Rahul Singh

I am a Data Scientist based out of New Delhi, India. My primary interests include Data Science,Big Data,Data Counsultant, Machine Learning, Deep Learning,Natural Langauge Processing,Statistical Modeling,Business Intelligence and Data Analytics. I have focused on deriving useful business insights from large datasets and helping organizations generate business value and revenue using an agile and data-driven approach.

I obtained my Master's Degree MCA(Master of Computer Application)from ABES Engineering College, India in 2009.

I obtained my Bachelor's Degree(B.Sc) in Computer Application from Chhatrapati Shahu Ji Maharaj University, India in 2005.

Prior to joining Kochar, I've had the pleasure of working at:

• Constalytics, Data Scientist, January 2018 - July 2018

Email  /  CV  /  LinkedIn  /  Github

My interests broadly lie in the fields of Data Science, machine learning, and deep learning and Big Data. Much of current work research is focused on supervised learning, unsupervised learning, and dataless classification i.e. training machine learning models with little or no data.

Working on applied research problems in automated contextual advertising for Knorex KAIROS and Knorex XPO.

Worked on LingoLens, a K12 language learning mobile app. Contributed towards building a question tagger system for peer to peer question answering forum for the CampK12’s Generation Blockchain 2018 Summit. Co-designed and co-instructed CampK12's first Machine Learning & AI course.

The project involved training and deploying a road segmentation and extraction system using high-resolution satellite imagery for reliable and low-cost terrain monitoring and infrastructure quality assessment.

Optimized the trained model for real-time applications with final inference speed of only 0.28 seconds on Tesla K80 GPU achieving a mask accuracy of 95% and a dice score of 65% on the validation set.

A multilingual language learning app for K12 students providing translations and transliterations for indoor and outdoor objects in over 20 languages.

We implemented a real-time object detection and on-device scene classification model Trained a custom YOLO model to detect ∼150 objects with an mAP of 67% improving the on-device detection and classification inference speeds by 19% with only 6% performance degradation compared to SoTA methods such as RetinaNet and SSD.

In this project, I led a 3 member team a ML-based system to classify books into their genres purely based on its title, without prior knowledge or context of author and origin.

I was responsible for the architecture, training, and deployment of the model. We improved the TFIDF and LR baselines by training a LSTM using pre-trained word2vec embeddings.

Open-source implementations for various computer vision tasks such as Template matching, Object Tracking, Face Swapper, Live Sketch, etc, using OpenCV.

60+ stars, 60+ forks.

This project involved developing a social blog/profile web app where users can interact with each other. Other features including personal feed, posts, comments, signing, and logging in.

The web app follows RESTful approach for CRUD operations and was deployed using Heroku. Backend frameworks used: NodeJS, ExpressJS, MongoDB, PassportJS

More projects...

Website template credits go to Jon Barron.