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The main theme of my professional career has been making unnecessarily difficult things easier so practitioners can focus on the difficult things that they're good at. My PhD research produced static program analyses to make it easier for software engineers to exploit hardware parallelism, but I also developed a framework to facilitate developing static program analyses. In industry, I built on my program-analysis expertise to build a distributed configuration management service based on a semantic model of system configuration. I have spent most of the last decade helping people make sense of data at scale by working in distributed data-processing and machine learning communities and by developing tools to make it easier to build and maintain machine learning systems.

To get a more specific sense of what I'm interested in lately, check out this IEEE Software article and these conference talks:

I also have a weblog.

Popular repositories

  1. LaTeX dissertation template for the University of Wisconsin--Madison

    TeX 45 35

  2. This is material for a tutorial that @sophwats and @willb developed about machine learning workflows and about running them on OpenShift

    Shell 36 30

  3. silex Public

    Forked from radanalyticsio/silex

    something to help you spark

    Scala 18

  4. experiments with analytics for cycling data

    JavaScript 13 10

  5. the notebook component of a PySpark application to calculate value-at-risk for a portfolio of securities

    Jupyter Notebook 11 9

  6. Materials for a workshop on hash-based sketches

    Jupyter Notebook 11 5

108 contributions in the last year

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Mon Wed Fri
Activity overview
Contributed to willb/willb, NVIDIA/data-science-blueprints, rapidsai/cuml and 3 other repositories

Contribution activity

October 2022

willb has no activity yet for this period.

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