Abhishek Shreekant Bhandiwad

Biography

Abhishek Shreekant Bhandiwad received the Bachelor of Technology degree from the Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science (BITS), Pilani, India, in 2020. From 2020 to 2022, he was a Design Verification Engineer at Analog Devices, Bangalore, where he worked on multi-gigahertz transceiver projects. He also interned at NVIDIA, Bangalore, in the HSHUB Architecture team in 2020. He received the Master of Technology degree in Signal Processing from the Indian Institute of Science (IISc), Bangalore, India, in 2024. Since 2024, he has been pursuing the Ph.D. degree at the Spectrum Lab. His research interests include optimization, inverse problems in computational imaging, and the application of deep learning in computational imaging.

Publications

2026

ICASSP 26

Sparse recovery using tight frames and minimax concave penalty

K. K. R. Nareddy , A. S. Bhandiwad, A. J. Kamath, and C. S. Seelamantula

In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2026

Abstract DOI Download .bib $@inproceedings{nareddy2026sparse, author = {Nareddy, K. K. R. and Bhandiwad, A. S. and Kamath, A. J. and Seelamantula, C. S.}, booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, title = {Sparse recovery using tight frames and minimax concave penalty}, year = {2026}, pages = {326-330}, keywords = {Radio broadcasting;Frequency modulation;Pixel;Modulation;Radio broadcasting;Protocols;Frequency modulation;HTTP;Digital images;Learning (artificial intelligence);Sparse recovery;tight frames;back-projection loss;minimax concave penalty;nonconvex optimization}, doi = {10.1109/ICASSP55912.2026.11465068} }$
$Sparse signal recovery is an important problem in compressed sensing, signal/image processing, computational imaging and machine learning. The design of the sensing matrix is crucial in compressed sensing. While random Gaussian sensing matrices are relatively easier to construct, it is known that matrices constituting a tight frame provide superior reconstruction performance. We formulate the problem of sparse signal recovery using a new data-fidelity term that effectively "tightens" the sensing matrix, and incorporate the minimax concave penalty (MCP) instead of the ℓ1-norm for promoting sparsity. We carry out the optimization using a proximal gradient method and its Nesterov accelerated counterpart. Simulation results demonstrate that the proposed algorithms result in up to three times faster convergence and superior reconstruction accuracy over benchmark methods.$

2025

ISCS 25
Weakly-Convex Regularization for Magnetic Resonance Image Denoising

A. Prabakar, A. S. Bhandiwad, A. J. Kamath, and C. S. Seelamantula

Apr 2025

Cite Download .bib $@misc{prabakar2025weakly, archiveprefix = {arXiv}, author = {Prabakar, A. and Bhandiwad, A. S. and Kamath, A. J. and Seelamantula, C. S.}, eprint = {2508.14438}, primaryclass = {eess.SP}, title = {Weakly-Convex Regularization for Magnetic Resonance Image Denoising}, url = {https://arxiv.org/abs/2508.14438}, year = {2025} }$ Link
@misc{prabakar2025weakly, archiveprefix = {arXiv}, author = {Prabakar, A. and Bhandiwad, A. S. and Kamath, A. J. and Seelamantula, C. S.}, eprint = {2508.14438}, primaryclass = {eess.SP}, title = {Weakly-Convex Regularization for Magnetic Resonance Image Denoising}, url = {https://arxiv.org/abs/2508.14438}, year = {2025} }
ICASSP 25
On the Design of Weakly-Convex Regularizers for Solving Linear Inverse Problems

A. J. Kamath, A. S. Bhandiwad, and C. S. Seelamantula

In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2025

Cite Download .bib $@inproceedings{kamath2025design, author = {Kamath, A. J. and Bhandiwad, A. S. and Seelamantula, C. S.}, booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, title = {On the Design of Weakly-Convex Regularizers for Solving Linear Inverse Problems}, url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:artPoR2Yc-kC}, year = {2025} }$ Link
@inproceedings{kamath2025design, author = {Kamath, A. J. and Bhandiwad, A. S. and Seelamantula, C. S.}, booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, title = {On the Design of Weakly-Convex Regularizers for Solving Linear Inverse Problems}, url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:artPoR2Yc-kC}, year = {2025} }

2024

ICASSP 24
Variational Analysis of Adversarial Regularization for Solving Inverse Problems

A. S. Bhandiwad, A. J. Kamath, S. Asokan, and C. S. Seelamantula

In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2024

Cite Download .bib $@inproceedings{bhandiwad2024variational, author = {Bhandiwad, A. S. and Kamath, A. J. and Asokan, S. and Seelamantula, C. S.}, booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, title = {Variational Analysis of Adversarial Regularization for Solving Inverse Problems}, url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:GFxP56DSvIMC}, year = {2024} }$ Link
@inproceedings{bhandiwad2024variational, author = {Bhandiwad, A. S. and Kamath, A. J. and Asokan, S. and Seelamantula, C. S.}, booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, title = {Variational Analysis of Adversarial Regularization for Solving Inverse Problems}, url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:GFxP56DSvIMC}, year = {2024} }