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Assistant Professor, Diego Mazzotti, PhD

Dr. Diego R. Mazzotti, Ph.D., Assistant Professor, Medical Informatics

Diego Mazzotti

Dr. Diego Mazzotti is an Assistant Professor in the Division of Medical Informatics, Department of Internal Medicine at the University of Kansas Medical Center. Dr. Mazzotti received his Ph.D. in Psychobiology at the Federal University of São Paulo, Brazil and a Certificate in Biomedical Informatics from the University of Pennsylvania Perelman School of Medicine. During his doctoral work, he contributed to the characterization of several physiological and molecular aspects of sleep in healthy aging. Since then, Dr. Mazzotti committed his career to fill the gap between basic and clinical sciences through translational and clinical research informatics. His areas of expertise encompass Sleep Medicine and Circadian Biology, Health Informatics, Bioinformatics and Genetics.

Dr. Mazzotti also served as a Research Scientist at the Center for Applied Genomics, Children's Hospital of Philadelphia and took a faculty position as a Research Associate in Sleep Medicine at the University of Pennsylvania Perelman School of Medicine. He also worked as a Bioinformatics Consultant in several projects spanning many human complex disorders.

In July 2020, Dr. Mazzotti joined the faculty at the University of Kansas Medical Center. His current research interests focus on the application of innovative methods to the analysis of high-dimensional physiological, behavioral, genetic and epidemiological data in sleep and sleep disorders, to understand how they can be translated into clinical knowledge and into applications that can advance healthcare. Such methods include supervised and unsupervised machine learning, data integration and harmonization and development of tools that facilitate clinical research with the potential to impact clinical care. To achieve these goals, Dr. Mazzotti aims to establish a solid multidisciplinary research program in the interface between Biomedical Informatics and Sleep Medicine, particularly in the following areas: novel analytical approaches to obstructive sleep apnea phenotyping, predictive modeling and clinical decision support of cardiovascular outcomes using sleep physiological markers, clinical sleep data integration for health outcomes research using electronic health records and elucidating the genetic basis of sleep and sleep disorders in humans.

Last modified: Jul 22, 2021