I recently joined Dolby Laboratories as a Deep Learning Researcher in the Applied AI team in January 2022. Prior to this I graduated with my PhD from the DICE Lab at New York University under the supervision of Dr. Chinmay Hegde, in December 2021. I have also interned at Google in 2021, Adobe in 2020 and MERL in 2018. I am an alumnus of Iowa State University and BITS Pilani, Goa, where I earned degrees in Electrical Engineering and Physics.
My interests lie in the intersection of signal processing, computer vision and deep learning. During the course of my PhD, I worked on several problems in inverse imaging, such as compressed sensing, phase retrieval and HDR imaging using deep neural networks. More recently I have been analyzing unrolled gradient based networks for inverse imaging problems. On the computer vision side, I have worked on developing algorithms to train adversarially robust neural networks. Concurrently I have also looked at new attack models to inspect vulnerabilities of vision based transformer architectures.
Outside of the topics that I explored during my PhD, I am also broadly interested in neural network model compression, designing deep learning based image and video compression models, generative networks and self-supervised learning.
May 2022: Our paper on Token Attacks on Vision Transformers has been selected for spotlight presentation at CVPR 2022 Workshop on Transformers for Vision.
May 2022: Extended version of our paper on Inverse Imaging using Generative Priors has been accepted to IEEE Transactions on Information Theory.
March 2022: I will be giving a talk at the SIAM conference on Imaging Science on March 23.
February 2022: Our paper on Inverse Imaging using Generative Priors has been accepted to ICASSP 2022!
January 2022: We have openings in our team, apply here!
January 2022: I moved to the San Francisco Bay Area. Feel free to reach out to me if you’re in the area!