My research interests lie primarily in image- and signal- processing, machine learning, remote sensing and deep learning. I am interested in using these expertise in solving problems related to digital multimedia, cloud imaging, and solar and renewable energy.

I am currently working (or have worked) on several related research projects.


This project involves designing an advertisement detection- and integration- system for multimedia videos. It is useful for next-generation online publicity (viz. product placement and embedded marketing), wherein advertisements are seamlessly integrated into the video scenes. We use deep-learning based techniques for determining if a video frame contains an existing advert, and for accurate localization of adverts in the selected video frame. Subsequently, new adverts are seamlessly implanted into the original video, to create a new augmented video.

Cloud Imaging

In this project, we develop low-cost, high resolution, ground-based sky cameras for imaging the sky scene. These cameras capture continuous stream of images, which are essential to learn cloud dynamics and understand various atmospheric events. We devise state-of-the-art image segmentation and image classification algorithms that computes automated cloud coverage data, recognizes cloud types and estimates the cloud-base height.

Solar Analytics

In this project, we use a multi-modal data integration approach, in using various sensors (camera images + weather station recordings) to provide useful insights about solar energy. We use image- and weather-station- data for accurate solar energy estimation and forecasting. This is useful in the field of photovoltaic (PV) generation and integration.


This project deals with the analysis of user- interaction and cooperation in wireless networks. We propose algorithmic framework, that helps in improving the performance of the IEEE 802.11 protocol.

We also work in proposing a dynamic frequency allocation and interference mitigation algorithm for dense urban wireless networks. The algorithm is implemented and tested on-air in a proprietary Ericsson software.


In this research theme, we dealt with several software-related issues. Heap management is responsible for the allocation of heap segments to a running application. We propose a new heap management technique that has an inherent auto compaction technique in its algorithm leading to minimum fragmentation of memory space.

We also developed randomized cryptographic techniques that can be effectively provide better security solutions.