My name is Drew and I am a senior at the University of Minnesota studying Data Science with a minor in Mathematics. At the University, I am also a student in the University Honors Program (UHP) which has provided many opportunities including taking rigorous courses and completing an undergraduate honors thesis. The thesis will be a culmination of research I will complete towards the end of my undergraduate career. Currently, I am working with Assistant Professor Aryan Deshwal on high-dimensional Bayesian optimization, a domain with many real-world scientific and engineering applications. After finishing my Bachelor's degree, I plan on pursuing a PhD in computer science.

• Languages: Python, MATLAB, R, Julia, C++, SQL, LaTeX
• Libraries: Matplotlib, Pandas, Scikit-Learn, TensorFlow, PyTorch, GPyTorch, BoTorch, JuMP
• Tools: Git, GitHub, Weights & Biases, Docker, Tableau, Snowflake, PostgreSQL, Microsoft Excel

 🏅 Microsoft Azure Data Scientist Associate (in progress)

 🏅 Microsoft Azure Data Engineer Associate (in progress)

 📑 Resume (last updated September 24, 2025)

 📑 Curriculum Vitae (last updated September 24, 2025)

• Bayesian Optimization (bayesian-optimization)
  This repository contains my work with Bayesian optimization and related topics. It consists of notes and tutorials on various topics, interactive examples, and from-scratch implementations.
• More coming soon!

• Predicting Residential Structure Burn Status Post-Wildfire from Aerial Images (wildfire-structure-damage-detection)
  This repository contains my group's work to develop machine learning architecture that can successfully predict the burn status of residential structures following a wildfire using aerial imagery. This work was completed as part of the course requirements for CSCI 4521: Applied Machine Learning at the University of Minnesota in Fall 2025.
• Migration in Mozambique (mozambique-migration-analysis)
  This repository contains my group's work to analyze census microdata from Mozambique collected in 1997, 2007, and 2017. Our goal was to classify respondents as having migrated in the past 1- and 5-year windows using classification models and association analysis. This work was competed as part of the course requirements for CSCI 5523: Data Mining at the University of Minnesota in Fall 2025.