Apr 12, 2025 · 1 min read
Motivating Problems in Math —
What are mathematical achievements that are useful for motivating the study of mathematics?
Sep 19, 2022 · 3 min read
A Regularization Proof —
Investigating behavior of a function minimum as we add regularization.
Feb 4, 2020 · 1 min read
On NumPy Multithreading —
Two notes. First, numpy supports multithreading, and this can give you a speed boost in multicore environments! Second, multithreading can hurt performance when you're running multiple Python / numpy processes at once.
Oct 25, 2019 · 2 min read
Fast Hierarchical Clustering Using fastcluster —
O(N^2) routines for the most commonly used types of clustering.
Sep 28, 2019 · 4 min read
Software Engineering Tools Across 4 Languages —
Over the past two years, I've found myself getting more interested in abstract math as well as software engineering. Compared to machine learning, which can be a pretty heuristic science (think about the process of writing tests for machine learning code), both math and computer science tend to be built on modular components, each with a precise, testable description.
Jan 22, 2019 · 9 min read
Subtle Observations on Range Queries —
For my current research, I've had to read Kelleher et al.'s excellent msprime paper (2016) for simulating genetic sequences under the coalescent with recombination. A small trick that is used in their algorithm is the data structure of a Fenwick tree or binary indexed tree. Since I also have a side interest in competitive programming (mainly through USACO and Project Euler), I took a bit more time to learn this data structure.
Oct 24, 2018 · 8 min read
Missing Heritability and Microaggressions —
Missing heritability is like microaggressions: many seemingly insignificant effects can add up.
Jul 10, 2018 · 14 min read
Random Graphs and Giant Components —
This post will introduce some of the ideas behind random graphs, a very exciting area of current probability research. As has been a theme in my posts so far, I try to emphasize a reproducible, computational example. In this case, we'll be looking at the "giant component" and how that arises in random graphs.
Apr 4, 2018 · 8 min read
Distributions with SymPy —
Any good statistics student will need to do some integrals in her / his life. While I generally feel comfortable with simple integrals, I thought it might be worth setting up a workflow to help automate this process!
Jan 30, 2018 · 10 min read
Clustering with K-Means and EM —
K-means and EM for Gaussian mixtures are two clustering algorithms commonly covered in machine learning courses. In this post, I'll go through my implementations on some sample data.