In recent years, there has been a proliferation of methods for detecting changepoints (also known as breakpoints or structural breaks) in data streams. This surge has been driven by the wide range of applications where changepoint methods are needed, including genomics, neuroscience, climate science, finance, and econometrics, among others. This course serves as an introduction to multiple changepoint detection methods.

This course will first address the simpler task of detecting a single changepoint in the mean of a univariate data stream. This is crucial for understanding several state-of-the-art approaches designed for detecting multiple changepoints. Subsequently, we will delve into the fundamentals of two classical approaches for multiple changepoint detection: (1) binary segmentation and (2) dynamic programming. We will review their statistical and computational properties and explain some of their recent improvements.

We will illustrate the application of these approaches to genomic datasets using the Python/R programming language.

Arnaud Liehrmann

Guillem Rigaill is a senior researcher (aka DR) at INRAE in France. He is a member of the GNet Team at the Institute of Plant Sciences Paris-Saclay and the Stat & Genome team at the Laboratoire de Mathématiques et Modélisation d'Évry. He received his PhD from AgroParisTech in 2010 for the development of algorithms and statistical methods for the analysis of breast cancer data.

His research interests focus on developing efficient algorithms and appropriate statistical methodologies for analyzing high-dimensional genomic and transcriptomic data. He has been developing new models for changepoint detection and proposed inference procedures for these models that are both statistically and algorithmically efficient. He has applied those new tools in many interdisciplinary projects involving cancer and plant biologists, bioinformaticians, and statisticians.