Software Enginering Intern

May 2024 - Present

• Designed and implemented a robust peer-to-peer data distribution system with C#/ASP.NET/gRPC, enabling more efficient synchronization of large inspection recipe data across fleets of customer machines
• Conducted large scale integration of C++ software components using gRPC and proved a 50% reduction in middleware overhead from the previous solution
• Integrated microchip design service with a distributed application generating SQL databases of 10 million wafer defects to enable enhanced wafer defect clustering algorithms

Software Enginering Intern (Advanced Driver Assist Systems)

May 2023 - July 2023

• Established a novel method to automatically integrate software algorithm models across 10+ teams via Python and demonstrated to department-level management its ability to enhance workflow productivity
• Improved software stability by uncovering and resolving lingering memory defects in C++ software within a complex runtime environment; wrote model program exemplifying correct use
• Steered the efficient design of an embedded performance measuring application by leading team meeting on the design tradeoffs between different data streaming methods

Autonomous Software Development Intern (Autonomous Robotics)
May 2022 - August 2022

• Engineered an optimized pipeline using JavaScript/Java and WebSockets to send large amounts of fused sensor data over the network and display a real-time 3D visualization in a web application
• Designed a web dashboard controlling an AWS video stream and built embedded Linux and RTOS software to demonstrate capabilities of a computer vision and machine learning board
• Developed an inexpensive sensor interface device with multithreading and UNIX sockets in C++ on a microprocessor to cut device cost by 80% from the prior solution

Computer Vision Final Project
May 2024

For my final project for EECS 442 Computer Vision, I worked with a group of three on optical flow guided object tracking for traffic analysis.

We devised and evaluated a novel method method for tracking traffic flow that combines both machine learning methods (CNNs, object detectors) and classical computer vision methods (optical flow).

My primary responsibilites included locating datasets, designing and training the tracker model architecture, and creating the figures and content for the methodology section of the report.

Software Lead of university robotics project team

September 2021 - Present

• Initiated a streamlined process of simulating autonomous submarine software and physics via Docker and Unity (C#) to save time during testing and assist new members with onboarding
• Led a software team of 15 members by organizing software architecture, efficiently distributing tasks, verifying code quality and robustness, and assisting members with overcoming blockers
• Spearheaded development of a custom flight controller board using C++/Python to gain fine control of the autonomous submarine’s movement and save 75% on device hardware cost

Vice President of university cybersecurity club

September 2021 - May 2024

• Designed and taught web security workshops and created interactive challenges to engage new members, covering concepts such as client/server, JavaScript internals, SQL injection, and cross-site scripting
• Composed intricate and rewarding web security challenges for the organization’s annual Capture the Flag competition, leveraging node.js/Express.js, Google Cloud, Docker, and a Redis database (read more about my challenges here and here)
• Documented key design decisions and technical nuances of web security challenges and their solutions