Optimization Algorithms for LargeScale Systems: From Deep Learning to Energy Markets Brief Biography: Navid Azizan is a fifthyear PhD candidate in Computing and Mathematical Sciences (CMS) at the California Institute of Technology (Caltech), where he is coadvised by Adam Wierman and Babak Hassibi, and is a member of multiple research groups: DOLCIT, RSRG and SISL. During the summer of 2019, he was a research scientist intern at Google DeepMind. He received the B.Sc. degree in EE form Sharif University of Technology and the M.Sc. degree in ECE from the University of Southern California, in 2013 and 2015, respectively. His research interests broadly lie in mathematical optimization, machine learning, networks, and markets. He is the recipient of several awards, including the 2016 ACM GreenMetrics Best Student Paper Award, the Amazon Fellowship in Artificial Intelligence, the PIMCO Fellowship in Data Science, the Caltech CMS Fellowship, and the USC Provostu0027s Fellowship.

GradVis: Visualization and Second Order Analysis of Optimization Surfaces during the Training of Deep Neural Networks Current training methods for deep neural networks boil down to very high dimensional and nonconvex optimization problems which are usually solved by a wide range of stochastic gradient descent methods. While these approaches tend to work in practice, there are still many gaps in the theoretical understanding of key aspects like convergence and generalization guarantees, which are induced by the properties of the optimization surface (loss landscape). In order to gain deeper insights, a number of recent publications proposed methods to visualize and analyze the otimization surfaces. However, the computational cost of these methods are very high, making it hardly possible to use them on larger networks. In this paper, we present the GradVis Toolbox, an open source library for efficient and scalable visualization and analysis of deep neural network loss landscapes in Tesorflow and PyTorch. Introducing more efficient mathematical formulations and a novel parallelization scheme, GradVis allows to plot 2d and 3d projections of optimization surfaces and trajectories, as well as high resolution second order gradient information for large networks.