This is the site for Scott Sievert, a graduate student studying large-scale machine learning at UW–Madison. I think Python is pretty sweet 🐍 and love to ski 🎿!

Blog

I tend to blog about technical topics, including interesting mathematics and cool applications of Python.

All my posts are listed at Blog and organized with tags at Tags. Links to follow this blog are in the footer. I list some of my favorite posts at Favorite posts, and they're marked with a ☆. Here's a random favorite post:

Recent posts

Here's some of my recent posts:

• COVID-19, age and lockdowns

  Aug 9, 2020 in pandemic visualization

  I wrote “Visualization of the COVID-19 infection rates” with two goals: to warn people about the upcoming pandemic and to provide insight into that pandemic.

  The US took precautions within a couple months, and the length and intensity of the precautions has surprised me. Even four months later, individuals generally believe they should take actions to limit the spread of COVID-19. This includes wearing a mask and working remotely if possible.

  But are these precautions justified? There’s no harm done if everyone gets an benign virus. Do the data justify mandating wearing masks and closing schools? Let’s look.

  Read on →
  



• Visualization of the COVID-19 infection rates

  Mar 14, 2020 in pandemic visualization

  The Wuhan virus/COVID-19 is spreading through the world. The WHO classifies it as a pandemic (source), and the United States has declared a national emergency because of it (source).

  Data on the infection rate is difficult to find. This post will present some data sources and show some visualizations of the infections. It’ll also provide some tips on how to avoid coronavirus.

  Read on →
  



• Better and faster hyperparameter optimization with Dask

  ☆ Sep 27, 2019 in distributed machine-learning dask

  Dask’s machine learning package, Dask-ML now implements Hyperband, an advanced “hyperparameter optimization” algorithm that performs rather well. This post will

  • describe “hyperparameter optimization”, a common problem in machine learning
  • describe Hyperband’s benefits and why it works
  • show how to use Hyperband via example alongside performance comparisons

  In this post, I’ll walk through a practical example and highlight key portions of the paper “Better and faster hyperparameter optimization with Dask”, which is also summarized in a ~25 minute SciPy 2019 talk.

  Read on →
  



• Launching tasks from workers with Dask

  Jan 4, 2018 in distributed systems dask

  Dask recently surprised me with it’s flexibility in a recent use case, even more than the basic use detailed in a previous post. I’ll walk through my use case and the interesting problem it highlights. I’ll show a toy solution and point to the relevant parts of the Dask documentation.

  Read on →