Research Interests I am intriqued by the challenging problems in artificial intelligence, data mining, natural language processing, and knowledge graphs. In specific, my research is about a class of Neuro-Symbolic AI in which explicit knowledge plays a central role. My dissertation thesis, titled Knowledge-infused Mining and Learning advances the state of the art in five research thrust areas: (1) Recommender Systems, (2) Learning to Rank, (3) Summarization, (4) Conversational AI, and (5) Computational Social Data Science. An important corollary of my research is that it addresses one of the most important hurdles in the wider acceptance of AI: 91% of the companies surveyed indicated the need to have explainable AI, which forms a pertinent component in KiML. By using KiML, I contribute towards this timely need for Interpretable and Explainable Machine Learning. I have demonstrated its benefits in various multidisciplinary research mental healthcare, crisis informatics, conversational information seeking, virtul health assistants, and digital security.