College of Public Health and Health Professions faculty and students are using artificial intelligence tools to improve population health and treatment interventions.
AI in Public Health: A Snapshot in Time
Shena Hays, a multimedia and video specialist at the UF College of Public Health and Health Professions, served as director, writer, editor and executive producer of this documentary short film that was accepted to the American Public Health Association Film Festival.
Leadership
Mattia Prosperi Ph.D., FAMIA, FACMI
Meet some of our AI experts
Li Chen
Zhoumeng Lin BMed, PhD, DABT, CPH, ERT, ATS
Education and training programs
Undergraduate certificate
The Artificial Intelligence in Healthcare and Public Health certificate is offered to undergraduate students across campus. Students will acquire fundamental knowledge, ethical decision-making and applied skills in artificial intelligence applications in public health practice and health care settings.
Graduate certificate
The graduate certificate for Artificial Intelligence in Public Health and Healthcare is intended for graduates seeking to acquire technical knowledge, ethical decision-making and applied skills in artificial intelligence tailored to public health practice and healthcare settings.
PHHP instructors explain how they are integrating artificial intelligence instruction into their classes.
Conference calls for papers and abstracts
call for papers
Adaptation and Evidence-grounding of Generative Interventional Systems (AEGIS)
AEGIS is a workshop at the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2026) in Jeju, South Korea, focused on advancing community standards for generative AI and trustworthy data science. It aligns closely with KDD’s core research themes, including modern AI, scalable data systems, responsible data science, and AI‑for‑Sciences.
daisy goes to italy
6th Data and Artificial Intelligence Workshop (DAISY) June 30, 2026
This year, DAISY will be held internationally with a focus on the foundational AI problem of turning generative models into causally valid, uncertainty aware, and accountable systems for intervention planning, a gap not covered by existing conferences. The application thrusts will encompass decision support in public health, health care and policy, as well as other high-stakes domains.
Artificial intelligence work group
The PHHP’s AI work group is designed to bring together interdisciplinary expertise, foster novel ideas and engage both established and early career investigators. The work group also hosts presentations from renowned experts in health care and public health AI. This seminar series is free and open to the campus community.
Research thrusts
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.
Funding opportunities
FACULTY SUPPORT
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. Applications for the 2027-2028 cycle will open in late 2026.
Professional doctorate student support
Professional Doctoral Fellowship in Artificial Intelligence
The PHHP Professional Doctoral Fellowship in Artificial Intelligence aims at fostering students’ training and applied research in AI, relative to topics in public health and health professions. The long-term goal is to build a legacy of excellence in AI for the PHHP Professional Doctoral programs with national and international recognition. In the short-term, the awards provide an opportunity for completing specific projects in line with the student’s coursework and professional growth. Applications are accepted year-round.
faculty and student support
Generative AI Incubator
This program supports innovative faculty- and student-led projects that explore foundational, applied or ethical dimensions of AI, with an emphasis on interdisciplinary approaches and practical impact. Selected projects receive NavigatorAI credits, with additional support available for conference or publication costs. Projects must demonstrate relevance to public health or health professions, meaningful use of NavigatorAI and feasibility within a 6–12 month period.
Resources
HiPerGator
HiPerGator is a cornerstone of the University of Florida’s artificial intelligence initiative to integrate AI education and research across every academic discipline at UF.
#1 Fastest university-owned supercomputer in the U.S.
#10 Fastest university-owned supercomputer in the world
33M Research requests processed in 2025
Several College of Public Health and Health Professions faculty members and students are leveraging HiPerGator’s computing power to accelerate discovery, support grant-funded initiatives and interdisciplinary collaboration, provide exceptional educational opportunities, and foster innovation in multiple research areas including computational biology, neuroscience, biomedical engineering, precision medicine and predictive disease models. PHHP faculty have invested $250,000 in HiPerGator and have produced nearly 100 scholarly publications using HiPerGator.
“We use HiPerGator for pretty much all our projects. Especially for processing large datasets, including biomedical images, genetics and single cell genomics data. HiPerGator is essential to our work.” — Takis Benos, Ph.D., William Bushnell Presidential Chaired Professor, Epidemiology
“HiPerGator allows me to gain direct, hands-on experience in high-performance computing and large-scale AI model training, which are critical skills in this field.” — Zicheng Zhang, Ph.D. student in Public Health, Environmental Health concentration
The College of Public Health and Health Professions dean’s office provides various opportunities to support acquisition of HiPerGator resources, as well a technical guidance for project design and implementation using the infrastructure. Please see the funding opportunities and contact Associate Dean Mattia Prosperi for tailored solutions.
UF Health IDR ATLAS Health Data Research Platform
ATLAS is a free, publicly available, web-based tool developed by the Observational Health Data Sciences and Informatics (OHDSI) community with the aim to facilitate the design and execution of analyses on standardized, patient-level, observational data. If you are familiar with i2b2, ATLAS can be considered its evolved version, overcoming many of i2b2’s limitations.
AI News
DataFest drew competitors from all over UF to analyze a complex dataset.
Team members delivered several presentations and received multiple awards.
Symposium participants from three Florida universities discussed how to effectively and responsibly advance the technologies’ real-world use in…
