I am a Research Engineer at the Department of ECE, Rice University. I work with Prof. Ashok Veeraraghavan computational imaging group in the intersection of computer vision, machine learning and imaging. I am also a visiting researcher with Dr. Teresia O'Connor group at Baylor College of Medicine where I led significant machine learning and data collection efforts for multiple NIH grants to develop and deploy state-of-the-art computer vision algorithms to objectively measure children's TV and mobile use behaviors.
Before joining Rice, I finished my masters in Electrical engineering with Dr. Kaushik Mitra at IIT Madras, where I worked on generative models for image restoration in computational imaging frameworks. My master's thesis work won Qualcomm Innovation Fellowship for the year 2016-2017. I got my B. Tech in Electronics and Communications from RGUKT, Basar, India in 2015.
Broadly I'm interested in the computer vision, machine learning, and generative models and their applications. I love building state-of-the-art AI products for challenging problems.
We validated a camera based privacy-preserving tech to track TV viewing in real-time in the participant's home. This technology is now being deployed in a 5-year NIH study with 200 children to measure impact of screentime on their health.
We use a deep autoregressive image prior PixelCNN++ for solving inverse imaging problems. Since autoregressive nature explicitly models pixel level dependencies it reconstruct pixel level details much better than existing state of the art methods.
Using deep neural networks we reconstruct full sensor resolution light field from a single coded image. Our approach involves depth based rendering where depth is learnt in an unsupervised manner.
We use a deep recurrent image prior, RIDE, which can model long range dependencies in images very well. Using this for compressive image recovery we show much better reconstructions especially at lower measurement rates.
We show mathematical models of communication mechanisms debloyed by butterflies. This work is extended to an optimization algorithm, Butterfly Mating Optimization (BMO), Jada et al. 2015. We applied it for image clustering application.
ROBOG Autonomously Navigating Outdoor Robo-Guide
Kranthi Kumar R., Irfan Feroz G. M., Chakravarthi Jada, Harish Y., Anil Kumar V. International Conference on Swarm, Evolutionary, and Memetic Computing, 2014
Neural networks are used to learn the navigation information. For outdoor navigation, we propose an image processing pipeline for road detection.