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This book reflects recent developments in personalized medicine by the control theoretical community. This provides a comprehensive introduction to multidisciplinary fields like virology, immunology, epidemiology, statistics, machine learning, and control systems. The book here presents a set of 11 chapters introducing how control theoretical approaches can help to tailor therapies for different diseases, in particular, infectious diseases, diabetes, and cancer. Basic knowledge of control theory is helpful though not essential as the book includes a self-contained introduction in each chapter. The book is mainly recommended for graduate students and scientists working in the control field with biomedical applications. In particular, this book provides an effort to couple control theoretical approaches, PK/PD simulations, machine learning, and mechanistic mathematic models to pave the way towards personalized medicine. Leveraging different kinds of data through novel quantitative approaches will guide practitioners in decision making in the following decades.