research

I am currently a post-doctoral fellow in the Probability group at UBC.

My curriculum is available here.

Preprints and Publications

My path in Mathematics
I have followed a noncanonical path in Mathematics. I did my undergraduate studies in Business Management at Fundação Getulio Vargas, in São Paulo. I soon found out that I was inclined to become an academic, either in the Humanities or in Mathematics. I then went to IMPA, in Rio de Janeiro, where I started a Master's program and was rapidly compelled to pursue a research career in Mathematics. I learnt a good deal in my two years there and had the privilege of being part of possibly the best Master's class ever to pass the institute. A large portion of this class, myself included, decided to study Probability. To explain my choice in a few words: Probability seemed to, at the same time, be theoretically rich, having an interface with a very diversified portion of Mathematics, provide deep intuitive insights on mechanisms that govern natural phenomena, and be very contemporary, since most of the domains of activity were introduced in the last few decades and groundbreaking ideas seem to appear all the time. From 2007 to 2011, I was a PhD student in EPF Lausanne, Switzerland, under the supervision of Tom Mountford. I obtained the PhD degree in June 2011, and then joined the very stimulating Probability group at UBC.

My field of research
My research so far has been entirely on Interacting Particle Systems. This is a field of Probability that arised to model certain phenomena in Statistical Physics such as phase transitions. It is the study of a class of space-time probabilistic models; at each instant of time, each region of space is in a certain state, and the rules of the random evolution are given in terms of local interactions depending on these states. We are then interested in understanding macroscopic or time asymptotic behaviour that result from the interactions. Interacting particle systems are Markov Processes, that is, random processes with the property that the distribution of the trajectory on the future only depends on the current position. However, since they exist in sophisticated state spaces and involve a more elaborate setting than that of traditional Markov Processes, new tools had to be developed to handle their construction and their study.