I am a Ph.D. student supervised by Dr. Amit P. Sheth, the director of Artificial Intelligence Institute, University of South Carolina, Columbia, SC.

My dissertation research focusses on developing a richer representation of causality using knowledge graph for better explainability with the applications in autonomous driving and healthcare. CausalKG, causal knowledge graph utilizes the existing domain knowledge from the knowledge graph to support the casual relationships from the observational data leading to better causal understanding and explainability.

In the past, I have worked in healthcare industry involving pediatric asthma patients. The traditional healthcare practices miss out on a holistic view of the patient and rely on the patient's self-reporting of the symptoms. I explored the use of digital phenotype score for patient’s health abstraction, a personalized Bayesian prediction model to prevent future adverse health outcomes, recommendation system for an early personalized treatment intervention strategy for better patient health management, and a causal knowledge graph to support explainability of the patient model.

I aim to conduct interdisciplinary research. My research interest includes causal analysis, applied machine learning to solve health care problems, predictive analytics, information extraction, augmented personalized health, internet of things (IoTs), semantic web and semantic cognitive perceptual computing.

• 12-20-2021 - The paper I co-authored with Dr. Amit Sheth, "CausalKG: Causal Knowledge Graph" has been accepted to IEEE Internet Computing Volume 26 (1), Jan-Feb 2022


• 12-4-2021 - Lead ML4H 2021 Research roundtable discussion