For much of my early career, I worked primarily in Python for data science, analysis, and engineering. While I don’t use it as much anymore, it’s a handy tool for quick prototyping and data projects, especially in AI, LLM, and machine learning projects. It’ll come up from time to time, and those projects will show up here.
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Recent Posts
Design of a mucin-selective protease for targeted degradation of cancer-associated mucins
— The rubber hits the road on ColabFold! I hope that's rubber I'm smelling — although it may be the computer. A replication attempt of a protein fold figure in K. Pedram et al (2023). (7 min read)
An Annotated Guide to Hobbyist Protein Folding
— Amaze (or bore) your friends and family by picking up on a hot new hobby that's all the rage — protein folding! An annotated starter to the 2021 paper "ColabFold - Making protein folding accessible to all" (8 min read)
Don't Double Down: Structured Streaming to Wrangle Data
— Stop reprocessing your entire dataset every time new data arrives. A practical guide to Spark Structured Streaming with code examples and cost logic. (9 min read)
Littlefield Simulator: The Art of Laziness
— Efficiency: spending six hours building a web scraper to avoid five minutes of daily work. Automating a business simulation because checking in is for chumps. (8 min read)
Hello, Blog! (Posts by a dummy, for other dummies)
— The obligatory "Hello!" post to suss out what this site will be. Python examples as unnecessary as the post itself. (1 min read)
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— Stable matching, optimal stopping, and Bachelor analysis - visualized with some care. (1 min read)
Utah Office Consult
— People problems are the hardest engineering problems. A consult on thinking out-of-the-box to explain morale problems in small companies. (2 min read)
College Admissions and the Stability of Marriage
— My college presentation on the Gale-Shapley paper, recorded on an iPad, like a true professional. Non-market environments, matchmaking lattices, and gratitude for good professors. (1 min read)
What *are* Supply Chains, Anyway?
— A first foray into network visualization: messy graphs, abject terror. Early data viz experiments searching for supply loops. Bad graphs; interesting questions. (2 min read)