Property testing asks when we can make reliable global decisions about massive objects (such as graphs, probability distributions, and Boolean functions) by inspecting only tiny parts of them. This simple idea has led to a rich theory, with deep connections to combinatorics, complexity theory, analysis of Boolean functions, and sublinear-time computation.

Many classical questions in the field, especially those motivated by Boolean-function query models and complexity theory, are now reaching a mature stage. This workshop will look ahead to new models and problem formulations that arise naturally in data science, statistics, and machine learning.

The goal is to bring together researchers in property testing, learning theory, complexity theory, statistics, and machine learning to revisit classical problems through modern lenses and highlight promising new directions.

Schedule

The schedule below is tentative and subject to change.