DEEPALI KUNDNANI

Deepali Kundnani is a research affiliate and computational biologist with extensive experience in cancer bioinformatics, multi‑omics analysis, and translational medical research. Her work lies at the intersection of molecular biology, clinical data science, and artificial intelligence, with a strong emphasis on developing reproducible, software‑driven workflows for disease understanding and early cancer detection. She earned her PhD in Bioinformatics from the Georgia Institute of Technology, where her doctoral research focused on genome‑wide discovery and characterization of ribonucleotide incorporation as a novel epigenomic phenomenon in human cells, including cancer contexts. Her work combined large‑scale genomic analysis, statistical modeling, and custom bioinformatics tool development to uncover functional DNA features linked to disease. Prior to her PhD, she worked at the MD Anderson Cancer Center, contributing to the development and validation of multiplex immunoassays for protein biomarkers in patient blood samples, supporting lung cancer risk assessment and early detection efforts. Through this work, she gained hands‑on experience in clinical biomarker development, translational research, and collaborations with clinicians and diagnostic laboratories. At the Tumor AI Lab, Deepali is eager to apply machine learning and integrative data analysis to cancer genomics and clinical datasets, including EHR‑linked studies. Under the guidance of Dr. Avinash Sahu, she aims to lead independent research initiatives focused on early diagnosis, disease stratification, and precision oncology by bridging molecular data with real‑world clinical outcomes.