Collin A. P. Joseph
+1 (437) 423-8797 |
collin.joseph.0258@gmail.com |
collinapjoseph.github.io
Summary
Alumni of McGill University and University of Waterloo with a continuous growth mindset and a diverse skill pool. Proven software development expertise from 5 years of industry experience. Highly proficient in C#/.NET, C++ and Python. Extensive experience working on an Agile Development team and using tools including: Git, GitHub and JIRA. Well versed in Object Oriented Programming, Algorithms & Data Structures, and Machine Learning. Rapidly expanding skills to web technologies including: HTML/CSS, JavaScript and SQL.
Experience
Lead Software Developer
Jun 2023 - Mar 2025- Delivered new features for multiplatform (.NET Core & .NET Framework) C# application.
- Led multi-national development team while continuing hands-on development (C# & C++).
- Managed development of 3 key software products using Agile Project Management.
- Liaised with client support team to organize and triage client requests for 3 products.
- Released multiple features and updates for each product per year.
Software Developer
Dec 2020 - Jun 2023- Published and maintained multiplatform (.NET Core & .NET Framework) C# application.
- Implemented module to export 3D models from Autodesk Revit to glTF.
- Built C# REST API client to upload models to cloud rendering platform.
- Deployed C++ tool to generate and manipulate HDR image-based lighting environment maps.
- Enabled native & managed runtime interoperability between modules using C++/CLI.
- Parsed extensive variety of physically-based rendering material properties to glTF format.
- Maintained and updated legacy SketchUp Extension (HTML/CSS & Ruby).
Software Developer
Nov 2019 - Apr 2020- Delivered new features for Computer Vision inference server using C++.
- Prototyped new CNN models and updated existing ones using Tensorflow.
- Automated data pre-processing pipeline and model benchmarking using Python.
- Deployed watchdog client using Python for server monitoring and control.
- Shipped new features and updates to clients using Docker.
- Collaborated with team using version control and an Agile development workflow.
Graduate Student Researcher
Sep 2017 - Oct 2019- Developed ensemble classifier for medical microwave radar scans using Python.
- Performed extensive statistical analysis on radar scans using MATLAB and R.
- Optimized ensemble training using advanced adaptive algorithms.
- Improved predictive performance by proposing new time-frequency decomposition features.
- Streamlined data pre-processing by developing tools in Python.
Signal Processing Algorithm Developer (co-op)
Sep 2016 - Dec 2016- Prototyped an environment classification algorithm for low-resource digital hearing-aids (MATLAB & Assembly).
- Optimized acoustic noise reduction algorithm in Assembly, decreasing power consumption by 20%.
- Leveraged fixed point arithmetic operations to maximize algorithm efficiency on embedded hardware.
- Executed rigorous regression testing on algorithm to ensure performance equaled or exceeded previous iteration.
Signal Processing Algorithm Developer (co-op)
Jan 2016 - Apr 2016- Prototyped a digital equalizer firmware module in Assembly Language.
- Simulated and performed experimental analysis on digital equalizer using MATLAB.
- Executed and documented extensive test procedures for acoustic feedback cancellation algorithm.
- Documented and presented analysis and test results to firmware and software development team.
Signal Processing Algorithm Developer (co-op)
May 2015 - Aug 2015- Evaluated performance of an acoustic noise reduction algorithms using MATLAB.
- Developed functional simulations to evaluate performance of various digital signal processing algorithm configurations.
- Performed experimental analysis on multiple noise estimation methods for enhancement of noise reduction algorithm.
- Developed and executed automated acoustic tests for directional noise reduction algorithm using MATLAB & C.
Teaching Assistant
Sep 2014 - Dec 2014- Tutored engineering students in C# programming fundamentals.
- Presented course content to students on an individual basis and in classes of up to 100.
- Consistently completed administrative and pedagogical tasks ahead of deadlines.
- Cooperated effectively with teaching team of 18 members to ensure smooth running of the course.
Research Assistant
Jan 2014 - Aug 2014- Developed and evaluated a ray tracing simulation tool for radio wave propagation using C++ and MATLAB.
- Improved runtime by over 60% using CUDA parallel computing platform on Nvidia GPU hardware.
- Built GUI using MATLAB to visualize simulation results.
Projects
Blog Website
Oct 2025
A blog website that allows visitors to: view, create, edit and delete blog entries.
Demo: collinapjoseph/web-blog
Stack: HTML, CSS, EJS, Bootstrap, JavaScript, jQuery, Node.js, Express.js
Tabletop RPG Dice Roller
Oct 2025
Make Tabletop RPG dice roles via text input.
Demo: collinapjoseph.github.io/ttrpg-dice-roller
Stack: HTML, CSS, JavaScript, jQuery
"Simon" Memory Game
Sep 2025
Browser-based implementation of the "Simon" sequential memory game.
Demo: collinapjoseph.github.io/simon-game
Stack: HTML, CSS, JavaScript, jQuery
Drum Kit
Sep 2025
Browser-based app where user can play drums using keystrokes or mouse.
Demo: collinapjoseph.github.io/drum-kit
Stack: HTML, CSS, JavaScript
Education
Master's of Engineering, Electrical Engineering
2019
Master's Thesis: mcgill.ca/theses/q237hx09z.
Coursework: Machine Learning, Linear Models, Optimization.
GPA: 3.3
Bachelor's of Applied Science, Electrical Engineering
2017
Co-op program (24 months of industry internships in total).
Coursework: Algorithm Design & Analysis, Adaptive Algorithms, Digital Signal Processing.
GPA: 3.3
Google Prompt Essentials
Aug 2025
Advanced frameworks and techniques for Prompting Generative AI tools.
Methods for using Generative AI to streamline data analysis.
Strategies for using Generative AI as AI Agents to enhance productivity.
Deep Learning Specialization
Sep 2019
Five-course specialization developed by Stanford University's Andrew Ng.
Detailed explanation of essential Deep Learning architectures, including: Deep Neural Networks, CNNs, RNNs LSTMs.
Frameworks for structuring Machine Learning projects and Python-based assignments using Tensorflow and Keras.