About Me

Interest: Machine Learning, Image Processing, and Deep Learning

I am a PhD student at Dept. of Computer Science at the University of Texas at Austin.
I am working with Matthew Lease towards my dissertation on Interpretable Neural Newtorks for general purpose Machine Learning models.
I am a member of the Information Retrieval and Crowdsourcing Lab.

From 2014 - 2018 I worked on Denoising and Feature Selection for Hyperspectral Images.
I completed my Masters in Image Processing and Vision (EE) from IIT Kharagpur, under Jayanta Mukhopadhyay and Ritwik Layek on Computational model for Image Saliency.

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Featured Work

High-Precision, Low-weight, Fully Interpretable, Neural SVD solver for Big Data

A two stage neural optimization approach as an alternative to conventional and randomized SVD techniques, where the memory requirement depends explicitly on the feature dimension and desired rank, independent of the sample size. The network minimization problem converges to a low rank approximation with high precision. Our architecture is fully interpretable where all the network outputs and weights have a specific meaning.

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Featured Work

A Two-Stage Neural Framework for Non-Convex and Discontinuous Pareto Front Approximation.

A low cost, two-stage, hybrid neural Pareto optimization approach is accurate and scales with data dimensions,and number of functions and constraints. The neural network efficiently extracts a weak Pareto front, using Fritz-John conditions as the discriminator. The second stage is a low-cost, Pareto filter to extract the strong Pareto optimal subset. Fritz-John conditions provide us with theoretical bounds on approximation error.

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Research and Other Updates