Payton Jones

Payton Jones

Senior Data Scientist

Pluralsight

About

Payton is a Senior Data Scientist at Pluralsight. He graduated with a Ph.D. in Experimental Psychopathology from Harvard University.

Interests
  • Machine Learning
  • Network Science
  • Psychometrics
Education

  • PhD, Experimental Psychopathology, 2021

    Harvard University

  • BS, Psychology, 2016

    Brigham Young University

Experience

 
 
 
 
 
Senior Data Scientist
Pluralsight
Jul 2020 – Present Remote (Seattle, WA)
  • Work with production level deep learning systems scaled to more than 10 million users
  • Use network science to enhance machine learning algorithms for content recommendation
  • Develop scalable analytic solutions, synthesize results, and present to product leadership
 
 
 
 
 
Data Scientist
Hyka Therapeutics
Nov 2019 – Jul 2020 Boston, MA
  • Developed digital phenotyping & psychometric software
  • Provided solutions for data analyses & visualizations of real-time mental health data
  • Developed recommendation & sorting algorithms for therapeutic content deliverables

Projects

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All Machine Learning Open Source Tools
Advanced Rent vs. Buy Calculator

Advanced Rent vs. Buy Calculator

Interactive open-source calculator and visualizations for making the decision to rent or buy

networktools R package

networktools R package

Tools for psychometric networks. Bridge centrality, MDS plotting, and related functions.

networktree R package

networktree R package

Machine learning based partitioning for psychometric network models

What Statistic? R Shiny

What Statistic? R Shiny

An interactive tool for learning statistical testing methods in R.

Selected Publications

List of all publications (Google Scholar)

Payton Jones (2021). The Neurotic Treadmill: Decreasing Adversity, Increasing Vulnerability?. Doctoral Dissertation.

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Payton Jones (2021). What's in a trauma: Using machine learning to unpack what makes an event traumatic. Journal of Affective Disorders. https://doi.org/10.1016/j.jad.2021.07.066.

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Payton Jones, Patrick Mair, Thorsten Simon, Achim Zeileis (2020). Network trees: A method for recursively partitioning covariance structures. Psychometrika, 85, 926-945. https://doi.org/10.1007/s11336-020-09731-4.

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Payton Jones, Ruofan Ma, Richard J. McNally (2019). Bridge centrality: A network approach to understanding comorbidity. Multivariate Behavioral Research. https://doi.org/10.1080/00273171.2019.1614898.

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