William Ong

Software Developer

About

Hi there! I am a third-year undergraduate studying computer science at the University of Washington in Seattle. I am passionate about the intersection of machine learning and human-centered impact, where my goal is to use machine learning to address real-world problems and positively impact lives. Lately, my work has focused on applying machine learning and statistical methods to better understand neural dynamics during brain-computer interface (BCI) modulation, with the goal of advancing our knowledge of neural processes and the development of neuroprosthetics and BCI systems. In parallel, I have been channeling my interest in cars by working on a model that classifies car models in low-light, low-resolution conditions. Whether I am coding, brainstorming, or researching, I enjoy solving problems and turning imaginations into realities. I strive to redefine limits, developing innovative solutions that push the boundaries of what is possible.

Outside of the lab, classroom, and my room, I enjoy working out, playing basketball, and trying new activities that challenge me both physically and mentally. You might also find me car spotting, exploring new restaurants, taking photos of sunsets and sunrises, and reading books and papers to inspire new ideas and perspectives.

I am always looking to learn, take on new challenges, and contribute to something greater than myself. If you would like to connect, learn more about my work, or collaborate, feel free to reach out via email or LinkedIn. Thank you for reading!

Research

Learning to Learn: Modeling Neural Connectivity During BCI-Driven Plasticity

2025

Performed statistical and machine learning analyses on M1 neural activity data from mice to identify neural connectivity changes during brain-computer interface (BCI) modulation.

Machine LearningStatistical AnalysisNeural Data Analysis

VIBA-Net: A Multimodal Framework for Infant Behavior Annotation

2025

Developed a privacy-preserving and computationally efficient multimodal framework to automate the annotation of parent-infant interaction videos for infant development research.

Deep LearningAudio ClassificationEmotion RecognitionObject Detection

Projects

Nexus AI ChatbotIn Development

Scalable AI chatbot with autonomous learning and automatic feature evolution. Tailor it to your needs with just a few simple prompts.

PythonModel Context ProtocolNatural Language ProcessingFastMCP

What's That Car?

AI-powered car recognition system with 97% accuracy. Save your results, share your discoveries, and explore an ever-expanding collection of cars from around the world.

TypeScriptPythonAWS ServicesReactNext.jsTailwindFastAPIGemini AI

Insight Archives

Your daily source of inspiration, reflection, and insight. Navigate through time to explore timeless wisdom, and subscribe to stay updated with fresh perspectives.

TypeScriptReactNext.jsMongoDBSendGridGemini AINetlify Functions

rhythmix

Fully customizable and personalized music recommendations with Spotify. Discover new songs catered to your preferences, enjoy personalized playlists, and listen to your favorites.

Note: Some features are now unavailable due to recent Spotify API changes.

JavaScriptReactNode.jsHTML/CSSMongoDBSpotify APICron Jobs

Experience

Full Stack Developer & Team Lead

Husky Coding Project

January 2024 - October 2024

Implemented playlist recommendations using TypeScript, MongoDB, Spotify API, and Node.js, enabling daily refreshes across 10 genres and increasing recommendation accuracy by 20%
Developed a responsive, user-friendly UI using React, Figma, and CSS media queries, ensuring a seamless user experience across all devices
Led a team of 6, managing project development from conception to delivery, facilitating weekly meetings, and ensuring adherence to deadlines and product specifications

Programming Tutor

Clark College

January 2023 - May 2023

Assisted 30+ students with fundamental object-oriented Java concepts and C programming skills, including memory management and data structures
Led code review sessions to help students debug, test, and optimize their code for improved performance, modularity, and readability
Created an engaging and supportive learning environment that encouraged students to ask questions and explore new concepts

Education

University of Washington

BS in Computer Science

Graduating in June 2026

GPA: 3.83 / 4.0

Coursework:

Algorithms, Artificial Intelligence, Autonomous Robotics, Data Management, Data Structures & Parallelism, Deep Learning, Machine Learning, Software Design, Systems Programming

Clark College

AST in Computer Science

Graduated in August 2023

GPA: 3.92 / 4.0

Coursework:

C Programming, Data Structures & Object-Oriented Programming, Digital Logic Design, Discrete Structures, Programming Tools (debuggers / profilers / scripting)