Mohammad Al-Ani is a cardiologist for advanced heart failure and imaging (TTE, TEE, CMR, CCTA).
Li Chen’s lab focuses on developing deep learning and statistical methods and software for analyzing large-scale multi-omics data, such as genetics, single-cell genomics, and metagenomics.
Raquel Dias’s lab develops and applies cutting-edge artificial intelligence (AI) methods to examine important research questions across multiple fields of biological sciences, agricultural sciences, and biomedical research.
Reza Forghani’s research focuses on the use of AI to improve health through enhanced AI-assisted diagnostics, as well as non-interpretative AI applications for improving health care processes, quality, efficiency, and sustainability.
Xiao Fan’s primary interest lies in the application of AI to medical, biochemical, and genetic questions. She has completed projects on microRNA target prediction, annotation of intrinsically disordered regions in proteins, and prediction of protein crystallization.
Diego Guarin’s Movement Estimation and Assessment Laboratory at the University of Florida is pioneering the development of a new generation of AI tools for objectively assessing Parkinson’s disease from videos.
Faheem Guirgis’s current and future work is leveraging AI to perform multiomics analysis on ‘omics data from critically patients with Sepsis and ARDS.
Sarah Kim is a practicing pharmacometrician who is passionate about model-informed and AI-powered drug development and is currently leading several computational modeling projects to create and innovate quantitative solutions in healthcare.
Mei Liu’s long-term research goal is to develop innovative (AI/ML) methods to support Predictive, Preventive, Personalized, and Participatory (P4) medicine.
Alicia Mohr is an active surgeon-scientist who cares for critically ill trauma patients and whose research focuses on the interactions between stress, chronic inflammation, bone marrow dysfunction, and an altered microbiome following traumatic injury.
Michael Pizzi is interested in developing machine learning algorithms to identify trends in patient physiologic data to alert clinicians of impending intracranial pressure increases and worsening brain tissue compliance.
Louis Scampavia’s team employs High Throughput Screening (HTS) robotics to accelerate the drug discovery process thorough full automation of large-scale screening experiments.
Jinnie Shin has expertise in application of theory-based natural language processing and learning analytics in education research, and she has focused on investigating how to bridge the gap between psychometric analysis and artificial intelligence in education research.
Yu Wang’s research aims to develop AI/ML-based methods for building reliable and trustworthy cyber-physical systems (such as implanted/wearable devices) for healthcare applications.
James Wynn’s research is focused on the investigation of neonatal-specific innate immune cellular function and inflammatory signaling during sepsis as well as development of novel therapeutic immunomodulatory strategies aimed at improving sepsis outcomes.
Feifei Xiao’s research involves using machine learning methods to build valid predictive models to resolve public health–related problems, such as using support vector machine methods to predict aphasia severity and specific language measures using multi-modal neuroimaging datasets.
The aim of Jie Xu’s research is to develop integrative, fair, and interpretable approaches that can effectively mine insights from big health data, towards accelerating precision medicine.
Rui Yin’s research seeks to improve public health at both individual and population levels, providing insights into biological and medical problems with computational approaches.