Saumyadipta Pyne

Research Overview

My research areas include Computational Statistics, Data Fusion, and Generative AI. In recent years, we developed integrative and predictive models that can “look beyond the horizon” by synthesis of real observations with synthetic data to gain key insights into complex, dynamic phenomena that are difficult to observe in entirety. Examples of applications relevant to aging and longevity are reconstructing unobserved evolution in single cells under certain conditions, forecasting the future trajectories of aging phenotypes in individuals, representing degenerative patterns in tissues as continuously transforming landscapes, etc.