College of Engineering professors receive $452,178 research grant from the National Institute of Health for development of statistical learning tool

Associate Professor Hua Fang (Computer & Information Science) and Associate Professor Honggang Wang (Electrical & Computer Engineering) to use innovative tool to analyze studies on diets and health implications

Associate Professors Hua Fang and Honggang Wang
Associate Professors Hua Fang (left) and Honggang Wang (right) are the recipients of a $452K NIH award to develop an innovative statistical learning tool to analyze studies on diets and health implications

College of Engineering faculty members Associate Professor Hua Fang (Computer & Information Science) and Associate Professor Honggang Wang (Electrical & Computer Engineering) have received a $452,178 research grant from the National Institute of Health for their project titled VIP: Visual-Valid Dietary Behavior Pattern Recognition for Local-National Trials.

Fang and Wang will collaborate with national research institutes like the Fred Hutchinson Cancer Center, University of Minnesota, the University of Alabama at Birmingham, and the University of Massachusetts Medical School to conduct a nationwide project. The project will harmonize dietary data across four longitudinal National Institute of Health randomized controlled trials in Massachusetts and 44 clinic centers across the U.S. spanning up to 30 years.

The researchers acknowledge that chronic diseases like diabetes, obesity, and cardiovascular issues are among the most common, costly, and preventable of all health problems in the U.S., but a healthy diet can reduce the risk of these ailments. While there are many recommendations put forth by U.S. agencies, a noticeable gap exists between dietary pattern literature and the fast-growing statistical learning field.

By developing an innovative statistical learning tool, Fang and Wang will examine longitudinal trial data and identify trajectory patterns that characterize patients’ complex engagement and cognitive response to diet-based treatment effects.

Fang and Wang believe their work will contribute to the infrastructure for diet-related studies, advance pattern- recognition methods, assess dietary health risks and help scientific communities and the public compare local and national diet-quality guidelines. They also hope the project will create a data management platform that supports near-real-time pattern analyses and adaptive interventions for wider use.



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