My research interests are in the theoretical and practical aspects of machine learning, the principles and algorithms that affect learning and how they are applied to specialized domains in making predictions, especially where there are rapid shifts in observable features as well as how mathematical principles, statistics and probabilistic reasoning can be applied to aid better decision making. I am also interested in researching the frontiers involving the synthesis of deep learning and reinforcement learning (what is now known as deep reinforcement learning), and how they can improve the learning task of an agent interacting in a non-deterministic, non-stationary environment (where exact mathematical analysis are no longer feasible), especially when the states of the environment are only partially observable. I also have research experience in the areas of Computer Vision and Natural Language Processing.