Speed Session: Bayesian Design of Experiments and Parameter Recovery

Christian is a 5th year mathematics PhD student at Tulane University expecting to graduate in 2024. His research involves a combination of Bayesian statistics, partial differential equations, and Markov Chain Monte Carlo and includes both abstract and applied components. After working at the Virginia Tech National Security Institute during the summer of 2023 where his work largely focused on Bayesian design of experiments he is interested in pursuing a career in testing and evaluation.
With recent advances in computing power, many Bayesian methods that were once impracticably expensive are becoming increasingly viable. Parameter recovery problems present an exciting opportunity to explore some of these Bayesian techniques. In this talk we briefly introduce Bayesian design of experiments and look at a simple case study comparing its performance to classical approaches. We then discuss a PDE inverse problem and present ongoing efforts to optimize parameter recovery in this more complicated setting. This is joint work with Justin Krometis, Nathan Glatt-Holtz, Victoria Sieck, and Laura Freeman.
Session Materials: dataworks.test...