• Led the development and adoption of GraphQL federation for the auctions experience on eBay, improving the bidding experience for over 50 million active users across the world

• Implemented a subgraph to query and update user’s checkout preferences, resulting in a reactive user experience and an over 40% reduction in latency for checkout due to seamless orchestration, in addition to improving developer productivity and accelerating development of new flows

• Optimized eBay's core checkout CI/CD pipelines, cutting lead times by 2 hours and enabling efficient trunk-based development

• Streamlined payout experience for over 18 million sellers by improving scalability and resiliency with circuit breakers, multithreaded processes, and caching for payments applications

• Built robust front-end automation testing framework with dense validations that reduced weekly UI production bugs by over 80%

• Built an adaptive OCR algorithm to extract and understand information from captured documents

• Dockerized and deployed a flask server for the algorithm along with a REST API for client integration

• Automated name and address population, reducing guest checkout time and improving checkout conversation

• Part time Software Engineer at DMAI, an AI startup, developing educational tools for kids of all ages

• Developed robust realtime computer vision algorithms, such as emotion recognition, hand over face occlusion, adaptive background subtraction, finger detections and classification, etc. that involved extensive use of OpenCV, TensorFlow, and dlib

• Ported algorithms from python to C# and deployed scalable models to mobile devices to support hundreds of concurrent users

• Data Journalist at The Daily Bruin, working on web articles involving statistical analysis and visualizations of university data

• Collected and analyzed undergraduate lecture data using Selenium from the course webpage of UCLA. Edited and formatted data as jsons using python scripts

• Developed an article visualizing multiple aspects of the data using d3.js, such as average lecture length, average lecture size, average number of lectures per week, etc. These visualizations were in the form of dynamic graphs with adaptive filters like major, school, year, etc.