Together with Dr. Piper Jackson, paper accepted at the Canadian Undergraduate Computing Science Conference. Using discrete-event simulation modeling and the MIMIC-III data set, this paper produces a demographic and metadata-only model of patient transfer within hospital wards. The model successfully approximated the underlying transfer dynamics (95.63% accuracy measured using RMSE). The accompanying visualization may be used to examine patient flow.

Together with Dyson Fraser. Paper and associated tools used for analyzing a very large dataset (1.2TB) of Dota2 matches for pattern/metagame extraction. This was presented at the TRU Undergraduate Research & Innovation Conference 2019.

Introduced attendees to domains, DNS, and options for hosting websites and applications. Walked attendees through purchassing a domain name, setting up DNS, email, and static and dynamic website hosting.

Guest lecture in the Software Architecture class at Thompson Rivers University. Introduced students to version control systems placing them in their historical context. Introduced Git in the context of distributed version control systems and demonstrated its basic operations. Discussed online platforms that aid the use of version control.

Introduced participants to Nim, an experimental programming language with advanced metaprogramming and transpilation features.

Introduced participants to the use of Python for basic data analysis. Explained the concepts behind more Pythonic use of looping constructs. Taught basic use of graphing library Matplotlib and data analysis library Pandas as well as Jupyter notebooks in an applied manner. Introduced the topic of concurrency and the Global Interpreter Lock in Python.

Introduced participants to the Model-View-Controller (MVC) design pattern. Compared Django with other MVC or MVC-like frameworks. Led participants through the creation of a basic Django web application.

Simple neural network implementation using the Nim programming language. Intended for educational and demonstrative purposes. Project structured as a set of three packages handling the MNIST-specific components, a general purpose configurable neural network, and the final demonstrative executable.

Selenium-powered scraper for myTRU and TRU's Moodle grades websites. Includes email notification features for grade changes/additions. Not production ready.

Developed an experimental recipe application centered on voice-recognition. Application was developed as a native Android application using Java and the Android NDK. The app employs voice-recognition to allow the user to issue vocal commands to the application. This is indented for hands-free operation while the user is cooking. The application uses Realm as an object storage for its underlying user-submitted data.