Tanmayee Narendra’s research focuses on the intersection of machine learning and biological systems, with a particular emphasis on causal reasoning and epigenomic modeling. Their early indexed work centers on explaining deep learning models using causal inference, a framework that has received 28 citations for its approach to model interpretability. This interest in causality extends to the application of counterfactual reasoning for process optimization using structural causal models, which explores how algorithmic interventions can improve complex workflows. More recently, Narendra’s lead-author research has transitioned into high-resolution genomics, specifically focusing on learning shared chromatin landscapes and joint de-noising of histone modification assays to advance personalized epigenomics. The researcher’s broader contributions include collaborative efforts to refine precision medicine through computational tools. Publicly indexed outputs suggest a significant role in developing