Machine Learning Engineering Intern

• Trained and tuned deep learning models for content-to-content recommendation systems.
• Conducted successful A/B testing to show efficacy of changes on production systems.

Machine Learning Acceleration Intern

• Added robust standard error calculations to internal simulation metrics.
• Implemented Kernel Density Estimation and Bandwidth Selection for automatically analyzing performance degradation.

Research and Development Intern

• Lead development of epiviz.gl, a JS framework for visualizing genomic data with WebWorkers and WebGL.
• Developed data selection, rendering, navigation with a pseudo grammar-of-graphics implementation.

Software Engineering Intern (Cloud Integrations team)

• Optimized and bolstered Azure crawlers responsible for crawling millions of data points an hour.
• Debugged and implemented fixes for issues found by customers in production for crawled metrics.

DevOps Intern

• Created a scalable application for live tracking of release branches to production using AWS Lambda.
• Designed serverless architecture scalable to arbitrary codebase size with complete up-to-date release data.
• Designed DynamoDB schema and frontend with React for a responsive efficient API and user interface.

Software Engineering Intern (Large-scale analytics group)

• Developed graph analytics using Java and functional programming with Gremlin for compliant graph databases.
• Programmed low-memory implementations of machine learning algorithms for training on arbitrarily large data.
• Created analytics for graph multi-edge merging, time-series, and data fusion using Java and MapReduce.
• Developed random forest algorithm on a distributed data system for classifying attributes on graph vertices.

Software Engineering Intern

• Worked with a partner to build a microservice acting as a REST backend to serve PDF’s with Spring.
• Created a service to read generated reports through a REST API on a dynamic front-end with React.