artificial intelligence for the public’s health

Researchers in the College of Public Health and Health Professions are using artificial intelligence tools to improve population health and treatment interventions.

research themes

Applied AI

Studies led by faculty in both public health and health professions disciplines explore real-world health issues, including the outcomes of pharmaceutical treatments in large populations, the effects of re-purposing drugs for other health conditions, the impact of environmental contaminants on the risk of diseases, such as cancer, and the use of assistive technology to improve daily life for older adults and people with disabilities.

Ethical AI

Scientists are developing fair and equitable models that not only recognize social bias and health disparity, but also can be acted upon in interventions. Examples include increasing access to care for vulnerable and underserved populations and reducing stigma in order to improve quality of life for people living with HIV.

Interdisciplinary AI

Research across disciplines includes fusing molecular epidemiology and deep learning methods to track and curb transmission of infectious diseases, as well as AI-empowered neurocognitive research.  

Methodological AI

Methodological approaches include advancements in machine learning and “causal AI” with strong biostatistical foundations, such as efficient multi-omics big data analysis, deep propensity networks, automated learning of causal effects from large-scale electronic health records, multi-site large clinical trials, and Bayesian dynamic trials.

PHHP AI Leadership

Mattia Prosperi
Department: College of Public Health and Health Professions Dean's Office

Mattia Prosperi PhD, FAMIA

Professor And Associate Dean For AI And Innovation
Phone: (352) 273-5860

AI hires

Aprinda Indahlastari, Ph.D.

Dr. Indahlastari serves as an assistant professor of clinical and health psychology. Her broader research interests are in optimizing and personalizing existing medical devices through the use of computational modeling, such as machine learning and finite element methods, with the goal of achieving precision medicine that is tailored to each person.

Indahlastari, Aprinda

Panayiotis (Takis) Benos, Ph.D.

Dr. Benos is the William Bushnell Presidential Chaired Professor in the department of epidemiology. His group works on the intersection of machine learning, computational biology and systems medicine. The ultimate goal of the group is to identify risk factors and mechanisms affecting aging and contributing to the onset and progression of chronic diseases and cancer. They develop and use probabilistic graphical models and other machine learning methods to integrate and mine high-dimensional, multi-modal biomedical data and to investigate biological processes pertinent to health and disease. The disease focus of the lab includes chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, cardiovascular diseases and alcoholic hepatitis. Other ongoing projects are related to the identification of microbiome contributions to clinical outcomes in critically ill patients and the understanding of the mechanisms of cancer immunoprevention.

Takis Benos portrait

Li Chen, Ph.D.

As an associate professor in the department of biostatistics, Dr. Chen focuses on developing deep learning and statistical methods and software for analyzing large-scale multi-omics data, which include but are not limited to genetics, single-cell genomics and metagenomics. In particular, he is interested in applying the developed methods to study aging and cancer, as well as disseminating the developed software for public health researchers to use.

Chen, Li

Joseph Gullett, Ph.D.

Dr. Gullett is an assistant professor in the department of clinical and health psychology. His research is focused on the use of machine learning methods to predict intervention outcomes and disease progression in older adults with mild cognitive impairment, and the relationship of white matter microstructure with clinical disorders and their associated neuropsychological function.

Joseph Gullett

Noah Hammarlund, Ph.D.

Dr. Hammarlund is an assistant professor in the department of health services research, management and policy. His research merges health economics with innovations in artificial intelligence to investigate the role of social factors in the delivery of healthcare with the goal to better target policy solutions to disparities in health.  

Hammarlund, Noah

Muxuan Liang, Ph.D.

Dr. Liang is an assistant professor in the department of biostatistics. In his research, he applies statistical and machine learning techniques to large databases like electronic health records, to help health care providers make decisions based on patient-level information. These may include decisions about treatment, tailored cancer surveillance strategy and individualized risk prediction.

Liang, Muxuan

Zhoumeng Lin, BMed, PhD, DABT, CPH

As an associate professor in the department of environmental and global health, Dr. Lin’s research focuses on the development and application of computational technologies to address research questions related to nanomedicine, animal-derived food safety assessment, and environmental chemical risk assessment. The long-term goal is to develop AI-assisted computational approaches to support decision-making in human, animal and environmental health.

Zhoumeng Lin

Feifei Xiao, Ph.D.

An associate professor in the department of biostatistics, Dr. Xiao focuses on the development and application of powerful and efficient statistical methods for high throughput genetics and genomics data. Her work includes ongoing projects in cancer, aging and other public health related outcomes, with the goal of providing efficient statistical tools to integrate genetic and genomic data into the practice of precision medicine.

Feifei Xiao

Sai Zhang, Ph.D.

Dr. Zhang is an assistant professor in the department of epidemiology. His research interests include computational biology, machine learning, genetics, genomics and precision medicine. More specifically, he is interested in developing novel machine learning methods (e.g., deep learning and probabilistic graphical models) to decode complex human diseases by reasoning over large-scale genetic (single-cell) multiomic, and clinical datasets. His long-term goal is to build AI systems to assist scientific discovery, clinical decision-making, and personal health management.

Sai Zhang

PHHP Artificial Intelligence Work Group

UF PHHP workgroup

AI Seminar Series

“AI/QI: A virtuous cycle promoting resiliency” presented by Dr. Patrick Tighe on September 29, 2023 at 1 p.m., CTRB 2161-2162 and Zoom. Dr. Tighe is the Donn M. Dennis, M.D., Professorship in Anesthetic Innovation, and Professor, UF Department of Anesthesiology. In-person attendance will be limited to 30 people, which will be determined on the basis of registration timing. Lunch boxes will be provided to in-person participants. The talk will be also be live streamed via Zoom for online participants.


Funding opportunities


PHHP Research Innovation Fund

The Innovation Fund is designed to provide funding to PHHP faculty for pilot/feasibility studies to enhance their opportunity for obtaining extramural research funding. The program is organized around three major themes: artificial intelligence, direct clinical impact and general topics in public health and health professions.

Ph.D. student support

Ph.D. Fellowship in Artificial Intelligence

The PHHP Ph.D. Fellowship in Artificial Intelligence program fosters doctoral students’ training and research in AI and encourages out-of-the-box ideas. The awards are designed to offer an opportunity for academic growth, professional networking, results dissemination and research support for students and mentors.

The next Request for Applications will post in 2024.



The college has launched a certificate program for undergrads titled Artificial Intelligence in Healthcare and Public Health. The nine-credit certificate includes three courses: “Higher Thinking for Healthy Humans: AI in Healthcare and Public Health,” “Ethics in AI: Who’s Protecting Our Health” and “Data Visualization in the Health Sciences.”


The college is developing a certificate in Artificial Intelligence Research Methodologies in Healthcare and Public Health for graduate students that will focus on using AI to answer health-related research questions. Once these are fully established, the courses will be developed and submitted to the Graduate Council for approval.


more information


For more information on AI activities at PHHP, contact the college’s dean for AI and innovation, Dr. Mattia Prosperi.

AI at UF Health

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