About Me

Hi! I'm Haotian (Nitro) Zhang, my work spans intelligent systems that connect simulation, perception, and real operations. My capstone project explores AI-driven traffic hazard detection using RGB–LiDAR fusion in CARLA, while my Lean Improvement Library focuses on transforming manufacturing improvement cases into a searchable, governed knowledge platform used at scale.

Contact

Email: 412841308@qq.com

Education

Professional Experience

Software Engineering Intern - Boeing Tianjin Composites Co., Ltd.

December 2024 - Present | Tianjin, China

  • Designed and deployed a full-stack Lean Improvement Knowledge Library supporting ~900 internal users, centralizing continuous-improvement cases across departments.
  • Implemented a secure ASP.NET Core (.NET 10) backend with cookie-based authentication, role-based access control, and administrative governance workflows.
  • Modeled scalable organizational and ownership relationships (departments, positions, managers) without storing full employee records, reducing data complexity and maintenance cost.
  • Built high-performance search, filtering, and analytics APIs (Dapper + SQL Server) enabling rapid retrieval and statistical analysis of improvement outcomes.
  • Developed dynamic frontend components for cascaded classification, attachments, and in-browser previews, improving usability and long-term knowledge reuse.

Research Assistant - IAS Laboratory, University of California, Irvine

June 2025 - December 2025 | Irvine, CA

  • Developed a traffic hazard detection system in CARLA, producing and processing 50K+ multimodal samples (RGB + LiDAR) for Vision-Language Model training.
  • Designed a 0-1 hazard scoring evaluation framework to compare model predictions against human annotations.
  • Enhanced model robustness by fusing RGB and LiDAR data, and stress-testing against synthetic edge-case datasets.

System Testing Intern - Boeing Tianjin Composites Co., Ltd.

February 2023 - June 2023 | Tianjin, China

  • Automated manual workflows using Python scripts, improving product delivery efficiency by 60% and saving 4+ hours weekly.
  • Conducted system testing for a Manufacturing Execution System (MES), identifying and logging over 50 software bugs.
  • Trained 20+ users on MES operations and authored user manuals and deployment documentation.