My research seeks to uncover the fundamental principles governing how living and synthetic active systems self-organize far from equilibrium. How do collections of interacting units generate coherent motion, exchange information, and make collective decisions without central control? What physical laws underlie the emergence of biological organization across scales?
At the interface of statistical physics, nonlinear dynamics, and biology, we develop minimal theoretical frameworks capable of capturing the universal mechanisms that drive collective behavior. Our goal is not only to describe active matter, but to identify its organizing principles — bridging microscopic interactions, mesoscopic fluctuations, and macroscopic structure formation.
Foundations of Active & Living Matter
Active matter challenges the classical paradigms of equilibrium physics. We investigate how self-propulsion, interaction symmetry, particle shape, and stochasticity combine to produce emergent order in systems ranging from bacterial colonies to synthetic active materials.
Our work has contributed to clarifying the role of symmetry in active systems, the conditions for long-range order, and the nature of phase transitions in polar and nematic active matter. We develop both agent-based models and continuum theories to understand how simple microscopic rules generate collective motion, clustering, and large-scale pattern formation.
Collective Intelligence & Information in Motion
Living systems are not only active — they are informational. A central question of our research is how mobile interacting agents exchange orientational or dynamical information to achieve consensus and coordinated migration.
We investigate how information propagation depends on motility, interaction duration, spatial constraints, and non-reciprocal couplings. By integrating concepts from statistical physics and network theory, we aim to build a unified framework for collective decision-making in biological and synthetic active populations.
Stochastic Dynamics & Emergent Transport
Fluctuations are not noise to be averaged out — they are generative mechanisms of organization. We analyze stochastic models of self-propelled motion, including persistent random walks with speed and directional variability, to connect microscopic variability with emergent transport properties.
This approach provides a minimal theoretical basis to interpret experimental observations in cell motility, microswimmer dynamics, and proliferating active matter.
Toward a Physics of Adaptive Active Systems
The next frontier in active matter lies in understanding systems that adapt, learn, or exhibit heterogeneous response. We explore how non-reciprocal interactions, environmental feedback, and intelligent active units reshape the collective dynamics of active materials.
By extending the theory of active matter beyond static interaction rules toward adaptive and information-driven dynamics, we aim to establish a conceptual bridge between physical active systems and living organization.