Frank van Paassen
I am currently pursuing a Bachelor of Applied Science in Computer Science at McMaster University, maintaining a strong academic record with a GPA equivalent to 3.9/4.0.
About
I am currently pursuing a Bachelor of Applied Science in Computer Science at McMaster University, maintaining a strong academic record with a GPA equivalent to 3.9/4.0. I have a passion for software development and IoT projects, particularly those that apply technical skills to real-world problems in agriculture and education. My experiences include developing innovative data pipelines and scalable software solutions, teaching coding to a large number of students, and contributing to open-source UI framework NiceGUI.
I take pride in my programming versatility, working comfortably in multiple languages such as Python, C++, Elm, and JavaScript. My projects range from IoT systems for greenhouses to optimization calendar apps, and I thrive in both startup environments and academic settings. I’m eager to leverage my skills in cloud technologies, real-time data streaming, and interactive web interfaces for impactful and innovative software development.
- 2 years' experience
- Joined August 2025
Industry experience
- Education
- Agriculture & Mining
- Software & Internet
Work experience
Taught coding in Elm to over 1000 students, achieving high satisfaction among students and teachers. Developed educational tools including state diagram editors and interactive MVU simulation tools to facilitate learning.
Built an IoT moisture sensing system for greenhouses by collecting soil VWC, EC, and temperature data via TER12 sensors using UART and GPIO. Streamed over 80,000 datapoints through WebSocket to a backend with TimescaleDB hypertables, and developed a responsive web dashboard with NiceGUI featuring live graphs and Google OAuth2 login.
Developed a Python frontend with custom UI components to highlight crop stress hotspots and yield trends in real-time dashboards. Architected scalable data ingestion pipelines using Google Cloud Buckets, Firestore, and InfluxDB to optimize throughput of terabytes of IoT and image data. Engineered end-to-end ML pipelines with automated labeling, data versioning, and model validation. Integrated IMU sensors to monitor greenhouse rail tilt and visualized metrics via real-time heatmaps.
Education
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