I am a Master’s student in the department of Computer Science and Engineering at Vellore Institute of Technology. My primary research interests lies in Deep Learning and Computer Vision. I consider myself an open-minded person who is willing to learn new things and willing to share my knowledge with others. I love to explore, whether it be places or skills. I love collaborating with diverse teams to solve intriguing challenges. I am passionate about data science, GPU’s (of course) and Anime (lol!). To know more about me, scroll down…


  • Machine Learning
  • Deep Learning
  • Image Processing
  • Natural Language Processing


  • NXP Semiconductors

    Sept 2022 - Present

    Worked on a windows application project where addressed bugs, mitigated security vulnerabilities, and conducted testing. Currently, engaged in a Python automation project.

  • Vir Softech Pvt. Ltd.

    June 2018 - July 2018

    Developed an application that extracts values against each fields of an Insurance form using Image Processing and OCR Engine

  • Tenpi Technologies Pvt. Ltd.

    May 2017 - July 2017

    Developed an Android App with features such as- user signup/login , Bluetooth Low Energy (BLE) connectivity and Location Services.


Predict Energy Efficiency of Buildings

Predicting the Energy Efficiency (Heating Load and Cooling Load) of buildings using Azure Machine Learning Studio.
Boosted Decision Tree Regression method is used as the model due to its ability to learn non-linearity in data.

Media Server

A local network based Media Server built on Raspberry Pi for seamless media streaming across devices (cross platform).


An Open CV project that captures webcam feed and tracks a particular color and draws the path of that color movement. It can be used to write on screen by just moving a pen in air.

Knowledge-Based Expert system for diagnosis of Agricultural crops

Implemented expert system using Convolutional Neural Network on dataset of 40,001 diseased leaves to accurately identify major diseases in 9 crops. Achieved 99.78% accuracy.

Toxicity Detection

Scrapes youtube comments via url and tweets from twitter using Hashtags and can detect toxicity percentage on the data.