Shrabanti Chowdhury, PhD

I received my Ph.D. in Statistics from University of California, Riverside. I joined the GGS Department at Sinai in 2018 as a Post-doctoral fellow. Since joining ISMMS, I have been actively involved in large scale cancer studies from NCI-CPTAC (Clinical Proteomic Tumor Analysis Consortium). Primarily my research is focused on developing novel computational/statistical methods for integrating different types of complex high-dimensional multimodal -omics data sets to understand the molecular mechanism and identify signaling pathways driving the disease that, in turn, can be translated into the clinic. My current research involves developing a comprehensive pipeline employing advanced artificial intelligence and deep learning techniques for predicting treatment response in ovarian cancer, to enhance prediction accuracy by flexibly modeling and capturing complex correlations in high-dimensional data with a limited sample size. I also work on causal network learning based on high dimensional -omics data to facilitate system learning, such as cell-cell communication. Recently in 2025 I received an Early Career Investigator Grant as a Principal Investigator for 3 years from Ovariance Cancer Research Alliance.

View full list of publications at Google scholar.