Center for Sustainable Energy at Notre Dame




Yih-Fang Huang

Office:  259 Fitzpatrick Hall of Engineering

Phone:  574-631-5350


Department Website

Group Website

Current Positions
Senior Associate Dean for Education and Undergraduate Programs, College of Engineering
Professor, Department of Electrical Engineering
Concurrent Professor, Department of Computer Science and Engineering
Professor, Wireless Institute

Ph.D., Electrical Engineering, Princeton University
M.A., Princeton University
M.S., Electrical Engineering, University of Notre Dame
B.S., Electrical Engineering, National Taiwan University

Research Interests
Dr. Huang's research interests focus on theory and applications of detection and estimation. The conventional approaches to solving the problems of detection and estimation are typically based on the principles of mathematical statistics. When those problems arise within the context of signal processing or communications, they are referred to as statistical signal processing or statistical communications, respectively. The underpinning statistical principles are, however, applicable to a wide range of problems that include bio-related engineering problems and financial data analysis. Current projects involve the statistical signal processing problems that arise in interference mitigation and management for wireless communications, in distributed sensor networks, as well as those in the development of smart electric power grid technologies. A more interesting project is concerned with Set-Membership Adaptive Filtering (SMAF), which features discerning use of input data and selective update of filter coefficients. For nearly three decades, collaborating with students and colleagues, the Huang research group has developed a number of SMAF algorithms noted in the research community. Those algorithms are viable alternatives to conventional adaptive algorithms such as recursive least-squares (RLS) and least-mean-squares (LMS). Due to the selective update feature, SMAF algorithms result in a modular adaptive filter architecture that forms the basis of event-triggered adaptation that may lead to more resource-efficient distributed sensor networks.