I'm a 6th year PhD student in Intelligent Data Exploration and Analysis Laboratory (IDEAL
) at UT Austin
. I am very fortunate to be able to learn from and interact with Professors Joydeep Ghosh
, and Alex Dimakis
. My PhD research is to study various aspects of greedy algorithms in machine learning, both from theory and practical viewpoints. From the theory side, I have worked on studying approximation guarantees for "greedy-like" algorithms for sparsity and rank constrained problems for general functions based on their smoothness and convexity, and convergence rates for greedy algorithms like accelerated IHT, Matching Pursuit and boosting . From practical side, I have worked on using greedy selections for approximate variational inference for sparse regression, sparse PCA for fMRI applications, and for interpreting black box models. In the past I have worked on problems involving predicting buying propensity based on marketing touches, large scale recommendation systems, Ad Click prediction, and ranking.