Topics in Machine Learning and OptimizationApply
Topics in machine learning (ML). The project can be of a theoretical nature (e.g., design of optimization algorithms for training ML models; building foundations of deep learning; distributed, stochastic and nonconvex optimization), or of a practical nature (e.g., creative application and modification of existing techniques to problems in federated learning, computer vision, health, robotics, engineering). The precise topic will be decided together with the successful applicants, and will be tailored to their skills and background.
Program - Applied Mathematics and Computer Science
Division - Computer, Electrical and Mathematical Sciences and Engineering
Field of Study - Computer Science, Mathematics or a related discipline
Professor, Computer Science<br/>
Prof. Richtarik's research interests lie at the intersection of mathematics, computer science, machine learning, optimization, numerical linear algebra, high performance computing and applied probability. He is interested in developing zero, first, and second-order algorithms for convex and nonconvex optimization problems described by big data, with a particular focus on randomized, parallel and distributed methods. He is the co-inventor of federated learning, a Google platform for machine learning on mobile devices preserving privacy of users' data.
Desired Project Deliverables
Original research – contribution to a research paper.