artificial intelligence for the public’s health

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.

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.

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.

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.

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.  

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.

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.

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.

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.