image: Blake Johnson sits in his lab in Blacksburg. Photo by Peter Means for Virginia Tech view more
Credit: Virginia Tech
While at-home rapid tests for COVID-19 are being widely distributed for public use, it remains a broad goal to improve their reliability. On its website, for example, the Centers for Disease Control and Prevention cautions that even if the test to detect the virus is negative, it does not rule out the possibility of an infection. Serial testing is recommended to increase confidence that the negative reading is not false.
“There is certainly a benefit to at-home rapid tests for COVID-19, but false positive and negative diagnoses continue to pose a critical challenge to future pandemic management,” said Blake Johnson, associate professor in the Grado Department of Industrial and Systems Engineering.
Johnson has been awarded a National Science Foundation Early Career Development (CAREER) Award to develop biosensors with improved measurement confidence and speed. Johnson’s research suggests that using biosensor time series data and physics-based supervised machine learning — a form of artificial intelligence that makes predictions from data — can reduce the probability of erroneous results.
The CAREER award will afford Johnson and his team the opportunity to integrate physiochemical process modeling and supervised machine learning and chemical engineering and apply its methodology across various sensor types, sizes, form factors, and data structures.
“Identifying features of target binding and interfering inputs in biosensor time-series data could significantly improve the reliability and reproducibility of biosensors and biosensor-based controls,” he said.
The CAREER award is the National Science Foundation’s most prestigious award for early-career faculty, encouraging them to serve as academic role models in research and education and to lead advances in the mission of their organizations. To satisfy the award’s requirements, CAREER recipients must find ways to integrate education and research into their projects as well as conduct outreach.
The education goal of this project is to create an interactive Open Course Ware platform to increase education and workforce development opportunities at the interface of health care and data sciences for students in urban-underserved communities.
“These communities have greater health disparities and less access to health care programs,” Johnson said, “and preparing students to practice in underserved areas can benefit the overall well-being of the population.”
Planned activities connected to the award include gaming-driven simulations in biosensing for high school students; a virtual lecture and workshop on data archiving for sensor machine learning for undergraduate students; and virtual lectures on emerging applications of machine learning in the bioanalytical, life, and materials sciences for both high school and undergraduate college students.