With coauthors Fouzi Harrou, Ying Sun, Muddu Madakyaru, and Abdelkader Dairi, Elsevier has published our book, Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches. This book tackles multivariate challenges in process monitoring by merging the advantages of univariate and traditional multivariate techniques to enhance their performance and widen their practical applicability. The book proceeds with merging the desirable properties of shallow learning approaches – such as a one-class support vector machine and k-nearest neighbours and unsupervised deep learning approaches – to develop more sophisticated and efficient monitoring techniques. Many real-world applications are given in waste-water treatment plants, detection of obstacles in driving environments for autonomous robots and vehicles, robot swarm, chemical processes (continuous stirred tank reactor, plug flow rector, and distillation columns), ozone pollution, road traffic congestion, and solar photovoltaic systems. The book is available for purchase here.