Both articles in the Survey and Review section are about inverse problems. Inverse problems are a little like the quiz show Jeopardy! In Jeopardy! you are given an answer, and you must provide the correct question to that answer. In inverse problems, the “answer” is some measured data from a system, and the “question” is some quantitative information about the system under consideration. In the paper “On Identifiability of Nonlinear ODE Models and Applications in Viral Dynamics” by Miao, Xia, Perelson, and Wu, the systems of interest are governed by a set of ordinary differential equations. A typical problem involves a system with a set of unknown parameters. The system is excited with known input and is observed over time. The problem is to determine the unknown parameters from the observation. The first question that arises in such a problem is whether the unknown parameters can be determined from the measured data. This question has a long history and is referred to as “identifiability.” Mathematically, the issue of identifiability is central as it relates to the issue of uniqueness. To illustrate its importance, consider chemical reactions involving several species. The concentrations of the species are governed by a system of ODEs. Suppose we have access to the concentrations as a function of time over some time window. Can we determine what reactions took place and at what reaction rates? The paper provides a review of modern techniques for addressing the question of identifiability. As examples, the authors considered HIV models and show how these techniques can be used to find out which state variables need to be measured in order to determine all the parameters in the model. The paper by Seo and Woo, “Magnetic Resonance Electrical Impedance Tomography (MREIT),” is about a promising, new imaging modality that is presently at the research stage. MREIT combines two technologies—one cheap and simple, EIT, and one expensive and complex, MRI—to provide a way to map the conductivity distribution inside a body. Conductivity information can be used to complement an MRI or CT image in diagnostics. MREIT is an inverse problem. The equations that model the physical phenomena are elliptic partial differential equations with an unknown coefficient corresponding to the conductivity. Here the measured data are the magnetic field changes caused by the introduction of an electric potential in the scanned object. The inverse problem is to determine the conductivity from the measurements. The paper's main thrust is the development of practical algorithms for obtaining conductivity images. An impressive aspect of this work, a joint effort between a mathematician and a biomedical engineer, is the investment in instrumentation and measurement. Images are obtained from animal and human studies. It is important to note that sophisticated mathematics is crucial to producing meaningful images. Both papers are accessible introductions to the world of inverse problems. They illustrate the important and diverse applications in which such problems arise. They also give a sense of the mathematical issues associated with these problems.