I am an incoming software engineer at Microsoft and will be joining the company in the Fall of 2022. I graduated summa cum laude from Amherst College with a Bachelor of Arts in computer science and mathematics and received the Computer Science Prize. At Amherst, I was involved in the Amherst College Data* Mammoths research and learning group, where I worked as a research assistant under the leadership of Matteo Riondato. Prior to research, I worked for the Department of Computer Science as both a teaching assistant and peer tutor for multiple computer science courses. I was the president of the Amherst College Computer Science Club.
Outside of academics, I have held leadership positions in Amherst’s Asian Students Association as Co-Chair of the Asian Men’s Collective and as Treasurer of the Chinese Students Association. As for hobbies, I grew up playing ice hockey, tennis, rugby, cello, and guitar!
I interned on the Frontdoor team for Microsoft’s Commerce Catalog group. The team owns an ASP.NET API service that scales over 150,000 request per second and powers the Microsoft Commerce stack for services such as Azure, Windows, Office, and Xbox. My intern project focused on enhancing the on-call experience for engineers by developing features on the team’s service to diagnose product configuration issues.
I interned with the Contact Center Cloud Solutions team and implemented software solutions for Fidelity contact centers using AWS. My projects boosted the productivity of branch agents and improved customer call experience by leveraging services such as Amazon Connect, Lambda, Lex, DynamoDB, and Elasticsearch to route customers to the appropriate contact centers.
I worked with a team of engineers to develop Flask microservices for the startup company’s medical devices e-commerce platform. I primarily focused on increasing the accuracy of parsing patient medical insurance data for the company’s insurance payment microservice. Furthermore, I enhanced building and testing efficiency among engineers by developing Flask web applications for querying company utilized APIs.
I conducted research as a member of the Amherst College Data* Mammoths, led by Matteo Riondato. The research in which I was involved improved the scalability for balanced sampling by developing a parallel algorithm for the cube method. Our work was accepted for publication as a student abstract/poster to AAAI-22.
I helped students deepen their understanding of data structures and object-oriented programming in Java by holding one-hour teaching sessions twice a week. In these sessions, I supported students in applying what they learned from classes by providing guidance on how to approach their weekly projects and homework assignments.