A research team from UMBC and the University of Maryland School of Medicine (UMSOM) has received a Rapid Response Research (RAPID) grant from the National Science Foundation to detect COVID-19 infections earlier through computing. Aryya Gangopadhyay, professor of information systems at UMBC, is PI on the grant. He explains that this work will use machine learning to improve the speed and accuracy of COVID-19 diagnosis, helping to limit spread of the disease.
Developing highly accurate screening tool and synthetic datasets
Through the year-long grant totaling approximately $150,000, researchers will design, build, and train deep neural networks to detect cases of COVID-19. Gangopadhyay says this approach has a proven track record. Deep neural networks have already been used effectively in diagnosing pneumonia.
This research will combine the power of AI and medical imaging to solve a critical problem in infectious diseases with pandemic potential, including COVID-19 and others, explains Gangopadhyay. “Our focus for this research is COVID-19. The research is an example of multidisciplinary data science that combines expertise in different fields, such as medicine and computational research,” he says.
Research team
Gangopadhyay notes that the research will benefit from the infrastructure, research strength, and industrial partnerships of UMBC’s Center for Accelerated Real Time Analytics (CARTA).
UMBC’s team includes Yelena Yesha, distinguished professor of computer science and electrical engineering (CSEE) and director of CARTA; Yaacov Yesha, professor of CSEE; Phuong Nguyen, research assistant professor of CSEE; David Chapman, assistant professor of CSEE; and computer science Ph.D. students Sumeet Menon and Jayalakshmi Mangalagiri. Eliot Siegel, professor and vice chair of radiology at UMSOM and chief of imaging service at the VA Maryland Healthcare System, will contribute to the research.
The team plans to work quickly and hopes to have some results available by August. Then, the researchers will work with clinicians to validate their models and data to ensure that the tools are highly accurate in predicting COVID-19.
“We are very committed to this work,” Gangopadhyay says, recognizing the incredible potential of the research.
Collaboration during public health crisis
This is UMBC’s second NSF RAPID Grant responding to COVID-19. In early March, UMBC’s Charissa Cheah, professor of psychology, and Shimei Pan, associate professor of information systems, and Cixin Wang, assistant professor of school psychology at University of Maryland, College Park, received a grant to examine the intensified discrimination experienced by Chinese-Americans in the time of COVID-19.
Cheah shared, “Knowledge from this RAPID grant will help educators, health care providers, and policymakers to proactively support targeted marginalized groups and the larger public during future emergency events.”
Both UMBC awards demonstrate the necessity to move quickly and to collaborate strategically on research related to this public health crisis.
Banner image: UMBC’s Information Technology and Engineering building. Photo by Marlayna Demond ’11 for UMBC.
Tags: COEIT, COVIDresearch, CSEE, IS
