Scott Schwartz

Assistant Professor, Teaching Stream


Fields of Study

Areas of Interest

  • Bayesian Analysis
  • Machine Learning
  • Deep Learning
  • Statistics


Never shy for adventure, I dived into a couple years of public school and then started college early after being homeschooled for much of my life.  I took my first courses at the local community college, but eventually transferred to Trinity University in San Antonio where I discovered programming and statistics, and won a National Championship while playing with the soccer team! I left Texas to get a PhD in Statistical Sciences at Duke University in North Carolina, but then returned to my home state where I tried out both sides of the Texas experience, first spending time at Texas A&M in College Station and then the University of Texas at Austin.  My focus during these years had centered on bioinformatics; but, eventually eager for another adventure, I left academia to teach data science in a disruptive education context with an entrepreneurial start up.

This quickly led to a move to New York city and a data science industry position with a heathtech startup, and then a move to Stockholm, Sweden for a position with a fintech startup.  With the advent of COVID, I returned to North America and to academia, first teaching in the new School of Data Science at the University of Virginia before moving to U of T.  

Some recent publications from my bioinformatics collaborations are "Transcription Start Site Context Promoters" (Genome Biology, 2020) and "Photperiodic Response in Switchgrass" (Plant Cell and Environment, 2019), while some of my earlier statistical publications include "Confounding in Principal Stratification" (Statistics in Medicine, 2012), "Dirichlet Process Principal Stratification" (JASA, 2011), and "Birthweight and Censored Gestational Age" (Statistics in Medicine, 2010).