  In this paper, we describe a research agenda for deriving design principles
directly from data. We argue that it is time to go beyond manually curated and
applied visualization design guidelines. We propose learning models of
visualization design from data collected using graphical perception studies and
build tools powered by the learned models. To achieve this vision, we need to
1) develop scalable methods for collecting training data, 2) collect different
forms of training data, 3) advance interpretability of machine learning models,
and 4) develop adaptive models that evolve as more data becomes available.
