I am a Research Fellow at Microsoft Research Lab - India mentored by Dr. Praneeth Netrapalli and Dr. Prateek Jain. I am a part of the Provable Non-convex Optimization for Machine Learning Problems project group.
My interests are in aspects of Large-Scale Optimization, Random Matrix theory and Statistical Learning theory that arises in fundamental Machine Learning problems.
Our recent work, Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds has been accepted for a Spotlight presentation at NIPS 2018. Currently I am trying to understand the trade-offs between optimization and generalization in simple Neural Networks. Other topic that have caught my recent attention are Reproducing Kernel Hilbert Space theory and Manifold Optimization.
I am also interested in High dimensional geometry, Statistics, Analysis and areas that require geometric thinking. I aspire to pursue the areas of my interest through a Ph.D.
You can find my resume here.