PhD position in Mathematical statistics or Computational science and Engineering at the Department of Mathematics and Mathematical Statistics, Umeå University.
Project description and tasks
The connection between optimization and machine learning is at the heart of many recent breakthroughs in artificial intelligence and autonomous systems. A large number of machine learning applications are modeled as optimization problems, and as problems become more and more complex, new optimization methods are needed. This PhD project aims to advance the field of optimization and machine learning by developing new techniques and algorithms. The project adopts an integrated perspective that combines modeling and optimization. The research will focus on using operator splitting, stochastic approximation, and randomized linear algebra to create new methods that are well-suited for high-dimensional problems and will apply these techniques to various areas, such as deep neural networks and federated learning.
The project is part of the AI/MLX track of the Wallenberg AI, Autonomous Systems, and Software Program (WASP), and the PhD student will be enrolled in the WASP graduate school.