Using Machine Learning to Track COVID-19

"Working on a real-life project that will introduce students to how algorithms work in applications with crucial outcomes will provide them with the important skills that can transfer to other areas of computer and data science." As the race for a COVID-19 vaccine continues, Moataz Khalifa, assistant professor and director of Data Education at Washington and Lee University, is involved in an equally promising research project that focuses on a non-invasive, early detection system of the virus. In March, just as the numbers of cases were climbing around the world, Khalifa was invited by Wu Feng, Elizabeth & James Turner Fellow, professor of computer science at Virginia Tech and director of its SyNeRGy lab, to join his research lab to develop a deep-learning algorithm to enhance low-radiation CT scans of people's lungs. Feng's current research was already investigating similar applications in CT scans of brain tumors, and he received two National Science Foundation grants totaling $250,000 to expand his project to work on the COVID-19 early detection system. Currently, the genetic-based RT-PCR tests available to detect COVID-19 rely on swabbing the nasal cavity.