About CRiSS-LAB
CRiSS-LAB studies how societies decide what matters. We develop computational approaches to collective relevance: the processes through which people, groups, and institutions allocate attention, preserve memory, organize preferences, and coordinate decisions.
Our work connects computational social science, network science, dynamic modeling, causal inference, AI, and experimental platforms to study social systems under information abundance. Across education, science, culture, politics, organizations, and digital platforms, we generate evidence that helps institutions understand collective behavior and make better decisions.
The lab connects rigorous research with applied platforms and public-interest tools. Projects such as Lixandria, Discolab, PriorizaChile, SocialRec, MúsicaCL, DYNAMAP, and Capybara translate computational social science into usable evidence for students, schools, public institutions, organizations, business applications, and civic debate.
CRiSS-LAB brings together researchers, graduate students, data scientists, and collaborators from physics, engineering, education, psychology, economics, sociology, political science, and computer science. The lab operates through the Data Science Institute, School of Engineering, Universidad del Desarrollo.
Cristian Candia, Ph.D.
Head of the Computational Research in Social Science Lab.
Cristian Candia
Associate Professor, Data Science Institute, School of Engineering, Universidad del Desarrollo, Chile. Head of CRiSS-LAB.
Cristian Candia studies how societies transform information into collective relevance through attention, memory, preferences, and coordination. His work combines computational social science, network science, AI, and large-scale behavioral data to understand how groups, institutions, and societies decide what matters.