Artificial Intelligence Research Catalyst Fund awards 20 grants totaling $1 million
The University of Florida has awarded 20 faculty teams $50,000 each from UF Research's Artificial Intelligence Research Catalyst Fund to pursue a wide range of AI-related projects. The researchers will utilize the university’s world-leading computing capabilities to analyze vast amounts of data and predict solutions to health, agriculture, engineering and educational challenges.
A team of faculty reviewers evaluated 133 proposals from across the university before settling on 20 that the group determined had the most potential for elevating UF’s AI research profile.
The projects will leverage the university’s new computing capabilities, which are being developed through $60 million in gifts from alumnus Chris Malachowsky and NVIDIA, the company he co-founded.
“As part of UF’s push to become a national leader in artificial intelligence research and education, the AI Research Catalyst Fund was created to encourage multidisciplinary teams of faculty and students to rapidly pursue imaginative applications of AI across the institution,” said David Norton, UF’s vice president for research. “We anticipate that this seed funding will position these teams to pursue additional funding from government agencies and industry.”
Among the projects are one to use AI to identify biomarkers that will facilitate the early detection of Alzheimer’s Disease.
“By 2030, over 100 million people are expected to be living with Alzheimer’s Disease,” said Juan Claudio Nino, a professor of materials science and engineering, and principal investigator on the project. “Development of quantitative brain biomarkers to help early-stage detection, diagnostic precision, and guide intervention in Alzheimer’s is essential.”
Nino is working with Marcelo Febo, an associate professor in psychiatry and neuroscience, to use AI to analyze functional magnetic resonance images of healthy and diseased brains to identify clues to the onset of the disease.
Emre Tepe, an assistant professor of urban and regional planning, and Abolfazl Safikhani, assistant professor of statistics, will be using machine learning to track past and present land use patterns in Florida to simulate future impacts of anticipated changes in land developments.
“This model can also be easily applied to a wide spectrum of research areas such as traffic forecasting, urban energy consumption and disease spread,” Tepe said.
Another team will use machine learning to mine a large dataset of student responses to math problems on UF’s Algebra Nation platform to help teachers identify academically at-risk students even if they are learning remotely.
“We hope to use machine learning techniques to analyze big data to automatically detect students’ emotions and engagement factors, two of the most important factors influencing online students’ learning outcomes,” said Wanli Xing, an assistant professor in the College of Education’s School of Teaching and Learning.
The team will also be looking for ways to ensure that AI tools used to identify at-risk students do not unfairly reinforce gender, race and other inequalities.
“For example, a student course grade prediction model is twice as likely to incorrectly predict African-American students as having a high risk of failure compared to their Caucasian counterparts,” said G. Bahar Basim, a professor of materials science and engineering and co-principal investigator with Xing. “This can result in over-intervention and other undesirable consequences for African-American students.”
Another project led by Alina Zare, a professor of electrical and computer engineering, and Peter DiGennaro, assistant professor of entomology and nematology, will pair UF’s large dataset of nematode images with Zare’s research on automated plant root analysis to create a new way of quickly identify the agricultural pests in the soil.
“Effective management requires accurate parasitic nematode identification,” DiGennaro said, “but human-based identification requires years of intensive training. Developing a machine learning algorithm to identify and quantify nematode species could revolutionize parasitic nematode identification services, increasing speed and accuracy of recommendations to farmers.”
“Artificial intelligence is accelerating our ability to develop solutions to complex problems previously viewed as intractable,” Norton said. “With these investments in our researchers, we are accelerating UF’s impact in areas that benefit our state, our nation and the planet.”