Meet Our New Faculty

Duncan brings more than 15 years of experience developing and using biomedical ontologies for health informatics, as well as computer programming. These ontologies are used to harmonize diverse biomedical and biological data sources (such as dentistry, environmental metagenomics, and cancer data), detect inconsistencies, and gain new insights through the application of automated reasoning.

As the inaugural director of the Magnetoencephalography (MEG) Lab, Babajani-Feremi’s vision is to integrate machine learning/ deep learning with cutting-edge brain connectomics approaches based on neuroimaging and electrophysiological methods (e.g. MEG, functional MRI [fMRI], and intracranial electroencephalography [iEEG]) for diagnosis and treatment of patients suffering from neurological and psychiatric conditions (e.g., epilepsy and Alzheimer’s disease).

As UF Health chief medical informatics officer and UF College of Medicine assistant dean for clinical informatics, Donnell helps guide UF Health IT strategy and bridges IT and provider communities. In his leadership roles, he provides extensive health informatics expertise to facilitate the use of information, data, AI and other tech tools for clinical operations, medical education and research.

As Vice Chair of AI, Forghani’s vision is to increase research and development in the Department of Radiology, including collaboration with other clinical specialties and scientists at UF. The Radiomics and Augmented Intelligence Lab will lead research activities and pre-deployment evaluation of AI tools for precision diagnostics, streamlined clinical processes, and enhanced health care quality and education.

Liu develops novel machine learning and AI techniques to accelerate risk factor identification and discovery in medicine using EHR data, with clinical applications including prediction of adverse drug reactions, diabetic kidney disease, acute kidney injury and sepsis. Liu serves as Director of Predictive Analytics and Associate Director of Graduate Education in the Department of Health Outcomes and Biomedical Informatics.

An AI imaging scientist, Sarder has 19 years of experience in computational bio-imaging using cutting-edge AI and machine learning tools. Sarder’s research group collaborates nationally and internationally with researchers studying the fusion of spatial omics and imaging data to define normal healthy (“reference”) and study the intersection of reference and disease morphometries in the context of chronic kidney diseases.

Shao’s research focuses on developing artificial intelligence methods in medical imaging to improve patient care. His current research projects include: (a) developing machine learning algorithms for multimodal image registration, multi-object image segmentation, and image-to-image translation, etc.; (b) developing machine learning algorithms for accurate and fast disease diagnosis on medical images; and (c) integrating image processing algorithms into clinical workflows.

Shickel’s research focuses on applications of artificial intelligence and deep learning for enhanced clinical decision support using electronic health records data. He is interested in unifying patient data of multiple modalities for more comprehensive and personalized health representations, and in the human element of clinical AI, including explainability, fairness, causality, and hybrid systems integrating expert knowledge with data-driven methods.

Wong’s research interests include exploring deep brain stimulation from multiple facets including structural and functional connectivity, tractography/ connectomics, electrophysiology, and computational bioinformatics. His lab is focused on applying cutting edge machine learning techniques to better understand the mechanism of action of neuromodulation through large multi-modal datasets.

Xu focuses on developing integrative, fair and interpretable approaches that can effectively mine insights from big health data (e.g., electronic medical records, claims, medical images), and applying the mined knowledge to disease subtyping, predictive modeling, drug repurposing, etc. Her interests are in machine learning and health informatics, with a special focus on metric learning, federated learning, and privacy-preserving techniques.

Yin’s research is AI-driven precision medicine to improve public health outcomes and equity. His lab mainly focuses on developing machine learning approaches to address biomedical problems, including computational modeling (machine learning, statistics) in RNA viruses and rare diseases, genotype-phenotype associations for biomarker identification, clinical informatics and genomics for clinical decision support, and trustworthy AI to advance health equity.

Zielinski, chief of pediatric neurology, studies structure-function interrelationships in developing brain networks. His work bridges neuroimaging and neurobehavioral data, and spans both normative development and a range of conditions from autism to traumatic brain injury. His lab aims to optimize childhood brain health by leveraging high-dimensional feature sets to identify and rescue large-scale brain network dysfunction, dysstructure and decline.

Chen focuses on developing deep learning and statistical methods and software for analyzing large-scale multi-omics data, including but not limited to genetics, single-cell genomics and metagenomics. He is interested in applying the methods developed to study aging and cancer, disseminating software developed for public health researchers to use, and integrating multi-omics data with imaging and EHR data.

Liang develops and applies statistical and machine learning methods to large databases like electronic health records, to help health care providers make decisions based on patient-level information. These may include treatment recommendations based on patient-level information, identifying signals from high-dimensional data, and other novel machine learning techniques with applications to biomarker identification, cancer surveillance, and digital health.

Xiao develops and applies powerful and efficient statistical methods for high throughput genetics and genomics data, especially in cancer research. She works on next generation sequencing based chromosomal structural variation detection, omics data, and neuroimaging data analysis. She is especially interested in exploring the advantages of machine learning methods to handle high dimensional data generated from public health and medicine.

Zhang’s research interests include computational biology, machine learning, genetics, genomics and precision medicine. He develops 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.

Pruinelli is a nursing informatician with more than 10 years of clinical experience in multi-transplant organ coordination and information systems development and implementation. She applies machine learning methods to longitudinal heterogeneous clinical data to investigate the trajectory of complex disease conditions and health outcomes. Her work aims to identify problems and target interventions toward better patient outcomes.

Hasan’s research facilitates appropriate use of complex methods (e.g., cutting-edge AI, machine learning, statistical modeling, management science techniques) for analyzing large-scale longitudinal healthcare data. He is specially interested in developing (i) fair, trustworthy and equitable AI-driven Clinical Decision Support Systems to predict adverse health outcomes; and (ii) prescriptive decision analytic models to improve medication adherence and effectiveness of personalized interventions.

Jiao’s research focuses on investigation of precision medicine that is both applicable for specific diseases and affordable, through the use of advanced study design (i.e. adaptive treatment strategy), cutting-edge statistical methods, and machine learning approaches adopting longitudinal Big Data. As a pharmacoepidemiologist, he has experience in multiple therapeutic areas with a focus on cardiology and pulmonology.

Kim envisions developing AI-assisted imaging analysis and informatics tools that will accelerate the analysis of imaging data and provide insights into better understanding of disease progression. Ultimately, the AI-based tools will help in the rational design of disease prevention and treatment strategies. She is currently focusing on optimizing clinical trial designs for Duchenne muscular dystrophy and type 1 diabetes.

Li’s research interests lie at the intersection of deep learning, drug discovery, and precision medicine, with a special emphasis on AI-driven drug discovery. His work aims to develop innovative AI algorithms to expedite the discovery of novel functional molecules that lead to potential drug candidates and to optimize and automate real-world drug discovery and design pipelines.

Rouhizadeh develops machine learning and natural language processing methods with applications to clinical and pharmaceutical outcomes and public health, focusing on three major goals: (a) identifying signs, symptoms, diseases, disorders, and medications from unstructured electronic health records, (b) detecting signals of neurological disorders affecting children and the elderly, and (c) computational models for identifying social and behavioral determinants of health.

Seabra is developing, in collaboration with Dr. Chenglong Li, applications that teach AIs to autonomously design, screen and optimize molecules that attack selected disease targets. The process can speed up the drug discovery process by suggesting molecules to be synthesized and tested with improved chances for success.

Gullett’s 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.

Hammarlund 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.

Indahlastari uses multimodal neuroimaging and machine learning enhanced computational models to improve the efficacy and reliability of treatment outcomes from non-invasive brain stimulation methods such as tDCS. Her current projects include predicting electrical current dose in stimulated brain regions, identifying sources of inter-individual variability in treatment outcomes, and investigating potential mechanisms that contribute to physiological changes caused by electrical stimulation.

Lin develops and applies computational technologies, including AI and physiologically based pharmacokinetic (PBPK) modeling for environmental chemicals, nanoparticles and drugs in animals and humans to address research questions related to nanomedicine, food safety and chemical risk assessments. His long-term goals is to develop AI-assisted computational models to support a “One Health” approach for decision-making in human, animal and environmental health.

Kim, as a veterinarian, focuses on developing novel diagnostic and therapeutic applications to improve both human and animal health using artificial intelligence. His interest is to define the underlying mechanisms of naturally occurring cancers in animals and humans. By integrating sophisticated disease modeling using iPSC, bioprinting, and genome engineering, he creates AI-implemented tools to revolutionize comparative and translational medicine.