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"Hello World"- We have adopted this phrase as a sign to greet, sustenance, creativity and a sign of life.
This serves as an apt expression to breathe life into the following sentences as I begin to describe my journey as a Data Scientist.

Through a mixture of Coursera and self-learning, it was during my bachelor's that I recognized that the ability to process and interpret data in an efficient manner presented us to make the world a better place by providing us with the power to make data-driven decisions. Following some setbacks in my journey early on in my quest, I kept on laboriously researching the latest trends and solutions to stay abreast, recognizing overnight success stories are few and far between. Somehow, through a combination of self-interest and acquainting myself with great minds, I came across the fascinating world of Computer Vision which enables machines to replicate the human visual system, signalling a feeling of "Eureka!" in me- a reason for my being and a never-ending passion to yearn to learn, exploring and tackling problems in this domain.

I have extensively worked on implementing cutting-edge technology directly from research papers, and strive to be a continuous practitioner of learning by doing philosophy, thus aiming to provide value to any project I am tasked (or task myself) upon. It also entails that in my spare time, I love to read research papers and endeavour to code simplified implementations of them using the Tensorflow framework for deep learning.

Having said so, I have been quite the fortunate one, having had a management structure and teams around me to assist me, with the best exhibit being my current set-up here at Eagleview. I am responsible for developing applications centred on computer vision to refine and create solutions for multi-scale geospatial object recognition and segmentation in high spatial resolution remote sensing satellite imagery. My previous work experience in this domain includes having worked in the medical domain at Sigtuple, enhancing catalogues and recommendations of fashion stories at Charmboard, and developing end-to-end forecasting pipelines at an agrotech startup, Credible India.

Open source platforms are vital for technology agility, facilitating the free exchange of ideas within the developer community. I love gaining and sharing my experience on GitHub and contributing on a regular basis. I have created applications using Vision Transformers, Model Quantization, Self-supervised learning, Adversarial setups (GANs), Multiple Instance Learning, Object Detection, Image Segmentation and Gesture Recognition. Some of my recent open-source projects include the following:

• Tensorflow2.x implementation of Deep Lab V3 plus, which is a state-of-the-art model for semantic segmentation.
• End-to-end development of densely packed Object Detection using Single Shot Multibox Detector.
• DINO, a Vision Transformers training with the Self-Supervised learning
• Depth Estimation using Self Supervised Learning
• Super Resolution GANs with Model Quantization
• Colorization of images using Conditional GANs

You can connect with me through email at - [email protected].