About me

Thank you for stopping by! I am Pedro David García Fernández (David García), and here you can find my research interests. If you think we could work together, please feel free to contact me here.

I am interested in complexity and emergent phenomena in nanophotonic systems. My research background is primarily in disordered photonics, where I study how fabrication imperfections, randomness, and broken symmetries—ubiquitous across nature and length scales—shape light–matter interactions in nanostructures with short- and long-range spatial correlations. When disorder occurs at length scales comparable to the wavelength of propagating waves (optical, mechanical, or electronic), it can profoundly modify transport through scattering and interference, leading to phenomena such as diffusion, localization, and enhanced light–matter coupling.

Much of my early work focused on so-called frozen systems, where structure and dynamics are fixed and largely linear, yet still host rich physics when disorder enters at the wavelength scale. What initially appears as a limitation can instead become a resource, enabling new functionalities with applications ranging from imaging and lasing to quantum optics and information technologies.

More recently, my work has shifted toward active and nonlinear photonic systems, particularly optomechanical platforms operating close to dynamical instabilities. These systems provide a unique physical setting in which optical, mechanical, thermal, and electronic degrees of freedom interact across multiple timescales, giving rise to self-organization, stochastic dynamics, and emergent collective behavior. I am especially interested in regimes where noise, nonlinearity, and feedback become functional elements of the system rather than limitations to overcome.

My interests have gradually evolved from disorder and wave transport toward a broader question: how complexity emerges in physical systems and how it can be used to process information. My current research explores the roles of fluctuations, nonlinearity, and dynamical criticality as computational resources, with applications ranging from physical reservoir computing to the broader relationship between complexity, information, and computation.

In December 2021, I was appointed tenured researcher (Científico Titular) at the Spanish National Research Council (CSIC). I am currently setting up a research line on complex optomechanics and neuromorphic photonics at the Instituto de Ciencia de Materiales de Madrid (ICMM-CSIC). If you are interested, please contact me here.

EU Pathfinder project: NEUROPIC (2023–2027)

In March 2023 we started a 4-year EU Pathfinder project on integrated and programmable photonic circuits:
NEUROPIC — Nano electro-optomechanical programmable integrated circuits for neuromorphic computing.

The project is coordinated by the Instituto de Ciencia de Materiales de Madrid (ICMM-CSIC), in collaboration with the Department of Electrical and Photonics Engineering at DTU (Copenhagen), the Catalan Institute of Nanoscience and Nanotechnology (Barcelona), and additional research institutes and SMEs in Ireland and Germany.

For more information, listen to the interview on Spanish National Radio:

More information: https://www.neuropic-project.com/

EU Pathfinder project: ADAPTATION (2024–2028)

In April 2024 we started the 4-year EU Pathfinder project ADAPTATION, aimed at designing, characterizing, and developing novel bio-inspired devices for energy harvesting.

The consortium includes nine European partners: Universidade do Minho and the International Iberian Nanotechnology Laboratory (INL) (Portugal); CSIC, University of Vigo, Avanzare Innovation Tecnologica SL, and Cooling Photonics (Spain); University of Strasbourg (France); Utrecht University (Netherlands); and Sunplugged Solare Energiesysteme GmbH (Austria).

Open positions: pdgarfer.github.io/positions

Interests

• Mesoscopic photonics, complexity, and localization phenomena
• Quantum photonics
• Optomechanics
• Radiative cooling
• Hardware for artificial intelligence and complex neural networks