Five doctoral students representing four departments in the University of Florida College of Public Health and Health Professions are the 2026 recipients of the college’s Ph.D. Fellowships in Artificial Intelligence.
The awards are meant to foster students’ training and applied research in AI, professional networking, results dissemination, and travel and research support for students and their mentors. In the long term, the program’s goal is to build national and international recognition of AI innovation in the college’s doctoral programs.
The following Ph.D. students are recipients of 2026 PHHP AI fellowship funding, supported by the Robert G. Frank Endowed Professorship and other college resources.
Ibsa Ahmed
Program: Ph.D. in public health, One Health concentration
Project: “AI-Enabled Learning Digital Twin for Precision Child Health Prevention”
Ahmed’s work aims to close a research gap in public health by using digital twins — virtual replicas of real systems that continually update as new data becomes available — to predict long-term child health outcomes linked to Campylobacter transmission and targeted prevention. While the research will take place in rural eastern Ethiopia, campylobacter is a leading cause of foodborne illness in the U.S., as well. His long-term goal is improving understanding of Campylobacter infection everywhere, while simultaneously advancing the science and use of digital twins to improve child health in Florida and beyond.
Mentors: Sarah McKune, Ph.D., associate professor and interim chair, Department of Environmental and Global Health; Zhoumeng Lin, B.Med., Ph.D., associate professor, Department of Environmental and Global Health
“I’ve already begun to see the impact of this award firsthand,” Ahmed said. “It has opened doors to identify new collaborations across UF colleges, including the College of Medicine and the UF/IFAS Global Food Systems Institute, in ways I didn’t anticipate happening so quickly. That kind of interdisciplinary connection is exactly what I feel is necessary to tackle the complex challenges we face, and this fellowship is making the first step possible. It is also advancing our shared mission to leverage AI across disciplines to address complex societal problems, and watching that come to life in our active projects has been truly exciting.”
Tyler Busch
Program: Ph.D. in clinical and health psychology
Project: “Artificial Intelligence for Personalized Brain Stimulation in Heterogeneous Mild Cognitive Impairment”
Busch’s research uses brain imaging and AI to understand and predict how different subtypes of mild cognitive impairment in people at risk for Alzheimer’s disease respond to transcranial direct current stimulation. Using machine-learning models to combine brain imaging data with clinical profiles, Busch hopes to predict individuals’ neural response to stimulation, supporting more personalized treatment for people at risk for Alzheimer’s disease and related dementias.
Mentor: Aprinda Indahlastari Queen, Ph.D., assistant professor, Department of Clinical and Health Psychology
“As a Ph.D. student in clinical and health psychology with a neuropsychology concentration, formal training in AI is not typically embedded in my field, but is essential for advancing precision approaches to cognitive aging,” Busch said. “Collaborating across PHHP to develop interpretable, clinically grounded models that guide individualized neuromodulation and improve functional outcomes in older adults is directly aligned with my research and clinical goals.”
Xinyue (Clover) Chen
Program: Ph.D. in public health, environmental health concentration
Project: “Development of an Artificial Intelligence-Integrated Physiologically Based Pharmacokinetic Modeling Framework to Predict Drug Release and Tumor Delivery from Nanocarriers with Representation Correction for Comprehensive Cancer Treatment”
Chen seeks to improve prediction of drug behavior from nanoparticle-based cancer therapies before they reach clinical trials by integrating AI-based prediction with traditional mechanistic modeling. Better predictions could lead to faster access to effective treatments, particularly for cancers with limited therapeutic options. Chen’s fellowship is partially funded by the UF Cancer Institute.
Mentor: Zhoumeng Lin, B.Med., Ph.D., associate professor, Department of Environmental and Global Health
“I am truly grateful to join a community of scholars who believe AI can transform public health,” Chen said. “What excites me most is the translational potential of this work. Insights from these models could meaningfully inform drug development and reduce the gap between laboratory findings and patient benefit. I also deeply value the opportunity to learn from peers across disciplines. Ultimately, that cross-pollination of ideas is where innovation genuinely happens.”
Ruoxuan Wu
Program: Ph.D. in biostatistics
Project: “Statistical and Artificial Intelligence Methods for Heterogeneous Causal Effect Identification using Mendelian Randomization”
Wu will develop novel statistical and AI methods to identify causal mechanisms for sepsis, a complex condition that is a leading cause of death and critical illness worldwide. Wu’s approach will identify patient subgroups with varying risk profiles with the goal of broadening understanding of the genetic pathways underlying human diseases and improving precision medicine and population health.
Mentor: Feifei Xiao, Ph.D., associate professor, Department of Biostatistics
“As a Ph.D. student, I am especially interested in using AI-driven statistical methods to better understand complex health problems and support more rigorous scientific discovery,” Wu said. “I’m excited to be part of the AI Fellowship program because it provides a strong interdisciplinary community where I can learn from researchers across fields and build collaborations that connect methodological innovation with real public health challenges.”
Tiancheng Zhou
Program: Ph.D. in epidemiology
Project: “Development of an Artificial Intelligence Framework for Mental Health Outcome Prediction in People Living with HIV”
Zhou aims to develop advanced AI models to predict risk of depression in people living with HIV by comparing two approaches — traditional machine learning to analyze medical data and large language models to convert medical records to patient narratives — to determine which approach is more accurate and useful for doctors in real-world settings. The research could lead to more personalized and timely mental health care for people living with HIV, improving quality of life and ability to manage their condition.
Mentor: Simone Marini, Ph.D., associate professor, Department of Epidemiology
“I’m especially excited to be part of a program that fosters interdisciplinary collaboration in AI and public health,” Zhou said. “The opportunity to engage with other fellows and faculty working on diverse AI applications is incredibly valuable, and I look forward to contributing to impactful, real-world healthcare solutions.”