• I'm a machine learning engineer and software developer, and I've recently completed a Master's degree in Data Science and Analytics at Toronto Metropolitan University (formerly Ryerson University).
• I studied Physics in undergrad and finished with the highest average across all faculties in my school, earning a Governor General's Silver Medal.
• My thesis was an investigation into the state-of-the-art few-shot learning methods for text classification. We compared the performance of Hugging Face transformer models and few-shot learning algorithms when applied to training data sets with limited labeled examples.
• In my two year Master's degree I have first authored four publications: two in reinforcement learning and two in natural language processing using few-shot learning. You can check out my Google Scholar to view some available pre-prints/publications.

• I currently work as a machine learning engineer, managing projects from conception through to deployment. In this process I handle
  • Problem conception
  • Problem elaboration
  • Model conception and design
  • Model implemention
  • Data collection, feature engineering, data labeling (including few-shot labeling)
  • Model training
  • Model deployment (i.e., ML Ops), especially via AWS
  • Monitoring of deployments
• In addition to my machine learning work, I also act as an engineer to create user interfaces to create environments where deployed models can be tested by key stakeholders, clients, and by internal staff (often client-facing).
• For a more formal look at my work and education history, you can checkout my LinkedIn Profile.

I've engaged in a lot of projects over my 5+ years of development. Unfortunately, many of my worthwhile projects are contained in private repositories. While not all of them can be made public, I've been slowly extracting code that I can share with the public.

• A subset of the code from my Master's thesis is available here. This code is used for training NLP models with access to a limited amount of training data.
• You can checkout this repository for some of my work on using linear programming for reinforcement learning. Our work ultimately resulted in a journal publication, so I've added a small sample of the code there.

I'm extremely open to learning new systems and languages. I'm a fluent Python developer, but I have a well-established track record of learning new languages with a quick turn around:

• I was formally trained in Java in undergrad, and learned Kotlin for my own app development project in 2020.
• I learned Free Pascal to complete my undergraduate thesis.
• I've been solving problems with Rust since 2020.
• I learned C to add optimizations to my reinforcement learning projects, as the Python APIs for the linear program solver that we use are slow for our applications.
• I learned and used NodeJS, Angular, and Javascript in a contract web development and machine learning project to introduce a front-end for clients to manually label training data for model development.