Applied Mathematics
University of California, Los Angeles
About
I am a PhD student in applied mathematics at UCLA. I work on inverse problems for chaotic and turbulent systems: given partial, noisy observations of a system evolving in time, what can be recovered about its state and about the parameters governing it?
Most of my work so far has been on nudging algorithms, which couple a model to incoming data through a feedback term and drive the model toward the true state as observations arrive. These methods are appealing because they run on the fly, cost about as much as the forward simulation, and, in a number of settings, come with provable convergence guarantees.
More broadly, I am interested in PDEs and dynamical systems, mathematical modeling, scientific computing, and data-driven methods.
Before UCLA, I completed a BS and MS in mathematics at Brigham Young University. As an undergraduate I worked on the \(N\)-body problem, stochastic switched systems, and gerrymandering. My master’s thesis, advised by Jared Whitehead, was on data assimilation and parameter recovery for Rayleigh–Bénard convection.
You can reach me at jwmurri@math.ucla.edu, or read my CV.