Computational Research in Social Science Lab
CRiSS-LAB | Network science, AI, and behavioral data for public-impact research
CRiSS-LAB studies how people learn, remember, cooperate, move, debate, and make decisions in complex social systems. We combine network science, causal inference, machine learning, experimental game theory, and large-scale behavioral data to produce rigorous evidence and public-interest technology for education, culture, organizations, policy, and civic life.
About CRiSS-LAB
CRiSS-LAB is the Computational Research in Social Science Lab at Universidad del Desarrollo in Santiago, Chile, directed by Cristian Candia, Ph.D. We investigate collective intelligence, collective memory, education, school coexistence, mobility, cooperation, polarization, culture, organizations, science, and institutional decision-making as interconnected social systems.
Our work combines network science, causal inference, artificial intelligence, machine learning, recommender systems, experimental game theory, computational text analysis, and large-scale behavioral data. We connect this research with applied platforms such as Lixandria, Discolab, PriorizaChile, SocialRec, MúsicaCL, and Capybara, translating computational social science into usable evidence for students, schools, public institutions, organizations, and civic debate.
Cristian Candia, Ph.D.
Head of CRiSS-LAB.
People Behind CRiSS-LAB
Researchers, students, and Data & AI professionals building computational social science in Chile.
Our team connects network science, artificial intelligence, education analytics, collective memory, mobility, cooperation, and public-interest technology with applied projects for schools, institutions, and civic debate.
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