Title: Understanding Manifold-structured Data via Geometric Modeling and Learning Abstract: Analyzing and inferring the underlying global intrinsic structures of data from its local information are critical in many fields. In practice, coherent structures of data allow us to model data as low dimensional manifolds, represented as point clouds, in a possible high dimensional space. Different

Title: Improving the Convergence Rate of the Distributed Gradient Descent Method Abstract: This talk presents our recent work on the accelerated Distributed Gradient Descent (DGD) method for distributed optimization problems. We observed that the inexact convergence of the DGD algorithm can be caused by the inaccuracy in the consensus procedure in a distributed optimization setting.