Teaching Assistant – Systems Programming
University of Washington • Allen School of Computer Science
March 2026 – June 2026 • Sept 2026 – Present
- Taught weekly section conferences and provided technical guidance for 200 students in low-level C and C++ system concepts.
- Assisted students in debugging complex memory management issues and segmentation faults using debugging tools such as GDB and Valgrind.
- Graded student code, verified automated test suites, and leveraged Git, Linux CLI, and push scripts to deploy starter code and assignments.
Technologies:
C |
C++ |
GDB |
Valgrind |
Linux CLI |
Git |
Systems Programming
Research Assistant
UW Sensor Systems Lab • University of Washington
March 2026 – Present
- Leveraged Python and Agentic AI to construct physical simulations mapping acoustic forces on objects suspended in an acoustic levitator to accurately model 3D force distribution.
- Collaborated on building a Schlieren optical imaging system to render acoustic pressure sound waves visible to the naked eye.
- Designed, CADed, prototyped, fabricated, and programmed an automated correction rig utilizing computer vision and machine learning to calibrate a camera system across 4 degrees of freedom, capturing air refraction data from the Schlieren setup and saving hours of manual calibration.
- Processed real-time sensor feedback to guarantee robotic positioning precision within sub-millimeter tolerances.
Technologies:
Python |
Agentic AI |
Computer Vision |
Machine Learning |
CAD |
Acoustic Levitation |
Hardware Prototyping
- Engineered and deployed a responsive voter guidance web platform utilizing HTML, CSS, and modern JavaScript.
- Configured DNS and secure routing infrastructure utilizing Cloudflare for rapid and reliable page delivery.
- Streamlined ballot and candidate decision-making for nearly 2,000 active citizens during the 2024 Washington primary elections.
Technologies:
HTML5 |
CSS3 |
JavaScript |
Cloudflare |
DNS Management
- Trained an object detection computer vision model using Google Colab and YOLOv5; built interactive data visualizations of AI datasets in Python to evaluate training metrics.
- Coordinated dataset collection pipelines, refining data cleaning workflows to push model detection accuracy past 80%.
- Co-developed the web application frontend (HTML, CSS, JavaScript, Bootstrap) and backend (Flask, Cloudflare, DNS) to deploy the ML model with slide deck-style transitions.
Technologies:
Python |
YOLOv5 |
Flask |
Computer Vision |
Google Colab |
Bootstrap |
Cloudflare