Five research teams using Artificial Intelligence and Big Data have been awarded a total of $185,000 to conduct COVID-19-related research.
Establishing the awards was the first act of UCF’s new Artificial Intelligence & Big Data Initiative announced this summer. The intent of the program is to seed the development of research projects that use these tools to answer big research questions. The money will help to generate preliminary results that university leaders hope will lead to external research funding.
The competition initially announced that $175,000 would be available to interdisciplinary groups that offered innovative projects. But there was so much interest that the Office of Research, College of Engineering and Computer Science, College of Sciences, and College of Business, who are co-sponsoring the grants, decided to add another $10,000 to the fund. A total of 23 teams put forth competitive applications.
“There were many really interesting and innovative projects that hold a lot of promise in the mix,” says Debra Rienhart, associate vice president for Research and Scholarship. “It was very difficult to select just a handful. We can’t wait to see where these projects go.”
The applications were vetted by a selection team made up of faculty, researchers and administrators with knowledge about AI and Big Data.
The selected teams are tackling a variety of challenges from perception of COVID-19 and new technology to new sensors that may be able to detect and potentially learn how to defeat the virus.
The projects are:
COVID-19 and Medical AI Adoption: The Role of Technology Receptivity
The COVID-19 pandemic has changed many aspects of everyday life. The researchers hope to find out if the pandemic has changed consumer adoption of AI and predictive algorithms in the medical field. They also seek to identify the optimal human-AI interface.
Team leaders: Xin He, Lin Boldt and Sona Klucarova, College of Business
Amount Award: $28,000
Artificial Intelligence-assisted discovery of complex polymeric nanofilms designed to trap and kill the COVID-19 virus for personal protection equipment applications
Quickly developing, identifying, designing and fabricating novel nanomaterials to help produce more effective personal protection equipment to keep healthcare workers and teachers safe during the pandemic.
Team leaders: Ozlem Garibay and Sudipta Seal, College of Engineering and Computer Science
High-Dimensional Analysis for Spectroscopy of Exhaled Gas from COVID-19 Patients
Develop a highly sensitive monitor to test exhaled gases for markers that indicate COVID infection. The monitor would use AI to distinguish the symptomatic and asymptomatic individuals directly based on the spectroscopy of the breath. The instrument could potentially also estimate the concentration of markers in the exhaled air, learn their patterns and test for changes between phases as the disease develops.
Team Leaders: Mengyu Xu, College of Sciences, and Subith Vasu, College of Engineering and Computer Science
Genetically Modified Optical Sensors for Low Cost, High Throughput Detection and Screening for COVID-19
Create optical sensors that allow quick, on-the-spot detection of the virus and capability to measure immune response against the virus. The sensors would provide a fast, easy alternative to current serology tests, which serve to screen for the presence of antibodies to derive insights regarding immune response against the infection.
Team Leaders: Debashis Chanda, NanoScience Technology Center, and Mubarak Shah, College of Engineering and Computer Science
AEM: An AI-Powered and Epidemiology-Informed Modeling System for Accurate COVID-19 Prediction and Analysis
Further develop an analytic tool that takes data about COVID infections from across the nation and uses AI to predict the spread of the disease. The project, which combines math and computer engineering, creates models informed by 10 deep-neural networks. This modeling system has already received some attention from media outlets.
Team Leader: Liqiang Wang, College of Engineering and Computer Science, and Shunpu Zhang, College of Sciences
After teams are done with their work, they will present a report to the vice president of Research within six months of completion. All teams will also present their findings during the College of Engineering and Computer Science Seminar Series and at the COVID-19 Artificial Intelligence & Big Data Seed Funding Research Forum in 2021.