I work at the interface between computational neuroscience and machine learning, bringing in tools from complex systems, information theory and statistics, to try to understand how the brain acquires, stores, and processes information. To do so, I employ both analytic methods and numerical simulations/optimization.
My recent work has focused on understanding how the brain encodes and deals with uncertainty, training biologically plausible recurrent neural networks to perform sampling-based probabilistic inference. I apply these tools to the study of perception in neurotypical subjects and in autism spectrum disorders.
I have recently started working in the field of algorithmic fairness, with a focus on automated systems based on medical images, and neural data.
Sampling-based representation of uncertainty in the context of Bayesian inference.
Dr. Rodrigo Echeveste
Santa Fe, Argentina
Ciudad Universitaria UNL,
Ruta Nacional Nº 168, km 472.4,
FICH, 4to Piso (3000) Santa Fe – Argentina