This blog documents my work and learning at the intersection of data science, machine learning, and air cargo logistics.
I share practical notes from research projects, engineering experiments, and real-world data systems - focusing on reproducible workflows, clear explanations, and reusable implementations.
Topics I often write about include:
- Machine learning and deep learning experiments
- Data platforms and MLOps (MLflow, Docker, reproducible pipelines)
- ADS-B trajectory data and flight delay analysis
- Motion-sensor analytics and explainable AI (XAI)
- Data visualization, dashboards, and research workflows
The goal is to turn complex ideas into clear, practical knowledge that others can reuse.
What you’ll get
- research summaries and paper takeaways
- engineering experiments and project logs
- reusable code patterns and workflows
- learning paths for data science and MLOps
Posts are published whenever a project, experiment, or idea is worth sharing.
Who am I
I'm Jiahong Que, a PhD researcher at Frankfurt University of Applied Sciences. My research focuses on machine learning and data analytics for logistics and air cargo operations. Alongside academic work, I build practical projects around ML systems, data engineering, and reproducible research.