The project aims to advance the field of statistical change-point detection by developing novel methods and their associated theory to handle complex data with irregular signals. Unlike the traditional setting in which signals before and after the change point are often assumed to differ by a constant shift, irregular signals refer to the situation when the post-change signal may vary in highly unpredictable ways without any pre-specifiable pattern or structure. This can pose a tremendous challenge on many existing change-point tests, often resulting in notable reductions in their statistical power and increasing their vulnerability to maliciously designed adversarial attacks. By allowing the post-change signals to be irregular and not necessarily follow the standard assumptions as in conventional change-point analyses, the research developed in this project is expected to lead to more robust and next-generation statistical and machine learning protocols and toolboxes with rigorous theoretical guarantees for change-point detection in a wide range of applications. For example, detecting abrupt changes in power grids, attacks in sensor networks, or emerging trends in social networks all require powerful methods for detecting irregular changes. As a result, the research will advance not only the field of statistics but also a range of other disciplines including machine learning and artificial intelligence where data with irregular signals may arise. The research will also be in