Rating is available when the video has been rented.
This feature is not available right now. Please try again later.
Published on Oct 24, 2016
Peter Richtarik is a Reader in the School of Mathematics at the University of Edinburgh, and is the Head of a Big Data Optimization Lab. He received his PhD from Cornell University in 2007, and currently holds an EPSRC Early Career Fellowship in Mathematical Sciences.
My main research focus is the development of new optimization algorithms and theory. In particular, much of my recent work is in the emerging field of big data optimization, with applications in machine learning in general and empirical risk minimization in particular. For big data optimization problems, traditional methods are no longer suitable, and hence there is need to develop new algorithmic paradigms. An important role in this respect is played by randomized algorithms of various flavors, including randomized coordinate descent, stochastic gradient descent, randomized subspace descent and randomized quasi-Newton methods. Parallel and distributed variants are of particular importance.