Dr. Saiprasad Ravishankar received the B.Tech. degree in Electrical Engineering from the Indian Institute of Technology (IIT) Madras, in 2008. He received his M.S. and Ph.D. degrees in Electrical and Computer Engineering, in 2010 and 2014 respectively, from the University of Illinois at Urbana-Champaign. After his Ph.D., Dr. Ravishankar was an Adjunct Lecturer in the Department of Electrical and Computer Engineering, and a Postdoctoral Research Associate in the Coordinated Science Laboratory at the University of Illinois. From August 2015, he was a Research Fellow in the Department of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor. He was a Postdoc Research Associate in the Theoretical Division at Los Alamos National Laboratory from August 2018 to February 2019. He is currently an Assistant Professor in the Departments of Computational Mathematics, Science and Engineering, and Biomedical Engineering at Michigan State University (MSU).
Dr. Ravishankar's research interests include signal and image processing, computational and biomedical imaging, data-driven systems, machine learning, signal modeling, inverse problems, compressed sensing, dictionary learning, data science, image analysis, and large-scale data processing and optimization.
• Signal and image processing
• Computational and biomedical imaging
• Data-driven systems
• Machine learning
• Signal modeling
• Inverse problems
• Compressed sensing
• Dictionary learning
• Data science
• Image analysis
• Large-scale data processing and optimization
 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.
 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)
 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.
 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,” IEEE Transactions on Medical Imaging, vol. 36, no. 5, pp. 1116-1128, May 2017.
 S. Ravishankar, R. R. Nadakuditi, and J. A. Fessler, “Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its Application to Inverse Problems,” IEEE Transactions on Computational Imaging, vol. 3, no. 4, pp. 694-709, Dec. 2017.
 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,” IEEE Transactions on Medical Imaging, vol. 37, no. 6, pp. 1498-1510, June 2018.
Prof. Ravishankar leads research at the intersection of signal processing, computational imaging, machine learning, large-scale optimization, theory, and applications. The postdoc will work on developing new models, machine learning methods, algorithms, and theory for imaging (MRI, CT, and other computational imaging systems) and signal processing applications. Candidates should have (or expect to have shortly) a PhD degree in Electrical or Computer Engineering, Biomedical Engineering, Mathematics, or related fields, and should have strong programming skills.