I am a physicist with over 10 years of experience in data analysis. I have worked on theoretical physics, computational biology and neuroscience. I am specialized in the development of statistical models and Bayesian inference methods to detect patterns from complex data.
Among my scientific contributions I would like to highlight
1. A Bayesian approach to infer action potentials from calcium recordings of neuronal populations. This work enables to infer the underlying neuronal activity using state space models and modern sequential Monte Carlo methods.
2. A Bayesian inference method to cluster time series based on hierarchical model. The number of clusters is automatically estimated using the Dirichlet process as a prior. This method is very general and found applications in neuroscience and molecular biology.
3. An information-theoretic method to estimate redundance and synergy among multivariate distributions. This was applied to discover the genetic control of the coding strategy employed by neurons to sense their environment.
I am very passionated about formalizing ideas in mathematical terms. Often, the ability to express our questions in quantitative terms using models and probabilities, leads to optimal solutions. I enjoy the challenge of understanding a new question, figuring out a solution together and providing user friendly software.