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.
Peter's main research focus is the development of new optimisation algorithms and theory. In particular, much of his recent work is in the emerging field of big data optimisation, with applications in machine learning in general and empirical risk minimisation in particular. For big data optimisation 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 randomised algorithms of various flavors, including randomised coordinate descent, stochastic gradient descent, randomised subspace descent and randomised quasi-Newton methods. Parallel and distributed variants are of particular importance.