Portfolio

Andrew Wang

Backend-Focused Software Developer

Computer Science student at the University of Connecticut building practical software systems across backend development, machine learning, and databases.

About

Building software that matters.

I'm a Computer Science student at the University of Connecticut, expected to graduate in May 2026. I focus on backend development, machine learning, databases, and practical software systems. I enjoy building tools that connect technical implementation with real-world use cases.

B.S. Computer Science · University of Connecticut · Expected May 2026

Technologies

PythonSQLJavaScriptJavaC++FastAPIREST APIsMachine LearningDatabases

Experience

Work history.

Freelance Software Engineer

Confidential Client

November 2025 – Present
  • Develop backend functionality for Landing Gear Recommender, a conceptual aircraft landing gear sizing tool for early-stage design exploration.
  • Implement features for configuration generation, validation checks, weighted scoring, and sensitivity analysis.
  • Support CLI and FastAPI-based web application development.

Technical Analyst

UConn School of Engineering

September 2024 – Present
  • Provide technical assistance to around 20 students per week in the computer lab.
  • Support coursework, research, and project-related troubleshooting.
  • Perform routine checks on desktops, printers, and monitors to maintain lab functionality.

Tiger Team Analyst

UConn School of Engineering

January 2024 – May 2024
  • Resolved 20–30 technical support tickets weekly for UConn professors and staff.
  • Troubleshot hardware and software issues to ensure prompt, reliable service.
  • Followed up with requesters after resolution to confirm issues were fully addressed.

Projects

Selected work.

01 / 03

SDP-Email: Outlook Phishing Detection

Developed backend and machine learning components for an Outlook add-in that detects suspicious emails and alerts users in real time.

  • Built a Naive Bayes model to classify phishing emails using email content features.
  • Engineered backend logic to process opened emails and return detection results.
  • Connected the ML detection pipeline to an Outlook add-in warning system.
PythonNaive BayesMachine LearningBackend APIsOutlook Add-in