🟢 Open to work
Building AI-driven prototypes, automating workflows with Python, and turning early ideas into working demos with small teams.
My projects feature AI applications from an internship, collaborative hackathon builds, web prototypes, and automation scripts developed with Python, Git, and AI coding tools.
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TruBridge · ✅ Verified by Extern · ⏱️ In progress
A TruBridge externship in healthcare data analytics focused on analyzing public health datasets, building dashboards, and applying statistical methods to infection and outcomes data.
Building AI-driven prototypes, automating workflows with Python, and turning early ideas into working demos with small teams.
Yizhou Zhang, a DKU undergraduate building AI-enabled tools and web prototypes. He has led hackathon teams, interned in AI application development, and enjoys turning early ideas into working demos.
Externships
TruBridge Healthcare Data Analytics Externship
In progress
Experience
AI Application Development Intern
Jiangsu Longdao Data Group Co., Ltd. · May - July 2026 · 5 weeks
Team Lead & Co-Presenter
LoanShield — HackDKU 2026 · Apr 2026 · 1 week
Team Lead
Cravely — DKU × ED Healer 48-Hour Hackathon · Apr 2026 · 2 days
Server
Wutongxiang Restaurant · Jul - Aug 2025 · 4 weeks
Education
Duke Kunshan University (DKU)
Undergraduate Student
Changzhou No.1 High School
High School Student
Changzhou Foreign Languages School
Middle School Student
Skills
✅ Verified by Extern · ⏱️ In progress
A TruBridge externship in healthcare data analytics focused on analyzing public health datasets, building dashboards, and applying statistical methods to infection and outcomes data.
The externship examined large public healthcare datasets to identify patterns relevant to infections and outcomes. The work involved building interactive dashboards and applying statistical and trend analyses to the data. Delivered artifacts included cleaned datasets, analysis notes, and visualizations.
I converted raw public health data into actionable analysis and visual summaries. My work combined data cleaning, correlational analysis, and dashboard visualizations to make patterns in infection and outcome metrics visible.
