Hi, I'm Quinn Olson

Color-Thing

The Color Flow project is an interactive color flow simulation built with C++ and SFML, inspired by slime mold and ant colony behavior. It features customizable parameters to create diverse patterns. It includes hardware acceleration with CUDA and audio visualization capabilities.

• Customizable color flow patterns
• Interactive controls
• Hardware acceleration with CUDA
• Audio visualization

View on GitHub

Machine Learning Applications in Crime Analysis

As part of a team, I led the research, implementation, and application of various machine learning techniques to analyze crime data. This included:

• LSTM Networks: For predicting future crime trends.
• Clustering Algorithms: To identify crime hotspots.
• Association Data Mining: To find correlations between different crimes.

Key Contributions:

• Developed models for crime trend analysis.
• Enhanced data preprocessing and feature extraction.
• Created visualizations to present findings.

Check out the project site!

Classifying Music Genre with Deep Learning

I independently led a project to classify music genres using Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The model processes audio data at multiple time granularities to enhance accuracy.

Key Contributions:

• Developed a hybrid CNN-LSTM model for music genre classification.
• Enhanced feature extraction techniques for audio data.
• Improved model accuracy through time-series analysis.

Download the detailed paper (PDF)

Warhammer 40k Damage Calculator

A web-based tool that calculates the average damage for Warhammer 40k attacks based on input stats. Unlike other tools that use a brute force Monte Carlo approach, this calculator employs a probability-based method for more accurate and efficient results.

Key Features:

• User-friendly interface for inputting attack and defense stats.
• Probability-based calculations for average damage.
• Efficient and accurate results compared to Monte Carlo methods.

Check it out!