ABOUT

Me!

Josh Bakelaar

Major in Computer Science with a Minor in Game Development

Hi, I'm Josh!

I am a recent Computer Science graduate from Western University, where I also minored in Game Development. My experience spans a range of programming languages, including Java, Python, C++, and JavaScript, with a strong foundation in web development, VR simulations, and machine learning. As a mentor for First Robotics Team 5024, I’ve led programming projects, introduced agile methodologies, and helped implement data-driven solutions. Additionally, I’ve managed teams and coordinated leadership programs, enhancing my ability to work collaboratively and lead effectively. I’m passionate about leveraging technology to solve complex problems and eager to apply my skills in software development.

PROJECTS

5K24 Spark

Comprehensive Scouting Website for First Robotics Competition (FRC)

JavaScript, React.js, CSS

I engineered a platform to operate offline, enabling teams to scout and save data without an internet connection, ensuring uninterrupted functionality in dynamic competition environments. This comprehensive scouting website, developed specifically for FRC teams, facilitates the collection of data on other teams during competitions. The system seamlessly integrates all relevant information into a QR Code, streamlining the integration of data into teams' scouting spreadsheets. This innovative solution enhances data accuracy and accessibility, significantly improving the scouting process for FRC teams.

Structural Integrity

The VR Damage Assessment Simulator

C#, Unity

Structural Integrity is an advanced Virtual Reality education tool developed using C# and Unity, tailored for in-class learning with civil engineering students at Western University. This immersive simulator provides a hands-on experience, enabling students to navigate through virtual environments that mimic real-world structures. By interacting with these environments, students learn to identify various types of structural damage, assess their severity, and understand the implications for overall structural integrity. This tool not only enhances theoretical knowledge but also bridges the gap between classroom learning and practical application, preparing students for real-life engineering challenges. Through this engaging and interactive platform, students gain critical thinking and problem-solving skills essential for their future careers in civil engineering.

Analysis of Machine Learning Models

for Brain Age Prediction using the OASIS Dataset

Python

This project aimed to develop and compare machine learning-based models for predicting brain age from MRI data, an important task as discrepancies between physical age and brain age can signal neurological conditions or cognitive decline, making accurate prediction models clinically significant. The study conducted a comprehensive comparison of four distinct ML models: Support Vector Regression (SVR), Residual Networks (ResNet), and Lasso regression. By evaluating these models, the project sought to identify the most effective approach for precise brain age prediction, thereby contributing valuable insights to the field of medical imaging and neurological diagnostics.