ABSTRACT Visual contrast sensitivity represents a core processing ability of the visual system useful for diagnosing a variety of visual disorders. The simplest, easiest, cheapest and most portable way to quantify this ability is by querying directly—delivering appropriate visual stimuli and recording behavioral responses. As with all psychophysical tests, however, estimating contrast sensitivity functions (CSFs) requires serial data acquisition, leading to impractically long acquisition times. While full CSFs can therefore have significant clinical value, quick psychophysical screenings that lack quantitative precision are often used instead for practical reasons. The objective of this proposal is to combine machine learning algorithms and high-quality retrospective CSF data to design tunable diagnostic estimators that can be either quick (for screening) or thorough (for diagnostics), as desired. Our approach will be to train a multidimensional Bayesian active machine learning estimator that has been validated previously for visual field perimetry and audiometric testing—tests that share many properties with contrast sensitivity testing. In aim 1 we will implement and validate a machine learning CSF estimator (mlCSF). This type of estimator accommodates flexible assumptions and allows optimization of data collection for maximizing information gain. In aim 2 we will improve mlCSF efficiency with population CSF data. The Bayesian nature of mlCSF allows for previous empirical findings from a population to refine prior beliefs for new test subjects. Population summaries derived from previous CSF testing procedures will be used to establish informative prior beliefs for the mlCSF estimator. In aim 3 we will extend mlCSF models to include related individual measures. Other visual tests result in measurements that correlate with an individual’s CSF. Relationships among these extra predictors in previously collected visual processing data from the same individuals will be used to refine the prior beliefs of the mlCSF estimator. When complete, this study will have produced a cutting-edge active machine learning framework to estimate probabilistic contrast sensitivity functions using relatively few measurements. The flexibility of this estimator will allow experimenters and clinicians to combine theoretical assumptions and empirical prior beliefs to address a variety of clinical questions ranging from screening to diagnosis with the same procedure.