Dr. Agniv Sengupta is a Staff Scientist at the Center for Western Weather and Water Extremes (CW3E), Scripps Institution of Oceanography, where he serves as the Machine Learning team lead. His research interests include climate dynamics, global climate variability, hydroclimate prediction, and applications of machine learning in geophysical research. His current projects focus on improving the prediction skill of sub-seasonal to seasonal (S2S) forecasts in the western United States. This involves exploring innovative algorithms and approaches, advancing models for predictions across multiple timescales, and developing decision support tools and forecast products in coordination with stakeholders.

Prior to joining CW3E, Dr. Sengupta was a postdoctoral scholar (2020-21) with Dr. Duane E. Waliser at the NASA Jet Propulsion Laboratory (JPL). At NASA-JPL, his research focused primarily on leveraging sources of predictability at longer lead times for the development and dissemination of seasonal winter precipitation forecasts over the western United States using novel statistical and machine learning methods. In the interest of supporting the Fifth U.S. National Climate Assessment Report, he also investigated the representation of the global and regional water cycle in climate model simulations. Dr. Sengupta earned his Ph.D. (2020) and M.S. (2016) in Atmospheric and Oceanic Science from the University of Maryland College Park under the supervision of Dr. Sumant Nigam. His doctoral research focused on sea surface temperature-based statistical forecasting of the South-Southeast Asian summer monsoon rainfall. His M.S. thesis involved an attribution analysis of the evolution of the 2015-16 El Niño episode.