Featured Article in IEEE Signal Processing Magazine: Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing
Dr. Monga Elevated to IEEE Fellow! His contributions to computationally efficient image analysis and restoration have been recognized, including best paper awards from the IEEE, a National Science Foundation CAREER Award, and induction into the National Academy of Inventors.
Publication in IEEE TBME: GLAPAL-H: Global, Local And Parts Aware Learner for Hydrocephalus Infection Diagnosis in Low-Field MRI
Publication in IEEE TCI: Deep, Convergent, Unrolled Half-Quadratic Splitting For Image Deconvolution
NEW! Handbook of Convex Optimization Methods in Imaging Science - A comprehensive guide discussing imaging science through the lens of convex optimization.
Research Experience
Graduate research at iPAL is centered around developing new tractable solutions to challenging optimization problems, aiming for a favorable performance-complexity trade-off.
Background
The Information Processing and Algorithms Laboratory (iPAL), directed by Prof. Vishal Monga, focuses on convex and non-convex optimization methods in learning, vision, and signal processing. It particularly emphasizes estimation frameworks that incorporate domain-inspired prior knowledge.
Miscellany
Research areas include Medical & Computational Imaging, Sparsity Constrained & Robust Time-series Signal Estimation, and Radar Signal Processing.