Postdoctoral Researcher, Institute of Disease Modeling

I earned my PhD in theoretical physics from Laval University in Québec where I got involved in network theory through a simple applied problem that resided on a complex canvas: disease propagation on social networks. This problem is in part simple; we have some intuition of how people interact and how diseases are transmitted. However, complexity stems not from the question itself, but from the fact that social networks, like most systems in nature, are greater than the sum of their parts. To understand disease propagation, we must not only understand how one individual infects another, but also the complex patterns that link these individuals to the rest of the population. Unfortunately, our current understanding is a patchwork: finding relevant network properties and including them, one at a time, in our existing mathematical models without a clear view of the larger design.

Using tools from statistical physics and other methods of mathematical modeling, my research attempts to find overarching principles that could lead to a more complete view of complex networks. I hope to find a better perspective to study networks than as an ensemble of points and lines. Switching our focus toward the systems that produce these networks, such as looking at the hierarchical structure of our society rather than at local social networks, I aim to find better ways to quantify the global role of each element in the systems. This implies adopting broader definitions of what networks can be; whereas most traditional methods simply construct them like puzzles, piece-by-piece based on limited information.