Alex Strang is a postdoctoral instructor in computational and applied math at the University of Chicago. He received his PhD in applied math from Case Western Reserve University in 2020. He will join the UC Berkeley Stats and Data Science programs this summer.
Alex studies the structures of networks that arise in a variety of disciplines including biophysics, ecology, neuroscience, and in competitive systems. In each field, he seeks to understand the interplay between structure and dynamics. He is particularly interested in random walks on networks associated with biophysical processes occurring at the molecular scale. He also works on networks that represent competing agents who evolve according to a training protocol. He draws on tools from discrete topology, non-equilibrium thermodynamics, and functional form game theory to study the interplay of structure and dynamics in these systems.
He also works on Bayesian inference and sparsity promotion via hierarchical hyperpriors. His work here has focused on coordinate ascent methods for MAP estimation, the sensitivity of estimators (and the effective regularizer) to changes in hyperparameters, and variational methods for estimating confidence intervals.
Hierarchical ensemble Kalman methods with sparsity-promoting generalized gamma hyperpriors. (FoDS)
Hierarchical Bayesian models are a powerful numerical framework for estimation and inference. The hierarchical nature of the models allows efficient optimization via coordinate ascent. We develop an estimation framework for nonlinear inverse problems with sparsity-promoting priors. The method replaces the least squares step in a reweighted least squares algorithm with an ensemble Kalman update. The Kalman approach extends existing methods to nonlinear forward models.
George Herbert Jones Laboratory 309, 5747 South Ellis Ave,
Chicago, Illinois, 60637