Applications Workshop, San Diego. Title: Analysis of Fast Alternating Minimization for Structured Dictionary Learning. Authors: Saiprasad Ravishankar, Anna Ma, Deanna Needell.

  • 12/27/17: Two papers accepted at the IEEE International Symposium on Biomedical Imaging, 2018.

  • 11/14/17: Elected to serve on the IEEE Computational Imaging Special Interest Group (CI SIG) for a 3-year term starting Jan 2018.

  • 11/03/17: Invited talk in the Department of Electrical and Computer Engineering at Iowa State University (Link).

  • 11/01/17: Presented invited paper (talk) on "Physics-Driven Deep Training of Dictionary-Based Algorithms for MR Image Reconstruction,” in the session on "Computational Imaging" at the Asilomar Conference on Signals, Systems, and Computers.

  • 10/31/17: Presented paper on "Efficient Online Dictionary Adaptation and Image Reconstruction for Dynamic MRI" at the Asilomar Conference on Signals, Systems, and Computers.

  • 10/27/17: International PCT Patent Application filed on “Method of Dynamic Radiographic Imaging using Singular Value Decomposition”.

  • 10/27/17: Invited talk in the Department of Electrical Engineering and Computer Sciences at UC Berkeley.

  • 10/23/17: Paper submitted to ISBI 2018 on "Deep Dictionary-Transform Learning for Image Reconstruction".

  • 10/16/17: Paper submitted to ISBI 2018 on "Image-domain Material Decomposition using Data-driven Sparsity Models for Dual-energy CT".

  • 10/12/17: Serving as Associate Editor for the IEEE International Symposium on Biomedical Imaging (ISBI), 2018.

  • 10/11/17: Proposal for special session on "Smart Imaging Systems" accepted at the IEEE International Symposium on Biomedical Imaging (ISBI), 2018, to be held in Washington, D.C.

  • 10/02/17: Paper on "VIDOSAT: High-dimensional Sparsifying Transform Learning for Online Video Denoising," submitted to IEEE Transactions on Image Processing.

  • 09/29/17: Presented a talk on "Powering the Future of Imaging and Signal Processing with Data-Driven Systems" in the Department of Electrical Engineering at the University of Southern California.

  • 09/27/17: Organizer for special session on "Machine Learning for Computational Imaging" at the IEEE International Workshop on Machine Learning for Signal Processing, 2017.

  • 09/25/17: Co-authored paper nominated for the best student paper award at the IEEE International Workshop on Machine Learning for Signal Processing, 2017.

  • 09/19/17: Paper on "Online Data-driven Dynamic Image Restoration using DINO-KAT Models” presented at the IEEE International Conference on Image Processing.

  • 08/15/17: United States Patent 9734601 granted. Title: Highly Accelerated Imaging and Image Reconstruction using Adaptive Sparsifying Transforms.

  • 06/28/17: Chaired session on "Optical Coherence Tomography" at the OSA Imaging and Applied Optics Congress.

  • 06/28/17: Gave Invited talk on "Data-driven Models and Approaches for Imaging and Signal Processing" at the Optical Society of America topical meeting on Mathematics in Imaging.

© 2015 Saiprasad Ravishankar

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