The Information Processing and Algorithms Laboratory (iPAL) is directed by Prof. Vishal Monga. Graduate research in iPAL focuses on convex and non-convex optimization methods in learning, vision and signal processing. Our particular interest is in estimation frameworks where domain inspired prior knowledge is captured. This invariably leads to challenging optimization problems for which we develop new tractable solutions that facilitate a favorable performance-complexity trade-off.
We propose an unrolling technique that breaks the trade-off between retaining algorithm properties while simultaneously enhancing performance.
An AL method that benefits from a hierarchical approach to define a multiview data distribution, which takes into account the different "sample" definitions jointly, hence able to select the most impactful segmentation pixels with comprehensive understanding.
We develop two novel deep learning classification models with novel attention regularization for CT hydrocephalus classification and infection diagnosis (a 2D version and a 2D/3D hybrid version).
Convex Optimization for Adaptive Radar.
For more details about iPAL research in radar systems click here.
Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing by V. Monga, Y. Li and Y. Eldar.
The article can be found here.
This book discusses imaging science and provides tools for solving image processing and computer vision problems using convex optimization methods. Throughout the handbook, the authors introduce topics on the most key aspects of image acquisition and processing that are based on the formulation and solution of novel optimization problems.
Academia
Prof. Yonina Eldar (Weizmann Institute, Israel) and Prof. Trac Tran (Johns Hopkins University)
Government Labs
Dr. Muralidhar Rangaswamy (US Air Force Research Laboratory)
Dr. Nasser Nasrabadi (US Army Research Laboratory, now West Virginia University)
Industry
Dr. Raja Bala (Prev. Xerox PARC, Now Amazon)
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