Hi, I'm Fisher Munson-Warnken.
A Computer Science student focused on building clean, functional software.
About
I am a software developer, a UX designer, and an aspiring mathematician.
I have taken classes in statistical methods, computer systems, applied linear algebra, and
machine learning.
When I am not studying for school, I am working on various side projects.
Outside of programming and academics, I am a powerlifter, skier, climber, and Brazilian
jiu-jitsu athlete. I love to move my body and spend time outdoors.
I find that balancing technical problem-solving with physical activity improves my
productivity.
I am also an avid reader, movie watcher, and music enthusiast (strictly listening, though I
do play a bit of trumpet).
I am currently working on an arts and culture journal to log works that have inspired me,
featuring a graph implementation with nodes representing bodies of work and edges connecting
the keywords I have used to describe them.
I also enjoy board games such as chess, Magic: The Gathering, and Settlers of Catan, though
any game that requires a certain level of neural recruitment will get me invested.
Selected Projects
Cribs 2024
Mobile app to streamline landlard and tenant interaction. Inspired by the qualms of finding short term housing as a UBC student. Won most likely to be a startup at HackCamp, fall 2024.
View Cribs →
Crash Tested
A machine learning model built to predict the likelihood of an economic recession in Metro Vancouver. Trained using a random forest classifier on Vancouver business data from the past 20 years. The model won second place in UBC's Datathon, fall 2025.
Repository →
Anderson-Darling Normality Analysis
Built statistical software to parse and compute statistical methods on .csv files. Applied Anderson-Darling analysis on user specified datasets to check for nearly normal trends in the given data. Computed quantiles and probabilities derived from normally distributed data. Implemented state save and load methods through JSON configuration.
Minecraft Newsletter Prediction Model
A predictive model used to assess the likelihood of a player on the UBC Minecraft server subscribing to a weekly newsletter. Motivated by the need to identify what types of players were most engaged with the server. Trained the model on various metrics pulled from server API and used kNN algorithm to classify target variable.
Work Experience
Job History
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The Spot on the Dock
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REI Co-op
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Feldman's Bagels