Supporting next generation AI research

By Jill Pease

The University of Florida College of Public Health and Health Professions has launched the Ph.D. Fellowship in Artificial Intelligence program to foster doctoral students’ training and research in AI and encourage 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.

These Ph.D. students are among the inaugural recipients of PHHP AI fellowship funding.

Chandrashekhar, RaghuveerRaghuveer Chandrashekhar
Ph.D. in rehabilitation science
Project: Chandrashekhar will develop a personalized AI-based algorithm to predict physical health and mobility changes among older adults, based on data collected from a portable device that measures toe strength, paired with available individual variables, such as age, gender and health conditions.
Mentor: Hongwu Wang, Ph.D., assistant professor of occupational therapy

Sy, Michael AaronMichael Aaron Sy
Ph.D. in epidemiology
Project: Sy will investigate the use of AI coupled with next generation pathogen genomic sequencing to detect antimicrobial resistance among patients with acute respiratory distress syndrome. Findings could guide drug treatment decisions and improve health outcomes.
Mentors: Panayiotis (Takis) Benos, Ph.D., William Bushnell Presidential Chaired professor of epidemiology, and Simone Marini, Ph.D., assistant professor of epidemiology

Wu, XueXue Wu
Ph.D. in environmental health
Project: Wu will build an AI-based model to predict how long various drugs remain in the systems of different animals used for human consumption, a crucial consideration for both food safety and animal health.
Mentor: Zhoumeng Lin, Ph.D., associate professor of environmental and global health

Yan, XinyuXinyu Yan
Ph.D. in biostatistics
Project: Yan will create an AI-based model to predict multiple tobacco product use among young people at the individual level. The model can also help policymakers at the community level evaluate the potential impact of different policy scenarios and make informed decisions in tobacco control.
Mentors: Xiangyang Lou, Ph.D., research professor of biostatistics, and Ji-Hyun Lee, Ph.D., professor of biostatistics

Zhou, YuanYuan Zhou
Program: Ph.D. in biostatistics
Project: Zhou aims to develop a new foundational AI approach that combines deep learning with functional data analysis, which is a rigorous mathematical theory of data. The method will offer new ways to analyze complex biological data in relation to diseases, for example, by accurately modeling the connections between genetic variations and disease traits.
Mentor: Qing Lu, Ph.D., professor of biostatistics