Graduate Teaching Assistant for the course COGS-118B Introduction to Machine Learning. The goal of this course is to teach the foundations of machine learning. This involves presenting the algorithms as well as the mathematics and reasoning behind the algorithms.

- Prototyping Computer Vision (Deep Learning) based solutions for Intelligent Video Analytics.- Building microservice using Container Builder configuration file and Helm Chart [Kubernetes].- NVIDIA Graph Composer on AWS EC2 Ubuntu Instance with NICE DCV & No-Machine.

• Built ETL pipelines using Spark for Starbucks Recommendation – ‘USUALS’, serving 20 million customers across the US; generating revenues worth 100 million USD per annum.• Engineered Vehicle Detection System for Starbucks Drive-through to increase the throughput.• Improved promotional offer campaign using Uplift modeling for better net incremental revenue (NIR).

Worked under Professor Erik Cambria on 'Empathetic Dialogue Systems' for my Undergraduate thesis.

- Getting my hands dirty with Amazon Web Services - S3, EC2, Machine Learning, IAM, and CloudFront.- Using NbGrader to develop automated assignments to save instructors' time and provide a common platform to hundreds of student to learn simultaneously.

Data Analytics using R programming and Basic Management Skills required by Managers to get the best out of Data Scientists were the primary focus during the 4 week internship program.