Javier Alvarez-galvez PhD

Dr. Javier Alvarez-Galvez (Ph.D., Complutense University of Madrid) is a social research methodologist working in the intersection of social and health sciences. He is currently working as Ramon & Cajal Senior Research Fellow in the Department of Biomedicine, Biotechnology, and Public Health at the University of Cadiz, where he teaches biostatistics, quantitative methods, multivariate data analysis, and public health. His main research interests are related to computational social science, social/health data science, research methods, complex systems, social inequalities in (mental) health, and health sociology. In the past, he has worked as a Senior Research Associate in the Department of Applied Health Research at the University College of London, and as a Senior Lecturer and Associate Professor at different universities in Spain, such as the Complutense University of Madrid, the University of Sevilla and the Loyola University Andalusia. Javier has also been visiting researcher at the University of Sydney (AU), Iowa State University (USA), Harvard University (USA), the University of Surrey (UK), and the University of Ljubljana (Slovenia). He leads the recently created Computational Social Sciences DataLab (CS2 DataLab) which is aimed to apply innovative approaches of social data science, digital trace, and visualization techniques for advancing social research and policy planning. Our team brings together a new collaboration from across the University Institute of Research for Social Sustainable Development (INDESS), the Biomedical Research and Innovation Institute of Cadiz (INiBICA), and other external research institutions with expertise in computational social science. CS2 DataLab is an interdisciplinary laboratory focused on the development and application of innovative methods based on social network analysis, social data mining, simulation modeling, and machine learning tools for the implementation of ground-breaking research solutions to increase the social impact of our research in health and social sciences.