I am a postdoctoral researcher in Alex Pouget‘s lab in Geneva, Switzerland, a member of the International Brain Laboratory, and a visiting scholar at New York University. From 2014 to 2017, I was a postdoc in Wei Ji Ma‘s lab at NYU. I obtained my PhD at the Doctoral Training Centre for computational neuroscience based in Edinburgh, UK, under the supervision of Sethu Vijayakumar and Daniel Wolpert. I spent several months of my PhD at the Computational and Biological Learning Lab in Cambridge, UK.
I develop machine learning methods to facilitate and improve the quality of scientific inference (that is, model fitting and evaluation), using state-of-the-art techniques such as Bayesian optimization and approximate inference. Check out my package Bayesian adaptive direct search for model fitting (via optimization), and Variational Bayesian Monte Carlo for approximate Bayesian posterior and model inference.
My research in computational neuroscience focuses on how the brain combines different sources of perceptual information when there are multiple possible underlying explanations (causal inference). I am also interested in how the brain represents probabilistic beliefs (priors), and how these beliefs are formed, updated, and used for decision making under uncertainty. I explore these question with mathematical modelling, computational analysis and behavioral experiments.
I like to explore complex settings with a minimal set of assumptions. Prior to my entrance in neuroscience, I have been briefly working on cellular automata. I did my physics MSc thesis in statistical mechanics and cosmology, hiding my interest in the foundations of quantum mechanics, because in a physicist it used to be interpreted as a sign of senility. In the past, I worked for several years as editor and consultant for science and science-fiction.