My research aims at exploring the interface between Physics, Biology and Computer Science, in particular I deal with olfactory navigation. I try to understand how animals can successfully locate food, conspecifics and avoid predators. Animals live in a fluid environment (air or water) and they are expose anytime to turbulent and highly intermittent olfactory cues: evolution shaped their ability to navigate and find the odor source. I reproduce environmental conditions through Direct Numerical Simulations (DNS) of fluidynamics and scalar transport equations. I use supervised learning to infer where the odor source is located taking as input the turbulent signals from my simulations. This theoretical framework aims at finding new algorithms for olfactory navigation (octopus in particular).
Meanwhile I am also working on two different projects related to mice olfaction.
The first aims at quantifying the relation between glomeruli activation and odor fluxes the mice are exposed to.
In the second project we developed an algorithm that consistently reproduce the navigation strategy that mice develop in an experimental arena and it explores the trade-off between sniffing at the ground level and sniffing in the air.