Engineering Professor Yongyi Yang Named to 2021 Class of IEEE Fellows



By Simon Morrow

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Headshot of Professor Yongyi Yang

Illinois Institute of Technology Harris Perlstein Professor of Electrical and Computer Engineering and Professor of Biomedical Engineering Yongyi Yang has been named a fellow of the Institute of Electrical and Electronics Engineers (IEEE). 

According to IEEE, this distinction is reserved for “members whose extraordinary accomplishments in any of the IEEE fields of interest are deemed fitting of this prestigious grade elevation.” Yang was recognized “for contributions to medical image recovery and analysis.”

The distinction is awarded to fewer than 0.1 percent of voting members annually and requires the member to have conducted research with major societal impacts that extend outside their specialty. “It’s quite an honor to have my years of effort and my contributions to the field recognized by both people in my field but also researchers in other fields who voted for me to be chosen as a fellow,” says Yang.

A longtime forerunner working with digital images, Yang developed the formative approach to processing compressed images using an image recovery approach in 1993. He is a pioneer in the use of machine learning for image processing, and his algorithms have been used in post-processing and error-concealment, gaining traction in commercial applications.   

Yang has also turned his image processing expertise to the field of medicine, and his contributions include ongoing efforts to improve image analysis for cancer detection and diagnosis. In close collaboration with clinical practitioners, Yang developed machine learning approaches to automate the identification of early cancerous signs. He has applied this technique to analyzing breast cancer scans, where early diagnosis is critical to patient survival. Yang’s algorithm is now utilized for improving diagnostic accuracy. 

In his ongoing research that he collaborates on with Motorola Professor of Electrical and Computer Engineering Miles Wernick, Yang extends these machine learning techniques to other medical specialties including cardiac and kidney imaging. In July 2020 they received $6 million in National Institutes of Health funding for the projects.