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I am a Ph.D. candidate advised by  <a href="http://amit.aiisc.ai/">Prof. Amit P. Sheth</a> in the Artificial Intelligence Institute</a> at <a href="https://sc.edu/">University of South Carolina</a>. Prior to that, I did my undergraduate from
<a href="http://aiactr.ac.in/">Netaji Subhas University of Technology</a> (Formerly Ambedkar Institute of Technology) and masters from <a href="http://www.dtu.ac.in/">Delhi Technological University</a>(Formerly Delhi College of Engineering).
I hold industry research experience as an AI for Social Good Fellow with Dataminr Inc and Research Science Intern with Samsung Research America. I also hold academic research experience
through support from <a href="https://federalreporter.nih.gov/Projects/Details/?projectId=891050">NIMH</a> and <a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=2133842&HistoricalAwards=false">NSF</a> grants. During my early Ph.D days, I was fortunate to collaborate with various non-profits and private healthcare as a Data Science for Social Good Fellow
with University of Chicago. 

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<h2 style="font-family:verdana;"><b>Research Interests</b></h2>
I am intriqued by the challenging problems in artificial intelligence, data mining, natural language processing, and knowledge graphs. In specific, my research, funded from <a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=1520870"><b style="color:green">NSF</b></a>, is about a class of Neuro-Symbolic AI in which explicit knowledge plays a central role. My dissertation thesis, titled <a href="http://ceur-ws.org/Vol-2657/xproceedings.pdf"><b style="color:Maroon">Knowledge-infused Learning</b></a>
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.