In the last couple of years, I have noticed an emerging theme in my work. Across multiple biological systems, colleagues and I have articulated models that involve particles that (1) emerge at random times from a fixed source-location distribution; (2) move throughout a local environment randomly (either diffusing, or switching between deterministic states); and (3) are removed from the system due to state-switching or escape from some predefined region.
For lack of a better term, my collaborators and I are calling these systems “stochastic fountains,” and we have been studying what these systems look like when you only have access to partial information. For example, what if you only have a snapshot of particles at one instant in time? Or, what happens if you can only observe particles at the moment they leave the domain? The associated inference problems arise naturally in mathematical biology applications, and quickly involve a fascinating marriage of point process theory with approaches to classical PDE inverse problems.
Colloquium and Research Innovations in Mathematical Sciences
Wednesday, October 29
12:00pm
WXLR A206
Faculty host: John Fricks
Coffee and cookies will be served.
Scott McKinley
Associate Professor
School of Science and Engineering
Tulane University