Professor Jafar Saniie joined the Department of Electrical and Computer Engineering at Illinois Institute of Technology in 1983. He is currently a Professor, Senior Advisor to the ECE Chair, and Director of the Ultrasonic Information Processing Laboratory. Saniie's research activities have been in the area of signal processing and system-on-chip design for ultrasonic imaging, detection, and estimation with both industrial and medical applications. He has been a leader in the area of ultrasonic signal processing and has made profound and steady contributions to this field for the past 3 decades since he began his Ph.D. research work in the School of Electrical Engineering at Purdue University in 1978. At Illinois Institute of Technology, he has supervised the research of 22 Ph.D. students to completion and published more than 185 papers. Throughout the 1980’s, with research funding from the Electric Power Research Institute, he initiated pioneering research in ultrasonic signal analysis and modeling for imaging highly reverberant thin layers. His theoretical modeling for signal classification and pattern recognition laid the foundation for ultrasonic imaging of steam generator tubes used in Nuclear Power Plants and became the practical means for detecting defects, corrosion and volatile material changes. His ground-breaking research work for imaging reverberant layered materials has a broad range of applications including: the detection of thin planar defects in metals, the detection of laminated composite bonds, gap thickness measurements of adhesively bonded metals, and fatigue crack analysis. Over two decades, Professor Saniie has been directing research and advancing the theory and analysis of frequency-diverse ultrasonic imaging. He originated the concept of the Order Statistic (OS) Processor for frequency-diverse ultrasonic flaw detection also known as split-spectrum processing (SSP). SSP flaw detection is now a widely recognized signal processing technique for the ultrasonic imaging of materials. With a research grant from the National Science Foundation for the development of the Ultrasonic Information Processing Laboratory and with 8 continuous years of research funding from the Office of Naval Research, he advanced the theory and statistical analysis of SSP, OS filters, and the Bayes Classifier for both ultrasonic imaging and radar target detection. The results from Saniie’s analyses are the foundation in the development of optimization procedures over a variety of input signals and are applicable to any detection system where the sampled signals can be modeled statistically, such as in radar, sonar and ultrasonic detection systems. Saniie has also extensively studied the microstructure scattering of materials and researched signal processing methods for material characterization using neural networks, homomorphic processing, morphological processing, and time-frequency analysis. He has researched different time-frequency distributions, including wavelet transform, chirplet transform, and Wigner-Ville transform to evaluate the non stationary behavior of grain scattering and to estimate the grain size by estimating the frequency of back scattered echoes as the broadband ultrasonic wavelet propagates within the materials.