Computational Research in Social Science Lab

Network science, AI, and behavioral data for public-impact research

CRiSS-LAB studies collective intelligence, collective memory, learning, cooperation, mobility, polarization, and institutional decision-making using network science, causal inference, experimental game theory, machine learning, and large-scale behavioral data.

Past Talks

Quantifying student behavioral patterns and roommate peer effects [Charla Virtual]

Abstract: [ENG] Vast amounts of data are becoming increasingly available to capture social processes at large scale and in great detail. Meanwhile, the rapid development of AI and computational approaches offers powerful toolkits for analyzing various data.

Jul 12, 2024 10:00 AM Zoom Meeting

Quantifying student behavioral patterns and roommate peer effects [Charla Virtual]

¡Descubre la Conexión Entre la Física y las Ciencias Sociales! [Entrevista Virtual]

La Convergencia de las Ciencias Físicas y Sociales: Una Entrevista con el Dr. Cristian Candia En una entrevista reciente, Carla Hermann, Ph.D., la anfitriona del programa Let’s Get Physical, se sentó con el Dr.

Jul 30, 2023 12:00 PM CRiSS-LAB, UDD, Las Condes, Chile.

Cristian Candia

¡Descubre la Conexión Entre la Física y las Ciencias Sociales! [Entrevista Virtual]

Meet the team

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Current Projects

Meisner, interaction, and embodied communication

Creative and applied research project using the Meisner technique as a laboratory for attention, presence, listening, reciprocity, and social signal dynamics.

Cristian Candia

Last updated on May 30, 2026 Creative Research

MúsicaCL: computational music, culture, and networks

Creative and research project that connects Chilean music, cultural analytics, network science, and AI-assisted exploration of scenes, repertoires, and audiences.

Cristian Candia

Last updated on May 30, 2026 Creative Research

Collective Memory Decay in Science

ANID-FONDECYT Regular 2026 project on why retracted research continues to be cited and influence scientific knowledge.

Cristian Candia

Last updated on May 29, 2026 Research Grants

Capybara: AI-assisted school coexistence and bullying prevention

Applied platform that uses experimental game theory, network science, and AI to help schools detect early signals of bullying and strengthen classroom coexistence.

Cristian Candia, Carlos Rodriguez-Sickert, Tamara Yaikin, Cecilia Monge

Last updated on May 29, 2026 Applied Platforms

Featured Research Articles

Augmenting Firm Diversification Behavior Prediction with Graph Embeddings

Public procurement plays a crucial role in modern economies, serving not only as a mechanism to acquire public goods, services, and …

Niclas F. Sturm, Cristian Candia, Bruno Damásio, Flavio Pinheiro

Augmenting Firm Diversification Behavior Prediction with Graph Embeddings

Interconnectedness in Education Systems

Education systems are webs of interconnected students, teachers, programs, institutions, and decisions. This chapter argues that a …

Cristian Candia, Javier Pulgar, Flavio Pinheiro

Quantifying the temporal dynamics of collective memory and attention in social systems

Collective memory can be quantified through the collective attention received by cultural icons, artifacts, people, scientific ideas, …

Cristian Candia

Quantifying the temporal dynamics of collective memory and attention in social systems

Data Science and the Dynamics of Collective Memory

This chapter introduces computational social science as a data-driven framework for studying how collective memory forms, persists, and …

Cristian Candia

Data Science and the Dynamics of Collective Memory

Autism shapes social integration and reciprocity in elementary classrooms

This study examines social dynamics in elementary classrooms using experimental game theory and social network analysis. Across 26 …

Patricia Soto-Icaza, Melanie Oyarzún, Tamara Yaikin, Mirla Arcos-Polanco, Cristian Candia, Carlos Rodriguez-Sickert, Pablo Billeke

Autism shapes social integration and reciprocity in elementary classrooms

See all publications

Teaching

Unsupervised Machine Learning

Course on clustering, dimensionality reduction, representation learning, topic models, anomaly detection, embeddings, and unsupervised workflows for behavioral and social data.

Cristian Candia

Last updated on May 30, 2026

Network Science and Complex Systems

Current UDD course on complex networks, graph analytics, null models, communities, diffusion, and graph machine learning.

Cristian Candia

Last updated on May 29, 2026

Complexity Global School for Emerging Political Economies

Invited faculty participation in the Santa Fe Institute Complexity Global School for Emerging Political Economies 2025.

Cristian Candia

Last updated on May 29, 2026

Causal Inference for Data Science

Course module on causal questions, identification, quasi-experiments, observational data, and policy evaluation.

Cristian Candia

Last updated on May 29, 2026

From the Blog

Collective memory across books, chapters, and data

Recent book chapters extend the lab’s work on collective memory, attention, and computational social science.

Cristian Candia

Last updated on May 30, 2026

Grouping strategies change classroom collaboration

What recent physics education papers show about friendship, exploration, self-selection, and collaboration in classrooms.

Cristian Candia

Last updated on May 30, 2026

Unsupervised learning for social systems

Why clustering, embeddings, and latent representations matter for studying education, culture, organizations, mobility, and collective behavior.

Cristian Candia

Last updated on May 30, 2026

MúsicaCL: studying music as a cultural network

A note on the new MúsicaCL project and why Chilean music can be studied as a network of scenes, collaborations, repertoires, and collective attention.

Cristian Candia

Last updated on May 30, 2026

Meisner exercises as a laboratory for social signals

How actor-training exercises can help study attention, listening, reciprocity, and real-time coordination.

Cristian Candia

Last updated on May 30, 2026

Graph embeddings and firm diversification

A short explanation of how graph embeddings can help predict firm diversification behavior in public procurement markets.

Cristian Candia

Last updated on May 30, 2026