The doctoral degree, i.e. the Ph.D., is primarily intended for students desiring a career in research andor collegiate teaching. The focus is on advanced ECE topics, on learning to perform research and to write research papers, and on making fundamental new contributions to an ECE topic. Students take advanced course work and write a doctoral dissertation, also called a thesis. Program Overview: Students newly admitted to a doctoral program are classified as precandidates. Upon entering a doctoral program, there is a Ph.D. qualifying process, normally completed during the first two years. After all requirements except the dissertation are completed, students become candidates. Students entering the ECE doctoral program with a bachelor’s degree typically become candidates in the third year and and are strongly encouraged to complete the degree within five years. Such students ordinarily complete the requirements for a master’s degree along the way and receive this degree in addition to the Ph.D. A masters thesis is optional. Students who enter the ECE doctoral program with a master’s in the field of their program typically become candidates in their second year and are strongly encouraged to complete the degree within four years. Such students are not ordinarily eligible to receive an additional master’s degree. 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 reconstructionrestoration; adaptive filtering; distributed sensing and processing; and automated pattern analysis. From the early days of the fast fourier transform (FFT) to today’s ubiquitous MP3JPEGMPEG 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. Specialties: Compressive Sampling and Sensing; Computational Imaging and Inverse Problems; Data Science; Machine Learning, deep learning, convolution neural network (CNN) methods; Random matrix theory and applications; Signal Processing for Sensing and Sensor Networks; Source Coding and Compression; Statistical Estimation and Learning.