Matthew Goehner
I’m Matthew Goehner, a recent Computer Science graduate from Sheridan College who enjoys building real-world mobile and web software through hands-on projects and co-op placements.
About
I’m Matthew Goehner, a recent Computer Science graduate from Sheridan College who enjoys building real-world mobile and web software through hands-on projects and co-op placements. I’ve worked across Android and iOS using Kotlin, Swift, and React Native, and I’m comfortable integrating REST APIs, working with databases, and using Firebase and cloud services to ship features that actually help users.
In addition to mobile development, I’ve contributed to research and product-style work that blends data, validation, and performance. I’ve supported machine learning model testing and evaluation, enhanced Python data visualizations, and helped build app experiences ranging from weather and GPS-based functionality to an iOS litter-reporting platform that uses device input and image classification.
- Joined September 2026
Industry experience
- Software & Internet
- Computers & Electronics
- Healthcare
Work experience
Tested and refined a TensorFlow/Keras prediction model to improve performance and evaluation reliability. Collaborated with other co-op students to explore different approaches and identify features that improve accuracy. Analyzed and adapted prior CNN predictions, and enhanced a Python/Matplotlib visualization tool by adding validation features, multilayer geological data views, and performance optimizations for large datasets. Focused on data validation methods, including supporting new data types such as magnetic fields and rock samples for visualization alongside prediction data, with additional tooling for overlaps and filtering.
Tested and refined a TensorFlow/Keras prediction model to improve performance and evaluation reliability. Collaborated with other co-op students to explore different approaches and identify features that improve accuracy. Analyzed and adapted prior CNN predictions, and enhanced a Python/Matplotlib visualization tool by adding validation features, multilayer geological data views, and performance optimizations for large datasets. Focused on data validation methods, including supporting new data types such as magnetic fields and rock samples for visualization alongside prediction data, with additional tooling for overlaps and filtering.
Education
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