By Jill Pease
The University of Florida College of Public Health and Health Professions is pleased to announce the 2024 recipients of the college’s Ph.D. Fellowships in Artificial Intelligence. Now in its second year, the program fosters doctoral students’ training and research in AI and encourages out-of-the-box ideas.
In the short term, the awards are designed to offer an opportunity for academic growth, professional networking, results dissemination and research support for both mentors and students. The long-term goal is to build national and international recognition of AI innovation within the college’s doctoral programs.
The following Ph.D. students are recipients of 2024 PHHP AI fellowship funding, which is supported by the Robert G. Frank Endowed Professorship.
Yuan Li
Program: Ph.D. in rehabilitation science
Project: Li will develop a data-driven AI-based screening tool that leverages the latest advancements in wearable technology to facilitate personalized screening for sleep-related disorders. The goal is to create a screening tool using multiple variables collected from commercial wearable devices in various settings to generate an easy-to-understand score that can be an early indicator of sleep-related disorders such as obstructive sleep apnea.
Mentor: Hongwu Wang, Ph.D., assistant professor, department of occupational therapy
Dayuan Wang
Program: Ph.D. in biostatistics
Project: With the help of artificial intelligence techniques, Wang will test a series of statistical and computational strategies to improve detection of copy number variants, a type of genetic variation that involves duplications or deletions of specific genomic segments. These variations can play a significant role in the development or progression of diseases, including neurodevelopmental disorders and cancer.
Mentor: Feifei Xiao, Ph.D., associate professor, department of biostatistics
Pei-Yu Wu
Program: Ph.D. in public health, environmental health concentration
Project: Wu will build a machine learning and artificial intelligence-assisted quantitative structure-activity relationship, or QSAR, model to predict multi-organ toxicities of chemicals. The model will be designed to predict potential toxicity without the need to conduct live animal experiments for thousands of chemicals.
Mentor: Zhoumeng Lin, Ph.D., D.A.B.T., C.P.H., associate professor, department of environmental and global health
Honorable mention
Danting Yang, a doctoral student in epidemiology, will receive support to cover conference-related or journal publication fees for an accepted AI abstract or paper. Her mentors are Sai Zhang, Ph.D., and Krishna Vaddiparti, Ph.D., both assistant professors in the department of epidemiology.