Stochastic Processes and Applications

Randomness is inherent to real world problems so faculty research in this area includes the development and application of probabilistic tools to model, predict, and analyze randomness in applications. It encompasses various areas of both theoretical and applied probability, including Bayesian networks, computational biology, computational probability, discrete probability, mathematical finance, Markov processes, Markov chain Monte Carlo (MCMC) algorithms, optimal stopping, stochastic control, stochastic differential equations, random matrices, and random graphs.

Relevant coursework includes Markov and stochastic processes, stochastic analysis for finance, and random graphs as well as relevant courses on mathematical statistics and time series.  Advanced courses include measure-theoretic probability and stochastic differential equations.