Coralia Cartis is an Associate Professor in Numerical Optimisation in the Mathematical Institute, University of Oxford since 2013, and a Turing fellow at the Alan Turing Institute for Data Science since 2016; previously, she held academic and research positions at University of Edinburgh and Rutherford Appleton Laboratory. She holds a PhD degree from Cambridge University (supervisor: Prof Mike Powell) and a BSc in Mathematics from Babesh-Bolyai University, Cluj, Romania. Her research interests are in nonlinear optimisation algorithm analysis and implementation, especially complexity/global rates of convergence, and in diverse applications of optimisation from climate modelling to signal processing and machine learning.
Optimization for Data Science (Scalability and tractability challenges)
Known by many names, sketching techniques allow random projections of data from high to low dimensions, while preserving useful properties. This talk explores ways to use sketching (and related techniques) as a powerful dimensionality reduction tool, to improve the scalability of algorithms for diverse classes of optimization problems and applications, from linear to nonlinear, local to global, derivative-based to derivative-free; global rates of convergence as well as numerical illustrations will also be given.