Experience

Building tools that solve real problems across research, education, and defense sectors.

Industry Experience

Machine Learning Research Intern

Huawei Technologies – Leibniz Research Center, Optimization and Control Theory · Hong Kong

Jul 2026 – Jan 2027

  • Developed machine learning and reinforcement learning solutions for wireless network optimization
  • Built end-to-end Python pipelines for data processing, model training, validation, and experimental analysis
  • Benchmarked proposed methods against baselines, reducing unnecessary network switching by 81.3%

AI Developer Intern

LeadDigital · California, US

May 2026 – Jul 2026

  • Developed an AI-based workflow to analyze technical ship blueprints and convert engineering drawings into structured outputs
  • Built data preprocessing and evaluation workflows and analyzed model errors to improve output reliability

Software Developer

University of Toronto, IDELA Lab

Sept 2025 – Present

  • Built ML and database features for APLUS, used by 1000+ students, improving data speed by 30%
  • Developed ML pipelines and frontend interface for Balance AI

IT Officer

Department of National Defence

Feb 2025 – Present

  • Designed and maintained web systems for 200+ cadets/staff
  • Automated workflows with Azure cloud tools, reducing processing time by 30%
  • Coordinated cross-team digital initiatives and technical support

Software Developer Intern

Department of National Defence

June 2023 – Aug 2023

  • Engineered backend solutions for 85,000+ cadets, migrating 25,000+ records securely via Flask with Agile management
  • Built a user-friendly portal with file uploads and secure login

Research Experience

Machine Learning Research Intern

Huawei Technologies – Leibniz Research Center, Optimization and Control Theory · Hong Kong

Jul 2026 – Jan 2027

  • Developed machine learning and reinforcement learning solutions for wireless network optimization
  • Built end-to-end Python pipelines for data processing, model training, validation, and experimental analysis
  • Benchmarked proposed methods against baselines, reducing unnecessary network switching by 81.3%

Student Researcher

University of Toronto, IDELA Lab

Sept 2025 – Present

  • Applied machine learning to large-scale educational datasets within the Aplus research initiative, developing predictive models for adaptive learning
  • Conducted experimental analyses to evaluate AI-driven educational interventions and student performance outcomes
  • Supported methodological design and implementation of data-driven approaches in educational research

Full-Stack Research Assistant

University of Toronto, Kang Lee Lab

Sept 2025 – Present

  • Developed and maintained web-based research platforms for cognitive development studies
  • Built full-stack solutions using modern web technologies for data collection and experiment deployment

Research Assistant

Stanford University - Human-Computer Interaction in Artificial Intelligence

May 2025 – Sept 2025

  • Investigated prompt engineering strategies to optimize large language model performance in human-AI collaborative tasks
  • Designed and deployed interactive research prototypes using Next.js, JavaScript, and Vercel for iterative user studies
  • Conducted data analysis in R and applied machine learning models to uncover patterns in interaction data for predictive interface adaptation

Full-Stack Research Assistant

Stanford University - Human-Computer Interaction in Artificial Intelligence

May 2025 – Sept 2025

  • Built ML models (RoBERTa, Decision Trees) for social media depression detection, reaching 82% accuracy
  • Optimized LLMs with prompt engineering, boosting task efficiency by 20%
  • Deployed interactive HCI prototypes with Next.js + Vercel integrating ML backends

Technical Skills

Languages

C++, C, Java, Python, JavaScript, SQL, HTML5

Frameworks & Technologies

R, NumPy, Microsoft Azure, PyTorch, Next.js, Vercel, Git, CSS