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, bioinformatic and machine learning approaches to identify biomarkers and molecular pathways underlying cognitive decline, Alzheimer’s disease, cardiometabolic diseases (e.g., type 2 diabetes, cardiovascular
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, bioinformatic and machine learning approaches to identify biomarkers and molecular pathways underlying cognitive decline, Alzheimer’s disease, cardiometabolic diseases (e.g., type 2 diabetes, cardiovascular
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. Mingjie Liu, focusing on the development and implementation in advanced machine learning and deep learning models to predict new materials and molecule properties. This one-year appointment is renewable
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about the material behavior and its impact on performance. Using simulation data to train machine learning models of microstructure evolution. Mentoring and directing undergraduate, masters, and PhD
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graphical models, causal inference and machine learning. Dr. Liu's research interests lie in modeling the rapidly-accumulating big data (e.g., muti-omics) in biology and medicine for precision medicine via a
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collection, analysis, and reporting; including (at minimum) Qualtrics, Excel, and SPSS, and open to learning about AI, Power BI, and other tools. Demonstrated talent for collaboration and team building and
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colleagues, researchers, and educators from throughout the University of Florida community to create, identify, and evaluate learning resources and services in the areas of computer vision; works with students
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, supply chain systems, and transportation systems. We also currently host methodological research in data analytics, machine learning, human systems engineering, optimization, simulation, and stochastic
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Analysis About the Mentor: Dr. Shao obtained his PhD degree in Electrical and Computer Engineering from the University of Iowa in 2019, under the supervision of Dr. Gary E. Christensen (IEEE Fellow, AIMBE
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. Mingjie Liu, focusing on the development and implementation in advanced machine learning and deep learning models to predict new materials and molecule properties. This one-year appointment is renewable