Signal processing is a broad engineering discipline that is concerned with extracting, manipulating, and storing information embedded in complex signals and images. Methods of signal processing include: data compression, analog-to-digital conversion, signal and image reconstruction/restoration, adaptive filtering, distributed sensing and processing, and automated pattern analysis. From the early days of the fast fourier transform (FFT) to today's ubiquitous MP3/JPEG/MPEG compression algorithms, signal processing has driven many of the products and devices that have benefited society. Examples include: 3D medical image scanners (algorithms for cardiac imaging aand multi-modality image registration) , digital audio (.mp3 players and adaptive noise cancelation headphones), global positioning (GPS and location-aware cell-phones), intelligent automotive sensors (airbag sensors and collision warning systems), multimedia devices (PDA's and smart phones), and information forensics (Internet monitoring and automatic speaker identification). At the University of Michigan we view signal processing as a science in which new processing methods are mathematically derived and implemented using fundamental principles that allow prediction of the method's performance limitations and robustness. Signal processing research at UM is developing new models, methods and technologies that will continue to impact diagnostic and therapeutic medicine, radar imaging, sensor networking, image compression, communications and other areas.