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Our research at CHuepeLabs focuses on understanding the fundamental mechanisms behind the self-organization observed in the physical, biological, social & technological worlds.

Unveiling and manufacturing complexity for almost 20 years.


Self-organization is often observed in active matter systems such as cell colonies, developing tissue, insect swarms, bird flocks, and groups of autonomous robots.  Its emergence in fluid-like systems of self-propelled agents with aligning interactions has been the focus of intense research, and can be viewed as a decentralized consensus process on local heading directions. By contrast, active elastic dynamics, which result from position-based interactions similar to attraction-repulsion forces and cannot be directly mapped to consensus processes, are much less understood.
In this research, we study simple models of active elastic dynamics and explore their characteristic features, including: energy cascades, mode selection, long-range correlations, active crystallization, active jamming, and persistent oscillations.
This work is developed in collaboration with Dr. Pawel Romanczuk, Graduate Student Yinong Zhao (Humboldt University, Berlin)


A generic feature of many natural systems is their organization as a hierarchy of modules that help structure their interactions and processes. Basic building blocks combine into more complex modules that can in turn organize into further modular structures at yet higher levels. Although this type of modular-hierarchical (MH) structures have been observed in a variety of biological networks, we still lack formal tools to characterize them and have a limited understanding of their origins and dynamics.
In this work, we consider simple models that generate MH structures and networks to study their unique characteristic features. We analyze their topological, dynamical, and evolutionary properties. We also analyze the dynamics of various processes on MH networks, including: diffusion, first-passage, synchronization and evolutionary selection.

This work is developed in collaboration with Prof. Dirk Brockmann and Graduate Student Benjamin Maier
(Humboldt University -  Robert Koch Institute, Berlin, Germany)
and with Profs. Ali-Emre Turgut & Eliseo Ferrante and Graduate Students İhsan Caner Boz &  İlkin Ege Okay
(Middle East Technical University, Ankara, Turkey)


Developing decentralized control algorithms that can achieve collective motion, in groups of autonomous self-propelled agents using only limited local information, is one of the first steps required to develop swarm robotics. Inspired by models of collective motion developed for animal groups such as bird flocks or fish schools, most algorithms studied to date rely heavily on the communication of the relative orientations of neighboring agents.
In this research, we study the experimental control capabilities of a recently proposed model for collective motion that we recently developed, which only requires information on the positions of neighboring agents, and not on their headings.  We implemented this algorithm on an E-puck robot system and study its ability to converge to collective translation and rotation, starting from a state with random orientations.

This work is being developed with Prof. Zhangang Han and Graduate Student Yating Zheng (Beijing Normal University)


The emergence of post-truth can be understood through a quantitative study of the underlying collective opinion-formation dynamics on social networks.
In this research, we analyze numerically and analytically minimal complex adaptive network models (where node states and links evolve simultaneously) that capture features of the opinion dynamics on digital social platforms. These models display absorbing states where the network fragments into components that are in internal consensus, which correspond to the echo chambers that lead to post-truth.
Our current work focuses on analyzing Twitter data related to specific events, aiming to provide experimental computational social sciences connections  that connect to our theoretical results.

This work is being developed with the Social Communication Lab (SCL) & Social Listening Lab (SoL)
(Universidad Católica de Chile, Santiago, Chile)
and with Graduate Student Claudio Villegas (Santiago, Chile)


While music has always been related to science through acoustics and perception, the development of new technologies allows us to view it today in the context of complex systems.
In this research, we study how music can be considered an emergent phenomenon of a complex system and discuss the artistic, scientific, and technological implications of this perspective. Our work has related music and systems by testing different approaches for sonifying the dynamics of a flock or swarm, by generating minimal musical structures starting from a simple dynamical system, and by developing efforts to show that music displays self-organized critical dynamics. In our current efforts, we train dynamical Boolean networks to reproduce a musical corpus and then investigate the criticality of the resulting dynamical networks.

This work is being developed in collaboration with Profs. Maximino Aldana (UNAM, Mexico) and Rodrigo Cádiz (UC Chile)

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