A conference on numerical analysis and scientific computing
for graduate students and postdocs from the Mid-Atlantic region.
Inverse problems deal with recovering unknown causes from incomplete, noisy indirect observations of their effect. This task may be very difficult even when the observation model is linear, especially if the forward map is very smoothing. In this talk we show how, recently, numerical linear algebra and Bayesian inference have joined forces to provide amazing solutions to a wide range of challenging inverse problems.