Joao Ricardo Nickenig Vissoci

Joao Ricardo Nickenig Vissoci, MSc, PhD is an Associate Professor of Emergency Medicine, Neurosurgery, Global Health and Biostatistics and Bioinformatics. He is the Chief of the Division of Translational Health Sciences in the Department of Emergency Medicine, co-Director of the Global Emergency Medicine Innovation and Implementation (GEMINI) Research Center and the Associate Director for Education of the Research Design and Analysis Core (RDAC), Duke Global Health Institute. Dr. Vissoci has a background in social psychology and data science. Dr. Vissoci, a Brazilian native, earned a bachelor’s degree in Psychology from State University of Maringá/Brazil, a Masters in Physical Education, an MBA in Human Resources, and a PhD in Social Psychology. During his PhD, he completed a fellowship in Data Science at Duke University. After graduating his PhD in Social Psychology from the Pontificia Universidade Católica of São Paulo/Brazil, Dr. Vissoci completed a postdoctoral fellowship at the University of Sao Paulo (2015) in Design and Analysis for Mental Health research. He completed a second postdoctoral fellowship at the Duke Global Health Institute in Global Health and Data Science in 2016. Dr. Vissoci held a faculty position and taught Public Health and Health Sciences in Brazil from 2009 to 2015. After completing his fellowship at DGHI, he joined the Duke Department of Emergency Medicine as faculty in 2017. In his last 14 years as faculty (2009-current), he has mentored over 200 trainees at all levels of training from undergraduate, graduate, medical education, postdoctoral to faculty level. He has published over 200 manuscripts and collaborated on over 6 R-level NIH grants, multiple (K and D) NIH training grants, other federal grants UK/Brazil based, and foundational grants.

His research interests focus on leveraging data through analytics and technology to bridge the gap in access and equity in care in low resource settings, translating evidence into practice or policy impact. He uses data science and mixed-methods research to design and implement innovative data-driven solutions to address health care gaps.