Responsible for managing and co-ordinating 9 technical team members to provide expertise and develop solutions to any technical challenges faced by the ABIT committee.

I provide trainings on how to practically apply deeplearning to solve real-world problems on behalf of codecell RGIT.

I am responsible for satisfying all the technical needs of IEEE-RGIT. I also oversee coordination of the technical digital creation and the website maintenance team.

In charge of hosting, managing and co-ordinating hackathons on behalf of codecell RGIT.

This contribution features a novel technique of abstracting dynamic heat-map as the sequential input to custom LSTMs and implements a spinal health index estimator using only an input video of patients performing predetermined tasks.

This paper presents an end-to-end learning-based system that utilizes a multi-branched network architecture to estimate the speed of vehicles from only two time-varied, unprocessed images. And further proposes an optimized method for dataset generation and license detection

This patent describes an end-to-end approach to predict the user's intent from a feature-engineered EEG dataset using bidirectional recurrent neural networks.

This patent describes a deep learning approach for an optimized extraction of semi-structured data and the process to cross-validate the data with the live user to authenticate their identity in real-time.

This is the official implementation for one of my research papers

This project extracts all the text from a live camera and renders it back on screen to display it to the user

Utilizes the convolutional neural networks to recognize the faces of friends

Implements YOLO v4 on Raspberry pi with a camera module to intelligently take photo by looking at the gesture of the person in the frame and further sync it to the cloud