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
