By Erin Jester
In 2020, the University of Florida embarked on a mission to become a national leader in the application of artificial intelligence, with support from NVIDIA. Since 2021, UF has hired more than 100 new faculty to increase education and research in AI. UF’s College of Public Health and Health Professions is home to nine of them.
I am delighted to introduce our new AI faculty in PHHP. Their expertise is crucial for our mission to advance innovation in public health and health professions.
Our new faculty members are pioneering AI methods to tackle some of the most pressing health challenges, advancing both foundational AI as well as applications and clinical studies. Their research leverages AI to predict disease progression, developing non-pharmacological interventions for cognitive decline, analyzing multimodal genomic and clinical data to uncover disease mechanisms, integrating toxicology models, discovering new biomarkers for diseases like Alzheimer’s and cancer.
By expanding our AI infrastructure, training, and expertise, we are better equipped to address complex health issues and drive technological progress that will benefit individuals and communities alike. This enables precise, personalized, efficient health interventions, and transforming health care operations for the next generation of professionals. I am confident that the contributions of our AI faculty will position UF as a leader in AI research and education.
— Mattia Prosperi, Ph.D., FAMIA, FACMI
clinical and health psychology
Joseph Gullett, Ph.D.
My work focuses on the use of multimodal neuroimaging and neuropsychological performance data to predict response to intervention or disease progression with machine learning tools. The exciting part about this is that with accurate prediction models, we can save patients time, money and energy by developing a personalized medicine plan for what intervention might be effective for them based on their brain and cognitive performance characteristics.
clinical and health psychology
Aprinda I. Queen
I use a multi-modal approach that incorporates computational models, neuroimaging and neuromodulation. This is exciting because I am at the forefront of blending these innovative approaches to explore how they intersect and improve intervention outcomes through personalized strategies. For example, I utilize individual neuroimaging data to predict the outcomes of electrical stimulation and apply these findings to populations that have not yet been widely studied.
epidemiology
Takis Benos, Ph.D.
My group develops novel AI methods to analyze multi-modal genomic, clinical and imaging data to investigate disease molecular mechanisms, define disease subtypes and identify effectors of disease onset and progression.
biostatistics
Li Chen, Ph.D.
Currently, I am developing novel AI methods for spatial omics analysis in a large cohort of glioblastoma and lung cancer. If effective, the pilot data from these studies can be used to produce high-impact journal papers and grant proposals for National Cancer Institute and American Cancer Society.
health services research, management & policy
Noah Hammarlund, Ph.D.
My research uses artificial intelligence and quantitative methods to improve health care decision-making and outcomes. A key area of interest is integrating social and contextual factors into predictive models so that interventions can promote equity in care. AI enables us to uncover patterns and barriers that would otherwise remain invisible so that we can support more equitable and effective health care delivery.
biostatistics
Muxuan Liang, Ph.D.
My work focuses on developing statistical and machine learning methods to realize data-driven personalized/individualized decision-making. By efficiently using data, we can accelerate scientific discoveries and improve personalized diagnoses and treatments. This not only enhances our ability to tailor interventions to individual patients but also has the potential to optimize health care outcomes at scale by identifying patterns and insights that might otherwise remain hidden.
environmental and global health
Zhoumeng Lin, B.Med., Ph.D.
Our research is very exciting because our computational models can be used to address many research questions related to toxicology and environmental health. From an animal welfare perspective, our computational models can serve as an alternative to traditional animal experimentation, which will help to replace, reduce and refine animal testing, thereby supporting the 21st-century toxicity testing paradigm.
biostatistics
Feifei Xiao, Ph.D.
Feifei Xiao joined in the Department of Biostatistics as an associate professor with the Artificial Intelligence Initiative in 2022. Her research mainly focuses on the development and application of novel and applicable statistical methods for real data problems arising from modern genetics and genomics data, such as single-cell sequencing spatial multi-omics datasets.
epidemiology
Sai Zhang, Ph.D.
The exciting part of my work is using AI and big data analysis, we will be able to better understand disease mechanisms, discover novel therapeutics to cure the disease and even accurately predict disease before its onset. In this way, people will be able to precisely manage their personal health and conditions with a comprehensive understanding of their own bodies.
