I am a Researcher (Artificial Intelligence) at Centre for Applied Autonomous Sensor Systems, Orebro University, Sweden. Prior to my position here, I was working as a Data Scientist at True Corporation, a Communications Conglomerate, working with Petabytes of data, building & deploying deep models at production. I truly believe that Opacity in AI systems is need of the hour, before we fully accept & leverage the potential of AI. With this in mind, I have always strived to democratize AI, and focus more towards building Explainable Models & specifically learning good representations. My interests include Applied Artificial Intelligence, Machine Learning, Deep Learning, Reinforcement Learning, Optimization, Big Data, Algorithmic trading, Natural Language Processing, Distributed Systems & Wireless Networks. From my experience working on the real-world business problems, I fully acknowledge that finding good representations is the key in designing the system that can solve interesting & challenging real-world problems, that go beyond human-level intelligence, and ultimately explain complicated data for us that we don’t understand. In order to achieve this, I envision learning algorithms that can learn feature representations from both unlabelled and labelled data, be guided with and/or without human interaction, and that are on different levels of abstractions in order to bridge the gap between low-level data and high-level abstract concepts.