Associate Professor in Mathematics
Ph.D., University of Rouen, France, 2005
Education and Research Positions
|2013 - ||Associate Professor in Mathematics at the School of Mathematical and Statistical Sciences and Honors Faculty at Arizona State University, USA.|
|2007 - 2013||Assistant Professor in Mathematics at the School of Mathematical and Statistical Sciences and Honors Faculty at Arizona State University, USA.|
|2005 - 2007||Research Associate in Mathematics under the supervision of Claudia Neuhauser at the Department of Ecology, Evolution, and Behavior of the University of Minnesota, USA. |
|2002 - 2005||Ph.D. in Mathematics under the supervision of Claudio Landim and Teaching Assistant at the University of Rouen, France.|
|2001 - 2002||Diploma of Advanced Studies (2nd year of Master's Degree) analysis and stochastic models, Magna Cum Laude, University of Rouen, France.|
|2001 ||Agregation de Mathematiques (nationwide competition).|
|1999 - 2000||Maitrise in Mathematics (1st year of Master's Degree), Magna Cum Laude, University of Rouen, France.|
|1998 - 1999||Licence in Mathematics (3rd year of Bachelor's Degree), Magna Cum Laude, University of Rouen, France.|
Most mathematical models introduced in the life and social sciences literature that describe inherently spatial phenomena of interacting
populations consist of systems of ordinary differential equations.
These models, however, leave out any spatial structure or stochastic component while past research has identified space and stochasticity
as key factors in how communities are shaped, and spatial stochastic models can result in predictions that strongly differ from their
nonspatial deterministic counterparts.
In contrast, my research aims at understanding the role of space and stochasticity in a wide variety of applied sciences such as physics,
biology, sociology, economics, etc. through the mathematical analysis of a class of stochastic processes known as interacting particle systems.
In these models, members of the population~(particles) such as atoms, cells, plants, voters, players, etc. are located on the vertex set of
a connected graph.
The latter has to be thought of as a network of interactions that dictates the dynamics of the system as particles can only interact locally
with their neighbors, thus modeling the presence of an explicit spatial structure.
Space in the context of interacting particle systems must be understood in a broad sense: an edge between two vertices of the
underlying graph may be synonymous of geographic proximity, but also friendship relation, adherence to the same political party, etc.
The main objective of research in this field is to understand the macroscopic behavior and spatial patterns that emerge from the microscopic
interactions that describe the dynamics of large systems.