As a computational neuroscientist, my research focuses on understanding the relationship between neural activity and behavior, mainly from a theoretical perspective but with a strong connection to experimental data. If you are interested in my work, please feel free to contact me. The lab I’m part of is usually hiring at all levels, from internships to postdocs.

Modeling Plasticity and Learning

Our brain is a learning machine, yet the mechanisms of how this learning occurs remain elusive. I am dedicated to developing biologically plausible models of learning and plasticity, with the long-term goal of integrating these low-level rules with a high-level description of behavior. My research focuses on understanding how learning mechanisms can operate amidst changes induced by both stochasticity and environmental factors.


I am interested in phenomenological models of behavior, which quantitatively describe actions and decisions based solely on direct observations. My current projects aim to understand a behavioral manifold that can reduce high-dimensional behavioral recordings to a low-dimensional space. Using this approach, I am developing a system for early seizure detection based purely on behavioral recordings, avoiding invasive methods. Additionally, I explore the role of communication between agents in task-solving, particularly in navigation.

Reservoir Computing

During my PhD, I worked on reservoir computing, a machine learning technique that employs a fixed, random, and recurrent neural network to perform tasks. My research focused on applying this technique to dynamical systems, examining the network’s ability to learn system dynamics and predict future states.