The following core faculty members have active research interests in data science and teach most program courses.

Stochastic differential equations, stochastic optimization, data assimilation

Infinite dimensional Riemannian geometry, shape analysis, manifolds of mappings, geometric mechanics, medical imaging

Mathematical neuroscience and physiology, network science

Partial differential equations, Navier-Stokes equations, uncertainty, data assimilation

Computational mathematics, numerical and non-numerical algorithms, shape and signal analysis, optimization, high-performance computing and software

Financial mathematics, dynamical systems, stochastic analysis, portfolio and credit risk, high frequency trading, machine learning, stochastic processes, mathematical economics

Computer vision and pattern recognition, shape analysis, geometric topology, differential geometry

Numerical analysis, finite element methods, scientific computing, machine learning for solving forward and inverse problems in partial differential equations

Data science, geometric and topological data analysis, scientific and practical applications

Topological data analysis, optimal transport, applications to network science and biology

Applied probability, data science, financial mathematics, operations research