• Worked in the Alexa Entertainment division and built an AWS ECS application on Fargate from scratch

• Used TypeScript, Jest, and the AWS Cloud Development Kit (CDK) to create & test NLBs, hosted zones, record sets, VPC endpoint services, alarms, monitors, personal stacks, metric filters, and other infrastructure resources

• Managed VPC subnets, Regions, and Availability Zones, and incorporated pipeline configurations through code

• Achieved all baseline and stretch goals for the project and put together comprehensive documentation

• Actively engaged in team standups, design doc reviews, and code reviews

Completed over 150 projects with consistent on-time deliveries and positive client feedback,

including voice overs for:

• YouTube channel narrations

• Films/Cartoon Series

• Podcast/Radio

• Commercials

• Corporate presentations

• eLearning videos

• and more

• Led the project’s feature/color recognition & photo categorization efforts

• Researched and implemented computer vision techniques in Python using libraries like PyTorch, OpenCV, NumPy, and SciPy

• Significantly improved image processing by devising a segmentation algorithm with a U^2-Net for salient object detection, flexible threshold adjustments for masking, and conditional image cropping

• Engineered a deterministic color extraction technique to advance from k-means

• Developed a complete Python program for classification while adhering to a Scrum framework with 2-week sprints

This work was done in support of a project aiming to create non-invasive digital health tools to enhance infant nutrition practices. The mobile health tool will be utilized as part of an IRB-approved study in the lab of Dr. Elizabeth Johnson in the Division of Nutritional Sciences at Cornell in collaboration with the start-up, SimpliFed.

• Researched and applied artistic style transfer on static images in Python using convolutional neural networks (CNNs)

• Implemented background removal and color matching through machine learning/artificial intelligence models

• Assessed effects of assorted pre- and post-processing measures and iteratively improved upon results

• Utilized open-source lip-syncing algorithms to implement animation techniques in Python

The goal of this project was to explore ML/AI techniques to generate images and potentially videos with the aim of improving the “friendliness” of U.S. Supreme Court oral arguments and judgments. This work will serve as the foundation for future extensions with eventual use on the Oyez website.