Molly Kaipus

Graduate from Colorado Mesa University with a Bachelor of Science in Applied Mathematics, and a minor in Statistics, and actively seeking job opportunities in STEM fields. Proven ability to apply mathematical theory to real-world problems through interdisciplinary projects in statistics, biology, and computer science. Experienced mathematics tutor with a track record of improving student understanding and performance using clear communication, tailored strategies, and deep subject knowledge.

Investigating Biological Pattern Formation Using Reaction-Diffusion Models
Course: Senior Capstone

• Developed and analyzed mathematical models based on Turing's theory and J.D. Murray’s Mathematical Biology to explain animal coat patterns.
• Simulated reaction-diffusion systems in Python to support theoretical results and visual pattern formation.

A Sentiment Analysis of Hotel Reviews
Course: Python Machine Learning

• Built NLP pipelines to preprocess TripAdvisor hotel reviews and classify them as positive or negative using ML and deep learning models (e.g., SVM, LSTM, Transformers).
• Deployed a real-time web application that classifies user-inputted reviews using the top-performing classifier.

Lotka-Volterra Models with Cannibalism
Course: Methods of Applied Mathematics III

• Investigated predator-prey dynamics by modifying the Lotka-Volterra model to include predator cannibalism, based on published ecological studies.
• Analyzed model behavior through numerical simulation using the Runge-Kutta 4 method and explored effects of cannibalism on system stability.

Logistical Analysis of Titanic Data
Course: Correlation and Regression

• Performed logistic regression in R on an anonymized dataset resembling Titanic passenger data, identifying significant predictors of binary outcomes.
• Interpreted results despite ambiguous variable meanings through exploratory analysis and model diagnostics.

Analysis of COVID-19 Mortality Data
Course: Correlation and Regression

• Conducted regression modeling to identify key demographic, economic, and political predictors of COVID-19 death rates across counties.
• Applied model selection techniques (stepwise, AIC/BIC), log transformations, and multicollinearity checks (GVIF) to refine and compare linear models.