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.
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). (8 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" (10 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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Entries to SOME 1
— 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)
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