I’ve completed way more projects than what I can fit on my resume, so I made this page to host a more complete list of the projects I’m particularly proud of. Writeups and repository links are included wherever possible; you can get a bird’s eye view of both by consulting this website’s writeups tag or visiting my GitHub.

If you’d like to get in touch with me about a career opportunity, you can find me on LinkedIn or email me at lowe [dot] s [at] northeastern [dot] edu. (If you’re a human, you should be able to put that email address together!)

💻📊 Programming and Data Science Projects

🏆 League of Losers • Python, Jupyter, NumPy, scikit-learn, tensorflow, matplotlib, seaborn • Writeup • Repository

• A solo project examining data from League of Legends ranked games, primarily to show improvement since my previous data science project, especially in using tensorflow
• Performed exploratory data analysis, then trained and compared several different neural networks and compared them against each other and linear and quadratic discriminant analysis, a support vector machine, and logistic regression
• Achieved accuracy of >70% despite data covering only first 10 minutes of matches (which are typically 30-50 minutes)
• Writeup discusses how I iterated on each neural network to improve accuracy, why I chose the accuracy goals that I did, and what stuck out to me during my analysis.

🦠 COVID Mortality Prediction • Python, Jupyter, NumPy, scikit-learn, tensorflow, matplotlib, seaborn • Writeup • Repository

• I explored a data set from Mexico’s Ministry of Health with two teammates containing patient comorbidity and mortality. We used seaborn and matplotlib to analyze and visualize patient demographics and find the features that correlated most strongly with patient mortality
• We used principal component analysis to reduce dimensions from 20 down to 3 and trained trained various models including logistic regression, random forest, artificial neural network, linear and quadratic discriminant analysis, k-nearest neighbors, and more to predict patients’ health risk
• Despite the data set missing crucial features, we still achieved >70% accuracy! The team and I finally presented our findings and methodology to the class and earned a full score
• The writeup, Lessons From My First Data Science Project, explains our process, methodology, and findings more deeply, and includes some of the matplotlib and seaborn visuals I worked so hard on!

📜 PaperScraper • Python, PyTest, Poetry, APIs (Reddit, Imgur, Flickr), Git • Writeup • Repository

• I developed a command-line program that allows users to scrape wallpapers from reddit both from their own saved account features and from specific subreddits
• I iterated extensively over several years, and used several different design patterns and technologies (Poetry and pre-commit) to speed up and ease my development process. I also automated tests with PyTest, MagicMocks, VCR.py, GitHub actions
• The current program asynchronously batch downloads images using httpx (a requests alternative that allows async)
• The writeup, The Worst Project I Ever Finished, focuses mostly on the pain points I encountered what mistakes I don’t intend to repeat on my next projects

📻 nprcore.me • Vue.js, JavaScript, Spotify API, Git • Website • Repository

• I pair-programmed a joke website with a friend that compares users’ Spotify activity with National Public Radio’s recommended songs.
• We threw together a quick algorithm to bootstrap a user score before we had enough user data to create an empirical cumulative density function, and used it to rate music tastes
• The site blew up one day (getting over 5,000 hits in 24 hours) after it was recognized by two official NPR Twitter accounts: All Things Considered and NPR Interns
• (Unfortunately, the site domain expired, but we’re rehosting soon)

📐 GitHub Templates

• For standard Python projects, featuring Poetry, pre-commit, PyTest, and GitHub actions.
• For Jupyter notebooks, featuring Poetry, TensorFlow, scikit-learn, matplotlib, etc

(Note: I may eventually replace Poetry with uv and incorporate Docker to streamline setup across different machines)

📝 Articles and Reading Lists

• A Survey of Intermediate Python Features, which just discusses a handful of Python features that I hope will help bridge the gap between beginners and more advanced Python users
• My undergraduate thesis on Elliptic Curves and the Birch and Swinnerton-Dyer Conjecture, written in LaTeX. It also covers some brief SageMath/Python I wrote to check elliptic curve data based on findings from the L-functions and modular forms database
• A short introduction I wrote on greedy algorithms
• Advice for learning mathematics
• Advice for learning how to program
• A list of GitHub repositories I think are cool
• Advice for learning data science (coming soon!)