Research Interests
Numerical methods for optimization and optimal control problems. The dimension of today's data-related optimization problems can easily reach the order of billion or more. Dr. Qu investigates more scalable modern optimization methods, including randomized coordinate descent methods (serial, parallel, distributed, accelerated variants), stochastic gradient methods (mini-batch, variance reduction) and primal-dual methods. She is equally interested in exploring the power of randomization in the attenuation of the curse of dimensionality for the solution of optimal control problems.