◦ Course Generation using Generative AI: Working under Prof. (Dr.) Benjamin Nye to help create novel techniques for course generation, tutoring content generation, and OpenTutor courses for learning AI using Generative AI for the AIRCOEE program in collaboration with the US Navy.

◦ Cogeneration Testbed: Helping maintain the technologies for co-generation of tutoring content using open and cloud-based LLMs.

◦ Student Worker: Working under Prof. (Dr.) Mark Bolas to help create the introductory Python Programming course for Game Developers.

◦ ML Research: Helping to find new approaches to apply Generative AI using LLMs and Diffusion Models towards solving problems at a large-scale in the field of Creative Media like games and movies.

◦ WandB Reports: Creating high-quality Weights & Biases Reports around the newest trends in Computer Vision and Machine Learning

◦ Integrations: Developing new integrations for Weights & Biases with popular open-source libraries with logging capabilities

◦ Blog Posts: Writing highly technical and researched blog posts around different challenges within Computer Vision and Machine Learning

◦ Video Blogging: Developing informative videos around the written blog posts for generating educational content

◦ Research: Working towards a publication along Domain Adaptation problems in Autonomous Driving under Prof. C.V. Jawahar and Prof. Shankar Gangisetty

◦ Code Implementations: Operating with internal tools to perform large-scale GPU training and experimentation on Image Segmentation problems

◦ Demos and Examples: Work done towards adding newer demos, examples, and guides to the internal and external official documentation, most notably around converting torchvision models into TFLite

◦ Front-end and Back-end APIs: To work on adding to existing infrastructure for front-end and back-end functions in TensorFlow and JAX

◦ Internal AI Developer: To work on an AI Developer (Code-LLMs) that automates and builds upon the existing codebase and speeds up internal development, along with handling their training through Cloud resources like GCP and AWS

◦ Image Annotation Tool: In-house end-to-end tool, developed with TypeScript (APIs from/into Amazon RDS) and integrated into a React frontend to be used by doctors for tagging and annotation of patient images.

◦ ML CI/CD Pipeline: Designed and partially implemented a Continuous ML pipeline with use of AWS SageMaker, AWS RDS and AWS EC2 for deployment of a classifier.