Meet PHHP's newest AI hires
Aprinda Indahlastari, Ph.D., currently serves as a research 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. Read more about her work in this issue.
“AI methods have big potential to help researchers and clinicians by identifying the key factors that drive age-related cognitive decline in older adults and finding an effective way to mitigate these factors,” she said. “AI methods can also help recognize important characteristics of successful interventions that can be used to optimize future interventions aimed at improving cognitive aging.”
Panayiotis (Takis) Benos, Ph.D. will join the department of epidemiology from the University of Pittsburgh. 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.
Joseph Gullett, Ph.D., is a research 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.
Read more about Dr. Gullett’s recent study examining AI’s potential to predict dementia.
Noah Hammarlund, Ph.D., joins the department of health services research, management and policy from the University of Washington. In his research, he 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.
Muxuan Liang, Ph.D., will join the department of biostatistics next year from the Fred Hutchinson Cancer Research Center. 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.
“In many heterogeneous diseases, including cancer, it is uncommon to expect the same treatment effect for a single medication in each individual,” Liang said. “AI technology, such as deep neural networks, provides a great way to characterize the possible heterogeneity of the disease, accommodate interactions between multiple medications, and inform the practical use of medications and other interventions.”
Zhoumeng Lin, B.Med., Ph.D., DABT, CPH, joined the university last summer from Kansas State University as the first faculty member in PHHP hired under UF’s AI initiative. His 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,” Lin said.
Read more about Lin’s latest project in this issue.
Feifei Xiao, Ph.D., arrives at UF next year from the University of South Carolina. She 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.
“Everyone has seen UF’s growth over the past years,” Xiao said. “UF Health is currently ranked as the top health center in Florida and one of the top hospitals in the U.S. I see so many collaboration opportunities in big data health sciences, including biomedical data, health electronic records data, and more. These resources provide a wonderful platform for me to conduct independent and collaborative research and to further establish my research team.”