"Processing an unpredictable life,rendering an unbreakable me."
— Joyee Jin

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Who I Am

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Computer Science meets Cognitive Science

I build AI systems that understand human behavior, combining machine learning with insights from psychology and neuroscience.

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What I Build

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Depression Detection from Social Media

ML models (RoBERTa, Decision Trees) achieving 82% accuracy in detecting mental health signals. Deployed interactive prototypes for research at Stanford.

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Where I Work

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Research × Industry

Currently interning at Huawei's Optimization and Control Theory Lab, with experience in AI development at LeadDigital, HCI research at Stanford, and web platforms for Canada's Department of National Defence.

Joyee Jin

Hi, welcome to my home page!

I'm Joyee, a student at the University of Toronto double majoring in Computer Science and Computational Cognitive Science, with a minor in Mathematics. I love building things with software and machine learning, especially when they can solve interesting real-world problems.

I'm currently interning at Huawei Technologies, where I work on machine learning and reinforcement learning for wireless network optimization. Before that, I've worked on AI, software engineering, and research projects across different areas, from human-AI interaction to educational technology.

Outside of school and work, I love skiing and snowboarding, sailing, painting, and playing Nintendo Switch games.

Explore my experience → See my projects & publications → Browse recent projects ↓

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Moments

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Research Highlight

AAAI 2026 · Full Paper Acceptance

AI in the Wild

A meta-analytic evaluation of depression detection from social media data.

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Experience at a Glance

Current Role · Jul 2026 – Jan 2027 · Hong Kong

Machine Learning Research Intern

Huawei Technologies · Optimization and Control Theory Lab

Developing machine learning and reinforcement learning solutions for wireless network optimization.

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Past Role · May 2025 – Sept 2025

Full-Stack Research Assistant

Stanford University · Human-Computer Interaction in Artificial Intelligence

Built machine learning models for social media depression detection, reaching 82% accuracy, and deployed interactive HCI research prototypes.

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Recent Projects

Paper Reference System

Academic writing tool with article summarization and semantic search, reducing literature review time.

Tech: Python, ML (semantic search), NLP

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Hearing Disability Assistant

Arduino-based wearable that detects critical sounds (alarms, knocks) and logs alerts to a companion web app.

Tech: Arduino, Web Development, Hardware Integration

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Social Media Depression Detection

ML models (RoBERTa, Decision Trees) for detecting depression signals in social media, achieving 82% accuracy.

Tech: Python, RoBERTa, PyTorch, Next.js, Vercel

Research Project