Sudhansh Peddabomma

Email -

sudhansh6[at]gmail[dot]com
speddabomma[at]ucsd[dot]edu

Hello! I’m Sudhansh, a first-year Masters in Computer Science student at UC San Diego. I am a graduate of IIT Bombay, where I majored in Computer Science and Engineering with honors and pursued a minor in Entrepreneurship.

I have worked with Prof. Ajit Rajwade on the Likelihood Maximization method for Saturation Compressed Sensing. We are also working on developing algorithms for correction of Permutation Noise in Compressed Sensing. We have submitted a journal paper to Elsevier Signal Processing 2023 based on this work. In addition to this, I have worked with Prof. Marta Betcke on Stochastic Primal Dual algorithms for Tomographic Reconstruction of 3D volumes in Low-Dose conditions, and we have a journal paper in preparation.

Research Interests - Computer Vision, Image Processing, Robotics

CV / Resume

news

Apr 1, 2024	Started Teaching Assistantship for Theory of Computing
Jan 1, 2024	Started Teaching Assistantship for Quantum Cryptography
Nov 6, 2023	Paper on bounds for compressive signal recovery accepted at Signal Processing 2024.
Oct 15, 2023	Joined Cognitive Robotics Lab under the supervision of Prof. Henrik Christensen

latest posts

May 8, 2024	Reinforcement Learning in Nuclear Fusion
May 1, 2024	Scalable Behavior Planning for Autonomous Driving
Apr 24, 2024	Structural Visualization for Neural Networks

selected publications

A likelihood based method for compressive signal recovery under Gaussian and saturation noise

Shuvayan Banerjee, Sudhansh Peddabomma, Radhendushka Srivastava, and Ajit Rajwade

Signal Processing, 2024

Abs PDF

Practical sensing systems have a limited dynamic range, which gives rise to saturation noise in signal acquisition if the signal attains values outside this range at certain locations. Despite rapid advances in compressed sensing theory and systems over the years, the effect of saturation on compressed sensing measurements, especially in the presence of additive Gaussian noise, has often been overlooked in many existing approaches. In this paper, we propose a novel maximum likelihood based estimator for compressive reconstruction in the presence of both Gaussian and saturation noise.