International PhD Course in Nonlinear Statistics
Nonlinear statistics is an increasingly active research field at the intersection of geometry, statistics, machine learning and algorithmics. Nonlinear statistics seeks to answer fundamental questions that arise when defining new statistical models and tools for analysis of data that exhibits nonlinearity.
Such data is becoming increasingly prevalent in diverse fields including computational anatomy, phylogenetics, and computational biology, and are also receiving increased interest in machine learning, where nonlinearity adds to the possible flexibility of a predictive model. In addition to impact on applied problems, theoretical advances in nonlinear statistics also provides new insight into methodology from traditional linear statistics.
- Anuj Srivastava, Department of Statistics, Florida State University
- Tom Nye, School of Mathematics and Statistics, Newcastle University
- Tom Fletcher, Scientific Computing and Imaging institute, University of Utah
For more information, visit the course webpage
The deadline for registration is 15 May 2017. You need not be a PhD student to register for the course. Seats are assigned on a first come first serve basis.