Charles Margossian joined the Center for Computational Mathematics as a Flatiron Research Fellow in August 2022. Charles earned a Ph.D. in Statistics from Columbia University in 2022, and a B.Sci. in Physics from Yale University in 2015. His primary interest lies in the development of Statistical and Machine Learning methods with applications in the natural sciences. Much of his research is motivated by Bayesian modeling problems in Pharmacometrics and Epidemiology. He studies Markov chains Monte Carlo (MCMC) and approximate Bayesian computation, as well as hybrids which combine the two paradigms. His work bridges statistical methods, computation, and application through the development of probabilistic programing. He is a core developer of the Bayesian inference software Stan, and has an ongoing collaboration with the TensorFlow Probability team at Google Research.