Photonic Artificial Intelligence 

My main research work is focused on photonic hardware for the implementation of intelligent systems. The technological means used is the spatial soliton. Soliton waveguides exhibit a plastic behavior by their nature, that is, a modifiable behavior. The refractive index contrast of a soliton guide depends on the intensity of the light used to write it: therefore, by modulating the intensity of the light sent, it is possible to increase or decrease this contrast, giving neuroplasticity to the system. For this reason, the natural evolution of soliton integrated systems is in learning networks or neural networks. This process is typical of biological neural systems. Like them, soliton neural networks (SNNs), made by the interconnection of fundamental structures that are X-junction neurons, are able to learn information and store it in specific neural pathways through changes in the refractive index. 

Development of materials, metamaterials and polar metasurfaces for mid-infrared emission manipulation

This line of research aims at the theoretical and experimental investigation of polar metasurfaces and metamaterials for surface phonon polariton processing (SPhP). The development of a photonic platform in the mid-infrared (MIR) range is an active research field motivated by applications such as trace detection of biomolecules and noxious/explosive substances, passive radiative cooling, and devices for thermal imaging and medical diagnostics. Most of the progress in this field has been achieved through excitation of SPPs supported by doped semiconductors or in graphene. Surface plasmonic polaritons (SPPs) are electromagnetic waves traveling along a metal-dielectric or metal-air interface, in the infrared or visible frequency. The research is based on the exploitation of different polaritonic modes called surface phononic polaritons (SPhPs) in van der Waals (vdW) and polar materials based on their nanostructuring with random dispersion dielectric elements of subwavelength. Several materials are exploited, such as novel and extremely promising 2D vdW materials like hexagonal boron nitride (hBN) and molybdenum trioxide (MoO3), which have multiple Reststrhalen bands along two (hBN) or three different crystallographic directions (MoO3). 

Software Artificial Intelligence 

At the same time, I use software neural networks, Machine Learning and Deep Learning, for the creation of devices capable of predicting microclimatic fluctuations inside museums. The models used can be used to safeguard the works of art and to improve the quality of visitors' well-being as well as lower maintenance and management costs.

Educational teaching models

In recent years I have been interested in the cognitive processes through which the learning of complex concepts that allow the interconnection between cognitive areas takes place. Observing how the playful dimension plays a decisive role, I approached the analysis of educational models based on game-learning, observing how children who learn in this way are able to obtain a faster and more effective learning, even of abstract concepts.