I am a Ph.D. candidate at the AI Institute, University of South Carolina, where I am fortunate to be advised by Dr. Amit Sheth. My research focuses on advancing Neuro-symbolic AI by integrating causal reasoning to improve explainability, robustness, and human-like understanding in AI systems.
My dissertation, Causal Neuro-symbolic AI (CausalNeSy), introduces a novel framework for embedding causal knowledge into AI systems. This approach enables a deeper understanding of phenomena and situations by integrating domain expertise through human-in-the-loop models or leveraging information from knowledge graphs. My work aims to develop user-explainable causal models and systems, addressing complex real-world problems across diverse domains.
I have applied my research to interdisciplinary scenarios, including root cause analysis in smart manufacturing, enhancing interoperability in the industrial metaverse, and predicting health outcomes for pediatric asthma patients. Beyond these applications, CausalNeSy has broader potential for studying policy interventions, analyzing personalized treatment effects, reasoning about interventions, identifying causal pathways in biological mechanisms, studying disease progression and risk prediction, and providing transparent explanations for traditionally black-box models.
My research aligns with the vision of leveraging AI to address social disparities and advance personalized healthcare, emphasizes improving user engagement and healthcare outcomes. During my Ph.D. journey, I have contributed to several multi-disciplinary projects, led efforts in NIH and NSF grant proposal writing, and collaborated with leading AI institutes.
Beyond academia, I bring significant industry experience through internships at Bosch Center for AI and Siemens, where I developed innovative solutions such as the Causal Knowledge Graph (CausalKG) for autonomous driving and the MetaverseKG for industrial applications.
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Mailing Address: AI Institute, UofSC (AIISC), 1112 Greene St. Columbia, SC 29208
Office Location: Room 513
Email:
Phone: (937) 972-8652