My research focuses on solving large-scale machine learning problems. This challenge arises frequently in modern machine learning concerning a lot of data and complex learning models. Some examples include understanding the convergence and generalization characteristics of artificial neural networks and developing efficient and robust optimization methods in stochastic and distributed settings. I pursue this goal by investigating theoretical and applied aspects of the principles and practices in optimization as much as by studying the science of deep learning. My vision is to engage in the broad field of artificial intelligence and push the frontiers of the state of the art in machine learning research.